Robotics 40
☆ Scaling Spatial Intelligence with Multimodal Foundation Models
Zhongang Cai, Ruisi Wang, Chenyang Gu, Fanyi Pu, Junxiang Xu, Yubo Wang, Wanqi Yin, Zhitao Yang, Chen Wei, Qingping Sun, Tongxi Zhou, Jiaqi Li, Hui En Pang, Oscar Qian, Yukun Wei, Zhiqian Lin, Xuanke Shi, Kewang Deng, Xiaoyang Han, Zukai Chen, Xiangyu Fan, Hanming Deng, Lewei Lu, Liang Pan, Bo Li, Ziwei Liu, Quan Wang, Dahua Lin, Lei Yang
Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SenseNova-SI family, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel). We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SenseNova-SI-8M: eight million diverse data samples under a rigorous taxonomy of spatial capabilities. SenseNova-SI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks: 68.7% on VSI-Bench, 43.3% on MMSI, 85.6% on MindCube, 54.6% on ViewSpatial, and 50.1% on SITE, while maintaining strong general multimodal understanding (e.g., 84.9% on MMBench-En). More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training, analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. SenseNova-SI is an ongoing project, and this report will be updated continuously. All newly trained multimodal foundation models are publicly released to facilitate further research in this direction.
comment: Model: https://huggingface.co/collections/sensenova/sensenova-si; Code: https://github.com/OpenSenseNova/SenseNova-SI
☆ From Power to Precision: Learning Fine-grained Dexterity for Multi-fingered Robotic Hands
Human grasps can be roughly categorized into two types: power grasps and precision grasps. Precision grasping enables tool use and is believed to have influenced human evolution. Today's multi-fingered robotic hands are effective in power grasps, but for tasks requiring precision, parallel grippers are still more widely adopted. This contrast highlights a key limitation in current robotic hand design: the difficulty of achieving both stable power grasps and precise, fine-grained manipulation within a single, versatile system. In this work, we bridge this gap by jointly optimizing the control and hardware design of a multi-fingered dexterous hand, enabling both power and precision manipulation. Rather than redesigning the entire hand, we introduce a lightweight fingertip geometry modification, represent it as a contact plane, and jointly optimize its parameters along with the corresponding control. Our control strategy dynamically switches between power and precision manipulation and simplifies precision control into parallel thumb-index motions, which proves robust for sim-to-real transfer. On the design side, we leverage large-scale simulation to optimize the fingertip geometry using a differentiable neural-physics surrogate model. We validate our approach through extensive experiments in both sim-to-real and real-to-real settings. Our method achieves an 82.5% zero-shot success rate on unseen objects in sim-to-real precision grasping, and a 93.3% success rate in challenging real-world tasks involving bread pinching. These results demonstrate that our co-design framework can significantly enhance the fine-grained manipulation ability of multi-fingered hands without reducing their ability for power grasps. Our project page is at https://jianglongye.com/power-to-precision
comment: Project page: https://jianglongye.com/power-to-precision
☆ OpenRoboCare: A Multimodal Multi-Task Expert Demonstration Dataset for Robot Caregiving IROS 2025
Xiaoyu Liang, Ziang Liu, Kelvin Lin, Edward Gu, Ruolin Ye, Tam Nguyen, Cynthia Hsu, Zhanxin Wu, Xiaoman Yang, Christy Sum Yu Cheung, Harold Soh, Katherine Dimitropoulou, Tapomayukh Bhattacharjee
We present OpenRoboCare, a multimodal dataset for robot caregiving, capturing expert occupational therapist demonstrations of Activities of Daily Living (ADLs). Caregiving tasks involve complex physical human-robot interactions, requiring precise perception under occlusions, safe physical contact, and long-horizon planning. While recent advances in robot learning from demonstrations have shown promise, there is a lack of a large-scale, diverse, and expert-driven dataset that captures real-world caregiving routines. To address this gap, we collect data from 21 occupational therapists performing 15 ADL tasks on two manikins. The dataset spans five modalities: RGB-D video, pose tracking, eye-gaze tracking, task and action annotations, and tactile sensing, providing rich multimodal insights into caregiver movement, attention, force application, and task execution strategies. We further analyze expert caregiving principles and strategies, offering insights to improve robot efficiency and task feasibility. Additionally, our evaluations demonstrate that OpenRoboCare presents challenges for state-of-the-art robot perception and human activity recognition methods, both critical for developing safe and adaptive assistive robots, highlighting the value of our contribution. See our website for additional visualizations: https://emprise.cs.cornell.edu/robo-care/.
comment: IROS 2025
☆ PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image
3D modeling is shifting from static visual representations toward physical, articulated assets that can be directly used in simulation and interaction. However, most existing 3D generation methods overlook key physical and articulation properties, thereby limiting their utility in embodied AI. To bridge this gap, we introduce PhysX-Anything, the first simulation-ready physical 3D generative framework that, given a single in-the-wild image, produces high-quality sim-ready 3D assets with explicit geometry, articulation, and physical attributes. Specifically, we propose the first VLM-based physical 3D generative model, along with a new 3D representation that efficiently tokenizes geometry. It reduces the number of tokens by 193x, enabling explicit geometry learning within standard VLM token budgets without introducing any special tokens during fine-tuning and significantly improving generative quality. In addition, to overcome the limited diversity of existing physical 3D datasets, we construct a new dataset, PhysX-Mobility, which expands the object categories in prior physical 3D datasets by over 2x and includes more than 2K common real-world objects with rich physical annotations. Extensive experiments on PhysX-Mobility and in-the-wild images demonstrate that PhysX-Anything delivers strong generative performance and robust generalization. Furthermore, simulation-based experiments in a MuJoCo-style environment validate that our sim-ready assets can be directly used for contact-rich robotic policy learning. We believe PhysX-Anything can substantially empower a broad range of downstream applications, especially in embodied AI and physics-based simulation.
comment: Project page: https://physx-anything.github.io/
☆ Towards Affect-Adaptive Human-Robot Interaction: A Protocol for Multimodal Dataset Collection on Social Anxiety
Social anxiety is a prevalent condition that affects interpersonal interactions and social functioning. Recent advances in artificial intelligence and social robotics offer new opportunities to examine social anxiety in the human-robot interaction context. Accurate detection of affective states and behaviours associated with social anxiety requires multimodal datasets, where each signal modality provides complementary insights into its manifestations. However, such datasets remain scarce, limiting progress in both research and applications. To address this, this paper presents a protocol for multimodal dataset collection designed to reflect social anxiety in a human-robot interaction context. The dataset will consist of synchronised audio, video, and physiological recordings acquired from at least 70 participants, grouped according to their level of social anxiety, as they engage in approximately 10-minute interactive Wizard-of-Oz role-play scenarios with the Furhat social robot under controlled experimental conditions. In addition to multimodal data, the dataset will be enriched with contextual data providing deeper insight into individual variability in social anxiety responses. This work can contribute to research on affect-adaptive human-robot interaction by providing support for robust multimodal detection of social anxiety.
comment: Accepted at the Workshop on Benefits of pErsonalization and behAvioral adaptation in assistive Robots (BEAR 2025), held at the IEEE RO-MAN Conference 2025
☆ Contact-Safe Reinforcement Learning with ProMP Reparameterization and Energy Awareness
Reinforcement learning (RL) approaches based on Markov Decision Processes (MDPs) are predominantly applied in the robot joint space, often relying on limited task-specific information and partial awareness of the 3D environment. In contrast, episodic RL has demonstrated advantages over traditional MDP-based methods in terms of trajectory consistency, task awareness, and overall performance in complex robotic tasks. Moreover, traditional step-wise and episodic RL methods often neglect the contact-rich information inherent in task-space manipulation, especially considering the contact-safety and robustness. In this work, contact-rich manipulation tasks are tackled using a task-space, energy-safe framework, where reliable and safe task-space trajectories are generated through the combination of Proximal Policy Optimization (PPO) and movement primitives. Furthermore, an energy-aware Cartesian Impedance Controller objective is incorporated within the proposed framework to ensure safe interactions between the robot and the environment. Our experimental results demonstrate that the proposed framework outperforms existing methods in handling tasks on various types of surfaces in 3D environments, achieving high success rates as well as smooth trajectories and energy-safe interactions.
☆ ZeroDexGrasp: Zero-Shot Task-Oriented Dexterous Grasp Synthesis with Prompt-Based Multi-Stage Semantic Reasoning
Task-oriented dexterous grasping holds broad application prospects in robotic manipulation and human-object interaction. However, most existing methods still struggle to generalize across diverse objects and task instructions, as they heavily rely on costly labeled data to ensure task-specific semantic alignment. In this study, we propose \textbf{ZeroDexGrasp}, a zero-shot task-oriented dexterous grasp synthesis framework integrating Multimodal Large Language Models with grasp refinement to generate human-like grasp poses that are well aligned with specific task objectives and object affordances. Specifically, ZeroDexGrasp employs prompt-based multi-stage semantic reasoning to infer initial grasp configurations and object contact information from task and object semantics, then exploits contact-guided grasp optimization to refine these poses for physical feasibility and task alignment. Experimental results demonstrate that ZeroDexGrasp enables high-quality zero-shot dexterous grasping on diverse unseen object categories and complex task requirements, advancing toward more generalizable and intelligent robotic grasping.
☆ EL3DD: Extended Latent 3D Diffusion for Language Conditioned Multitask Manipulation
Acting in human environments is a crucial capability for general-purpose robots, necessitating a robust understanding of natural language and its application to physical tasks. This paper seeks to harness the capabilities of diffusion models within a visuomotor policy framework that merges visual and textual inputs to generate precise robotic trajectories. By employing reference demonstrations during training, the model learns to execute manipulation tasks specified through textual commands within the robot's immediate environment. The proposed research aims to extend an existing model by leveraging improved embeddings, and adapting techniques from diffusion models for image generation. We evaluate our methods on the CALVIN dataset, proving enhanced performance on various manipulation tasks and an increased long-horizon success rate when multiple tasks are executed in sequence. Our approach reinforces the usefulness of diffusion models and contributes towards general multitask manipulation.
comment: 10 pages; 2 figures; 1 table. Prprint submitted to the European Robotics Forum 2026
☆ Proceedings Seventh International Workshop on Formal Methods for Autonomous Systems
This EPTCS volume contains the papers from the Seventh International Workshop on Formal Methods for Autonomous Systems (FMAS 2025), which was held between the 17th and 19th of November 2025. The goal of the FMAS workshop series is to bring together leading researchers who are using formal methods to tackle the unique challenges that autonomous systems present, so that they can publish and discuss their work with a growing community of researchers. FMAS 2025 was co-located with the 20th International Conference on integrated Formal Methods (iFM'25), hosted by Inria Paris, France at the Inria Paris Center.
In total, FMAS 2025 received 16 submissions from researchers at institutions in: Canada, China, France, Germany, Ireland, Italy, Japan, the Netherlands, Portugal, Sweden, the United States of America, and the United Kingdom. Though we received fewer submissions than last year, we are encouraged to see the submissions being sent from a wide range of countries. Submissions come from both past and new FMAS authors, which shows us that the existing community appreciates the network that FMAS has built over the past 7 years, while new authors also show the FMAS community's great potential of growth.
☆ GaRLILEO: Gravity-aligned Radar-Leg-Inertial Enhanced Odometry
Deployment of legged robots for navigating challenging terrains (e.g., stairs, slopes, and unstructured environments) has gained increasing preference over wheel-based platforms. In such scenarios, accurate odometry estimation is a preliminary requirement for stable locomotion, localization, and mapping. Traditional proprioceptive approaches, which rely on leg kinematics sensor modalities and inertial sensing, suffer from irrepressible vertical drift caused by frequent contact impacts, foot slippage, and vibrations, particularly affected by inaccurate roll and pitch estimation. Existing methods incorporate exteroceptive sensors such as LiDAR or cameras. Further enhancement has been introduced by leveraging gravity vector estimation to add additional observations on roll and pitch, thereby increasing the accuracy of vertical pose estimation. However, these approaches tend to degrade in feature-sparse or repetitive scenes and are prone to errors from double-integrated IMU acceleration. To address these challenges, we propose GaRLILEO, a novel gravity-aligned continuous-time radar-leg-inertial odometry framework. GaRLILEO decouples velocity from the IMU by building a continuous-time ego-velocity spline from SoC radar Doppler and leg kinematics information, enabling seamless sensor fusion which mitigates odometry distortion. In addition, GaRLILEO can reliably capture accurate gravity vectors leveraging a novel soft S2-constrained gravity factor, improving vertical pose accuracy without relying on LiDAR or cameras. Evaluated on a self-collected real-world dataset with diverse indoor-outdoor trajectories, GaRLILEO demonstrates state-of-the-art accuracy, particularly in vertical odometry estimation on stairs and slopes. We open-source both our dataset and algorithm to foster further research in legged robot odometry and SLAM. https://garlileo.github.io/GaRLILEO
☆ PIGEON: VLM-Driven Object Navigation via Points of Interest Selection
Cheng Peng, Zhenzhe Zhang, Cheng Chi, Xiaobao Wei, Yanhao Zhang, Heng Wang, Pengwei Wang, Zhongyuan Wang, Jing Liu, Shanghang Zhang
Navigating to a specified object in an unknown environment is a fundamental yet challenging capability of embodied intelligence. However, current methods struggle to balance decision frequency with intelligence, resulting in decisions lacking foresight or discontinuous actions. In this work, we propose PIGEON: Point of Interest Guided Exploration for Object Navigation with VLM, maintaining a lightweight and semantically aligned snapshot memory during exploration as semantic input for the exploration strategy. We use a large Visual-Language Model (VLM), named PIGEON-VL, to select Points of Interest (PoI) formed during exploration and then employ a lower-level planner for action output, increasing the decision frequency. Additionally, this PoI-based decision-making enables the generation of Reinforcement Learning with Verifiable Reward (RLVR) data suitable for simulators. Experiments on classic object navigation benchmarks demonstrate that our zero-shot transfer method achieves state-of-the-art performance, while RLVR further enhances the model's semantic guidance capabilities, enabling deep reasoning during real-time navigation.
☆ Collision-Free Navigation of Mobile Robots via Quadtree-Based Model Predictive Control
This paper presents an integrated navigation framework for Autonomous Mobile Robots (AMRs) that unifies environment representation, trajectory generation, and Model Predictive Control (MPC). The proposed approach incorporates a quadtree-based method to generate structured, axis-aligned collision-free regions from occupancy maps. These regions serve as both a basis for developing safe corridors and as linear constraints within the MPC formulation, enabling efficient and reliable navigation without requiring direct obstacle encoding. The complete pipeline combines safe-area extraction, connectivity graph construction, trajectory generation, and B-spline smoothing into one coherent system. Experimental results demonstrate consistent success and superior performance compared to baseline approaches across complex environments.
comment: This paper has been accepted by IEEE SII 2026
☆ Monolithic Units: Actuation, Sensing, and Simulation for Integrated Soft Robot Design
This work introduces the Monolithic Unit (MU), an actuator-lattice-sensor building block for soft robotics. The MU integrates pneumatic actuation, a compliant lattice envelope, and candidate sites for optical waveguide sensing into a single printed body. In order to study reproducibility and scalability, a parametric design framework establishes deterministic rules linking actuator chamber dimensions to lattice unit cell size. Experimental homogenization of lattice specimens provides effective material properties for finite element simulation. Within this simulation environment, sensor placement is treated as a discrete optimization problem, where a finite set of candidate waveguide paths derived from lattice nodes is evaluated by introducing local stiffening, and the configuration minimizing deviation from baseline mechanical response is selected. Optimized models are fabricated and experimentally characterized, validating the preservation of mechanical performance while enabling embedded sensing. The workflow is further extended to scaled units and a two-finger gripper, demonstrating generality of the MU concept. This approach advances monolithic soft robotic design by combining reproducible co-design rules with simulation-informed sensor integration.
comment: 8 pages, 6 figures, 1 algorithm, 1 table
☆ Count Every Rotation and Every Rotation Counts: Exploring Drone Dynamics via Propeller Sensing
Xuecheng Chen, Jingao Xu, Wenhua Ding, Haoyang Wang, Xinyu Luo, Ruiyang Duan, Jialong Chen, Xueqian Wang, Yunhao Liu, Xinlei Chen
As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, \sysname. \sysname features two components: \textit{Count Every Rotation} achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. \textit{Every Rotation Counts} leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that \sysname achieves a sensing latency of 3$ms$ and a rotational speed estimation error of merely 0.23\%. Additionally, \sysname infers drone flight commands with 96.5\% precision and improves drone tracking accuracy by over 22\% when combined with other sensing modalities. \textit{ Demo: {\color{blue}https://eventpro25.github.io/EventPro/.} }
☆ ResAlignNet: A Data-Driven Approach for INS/DVL Alignment
Autonomous underwater vehicles rely on precise navigation systems that combine the inertial navigation system and the Doppler velocity log for successful missions in challenging environments where satellite navigation is unavailable. The effectiveness of this integration critically depends on accurate alignment between the sensor reference frames. Standard model-based alignment methods between these sensor systems suffer from lengthy convergence times, dependence on prescribed motion patterns, and reliance on external aiding sensors, significantly limiting operational flexibility. To address these limitations, this paper presents ResAlignNet, a data-driven approach using the 1D ResNet-18 architecture that transforms the alignment problem into deep neural network optimization, operating as an in-situ solution that requires only sensors on board without external positioning aids or complex vehicle maneuvers, while achieving rapid convergence in seconds. Additionally, the approach demonstrates the learning capabilities of Sim2Real transfer, enabling training in synthetic data while deploying in operational sensor measurements. Experimental validation using the Snapir autonomous underwater vehicle demonstrates that ResAlignNet achieves alignment accuracy within 0.8° using only 25 seconds of data collection, representing a 65\% reduction in convergence time compared to standard velocity-based methods. The trajectory-independent solution eliminates motion pattern requirements and enables immediate vehicle deployment without lengthy pre-mission procedures, advancing underwater navigation capabilities through robust sensor-agnostic alignment that scales across different operational scenarios and sensor specifications.
☆ Orientation-Free Neural Network-Based Bias Estimation for Low-Cost Stationary Accelerometers
Low-cost micro-electromechanical accelerometers are widely used in navigation, robotics, and consumer devices for motion sensing and position estimation. However, their performance is often degraded by bias errors. To eliminate deterministic bias terms a calibration procedure is applied under stationary conditions. It requires accelerom- eter leveling or complex orientation-dependent calibration procedures. To overcome those requirements, in this paper we present a model-free learning-based calibration method that estimates accelerometer bias under stationary conditions, without requiring knowledge of the sensor orientation and without the need to rotate the sensors. The proposed approach provides a fast, practical, and scalable solution suitable for rapid field deployment. Experimental validation on a 13.39-hour dataset collected from six accelerometers shows that the proposed method consistently achieves error levels more than 52% lower than traditional techniques. On a broader scale, this work contributes to the advancement of accurate calibration methods in orientation-free scenarios. As a consequence, it improves the reliability of low-cost inertial sensors in diverse scientific and industrial applications and eliminates the need for leveled calibration.
comment: 22 pages, 10 figures
☆ Unidirectional-Road-Network-Based Global Path Planning for Cleaning Robots in Semi-Structured Environments ICRA
Practical global path planning is critical for commercializing cleaning robots working in semi-structured environments. In the literature, global path planning methods for free space usually focus on path length and neglect the traffic rule constraints of the environments, which leads to high-frequency re-planning and increases collision risks. In contrast, those for structured environments are developed mainly by strictly complying with the road network representing the traffic rule constraints, which may result in an overlong path that hinders the overall navigation efficiency. This article proposes a general and systematic approach to improve global path planning performance in semi-structured environments. A unidirectional road network is built to represent the traffic constraints in semi-structured environments and a hybrid strategy is proposed to achieve a guaranteed planning result.Cutting across the road at the starting and the goal points are allowed to achieve a shorter path. Especially, a two-layer potential map is proposed to achieve a guaranteed performance when the starting and the goal points are in complex intersections. Comparative experiments are carried out to validate the effectiveness of the proposed method. Quantitative experimental results show that, compared with the state-of-art, the proposed method guarantees a much better balance between path length and the consistency with the road network.
comment: 2023 IEEE International Conference on Robotics and Automation (ICRA)
☆ DiffPixelFormer: Differential Pixel-Aware Transformer for RGB-D Indoor Scene Segmentation
Indoor semantic segmentation is fundamental to computer vision and robotics, supporting applications such as autonomous navigation, augmented reality, and smart environments. Although RGB-D fusion leverages complementary appearance and geometric cues, existing methods often depend on computationally intensive cross-attention mechanisms and insufficiently model intra- and inter-modal feature relationships, resulting in imprecise feature alignment and limited discriminative representation. To address these challenges, we propose DiffPixelFormer, a differential pixel-aware Transformer for RGB-D indoor scene segmentation that simultaneously enhances intra-modal representations and models inter-modal interactions. At its core, the Intra-Inter Modal Interaction Block (IIMIB) captures intra-modal long-range dependencies via self-attention and models inter-modal interactions with the Differential-Shared Inter-Modal (DSIM) module to disentangle modality-specific and shared cues, enabling fine-grained, pixel-level cross-modal alignment. Furthermore, a dynamic fusion strategy balances modality contributions and fully exploits RGB-D information according to scene characteristics. Extensive experiments on the SUN RGB-D and NYUDv2 benchmarks demonstrate that DiffPixelFormer-L achieves mIoU scores of 54.28% and 59.95%, outperforming DFormer-L by 1.78% and 2.75%, respectively. Code is available at https://github.com/gongyan1/DiffPixelFormer.
comment: 11 pages, 5 figures, 5 tables
☆ APP: A* Post-Processing Algorithm for Robots with Bidirectional Shortcut and Path Perturbation
Paths generated by A* and other graph-search-based planners are widely used in the robotic field. Due to the restricted node-expansion directions, the resulting paths are usually not the shortest. Besides, unnecessary heading changes, or zig-zag patterns, exist even when no obstacle is nearby, which is inconsistent with the human intuition that the path segments should be straight in wide-open space due to the absence of obstacles. This article puts forward a general and systematic post-processing algorithm for A* and other graph-search-based planners. The A* post-processing algorithm, called APP, is developed based on the costmap, which is widely used in commercial service robots. First, a bidirectional vertices reduction algorithm is proposed to tackle the asymm- etry of the path and the environments. During the forward and backward vertices reduction, a thorough shortcut strategy is put forward to improve the path-shortening performance and avoid unnecessary heading changes. Second, an iterative path perturbation algorithm is adopted to locally reduce the number of unnecessary heading changes and improve the path smooth- ness. Comparative experiments are then carried out to validate the superiority of the proposed method. Quantitative performance indexes show that APP outperforms the existing methods in planning time, path length as well as the number of unnecessary heading changes. Finally, field navigation experiments are carried out to verify the practicability of APP.
☆ CUTE-Planner: Confidence-aware Uneven Terrain Exploration Planner
Planetary exploration robots must navigate uneven terrain while building reliable maps for space missions. However, most existing methods incorporate traversability constraints but may not handle high uncertainty in elevation estimates near complex features like craters, do not consider exploration strategies for uncertainty reduction, and typically fail to address how elevation uncertainty affects navigation safety and map quality. To address the problems, we propose a framework integrating safe path generation, adaptive confidence updates, and confidence-aware exploration strategies. Using Kalman-based elevation estimation, our approach generates terrain traversability and confidence scores, then incorporates them into Graph-Based exploration Planner (GBP) to prioritize exploration of traversable low-confidence regions. We evaluate our framework through simulated lunar experiments using a novel low-confidence region ratio metric, achieving 69% uncertainty reduction compared to baseline GBP. In terms of mission success rate, our method achieves 100% while baseline GBP achieves 0%, demonstrating improvements in exploration safety and map reliability.
comment: Accepted in International Conference on Space Robotics 2025
☆ SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models
The Instance Image Goal Navigation (IIN) problem requires mobile robots deployed in unknown environments to search for specific objects or people of interest using only a single reference goal image of the target. This problem can be especially challenging when: 1) the reference image is captured from an arbitrary viewpoint, and 2) the robot must operate with sparse-view scene reconstructions. In this paper, we address the IIN problem, by introducing SplatSearch, a novel architecture that leverages sparse-view 3D Gaussian Splatting (3DGS) reconstructions. SplatSearch renders multiple viewpoints around candidate objects using a sparse online 3DGS map, and uses a multi-view diffusion model to complete missing regions of the rendered images, enabling robust feature matching against the goal image. A novel frontier exploration policy is introduced which uses visual context from the synthesized viewpoints with semantic context from the goal image to evaluate frontier locations, allowing the robot to prioritize frontiers that are semantically and visually relevant to the goal image. Extensive experiments in photorealistic home and real-world environments validate the higher performance of SplatSearch against current state-of-the-art methods in terms of Success Rate and Success Path Length. An ablation study confirms the design choices of SplatSearch.
comment: Project Page: https://splat-search.github.io/
☆ GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving
In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to represent these obstacles, which often fail to capture the complexity of irregularly shaped, real-world objects. To overcome these limitations, we present GUIDE, a novel framework that utilizes 3D Gaussians for instance detection and occupancy prediction. Unlike conventional occupancy prediction methods, GUIDE also offers robust tracking capabilities. Our framework employs a sparse representation strategy, using Gaussian-to-Voxel Splatting to provide fine-grained, instance-level occupancy data without the computational demands associated with dense voxel grids. Experimental validation on the nuScenes dataset demonstrates GUIDE's performance, with an instance occupancy mAP of 21.61, marking a 50\% improvement over existing methods, alongside competitive tracking capabilities. GUIDE establishes a new benchmark in autonomous perception systems, effectively combining precision with computational efficiency to better address the complexities of real-world driving environments.
☆ DiffuDepGrasp: Diffusion-based Depth Noise Modeling Empowers Sim2Real Robotic Grasping
Transferring the depth-based end-to-end policy trained in simulation to physical robots can yield an efficient and robust grasping policy, yet sensor artifacts in real depth maps like voids and noise establish a significant sim2real gap that critically impedes policy transfer. Training-time strategies like procedural noise injection or learned mappings suffer from data inefficiency due to unrealistic noise simulation, which is often ineffective for grasping tasks that require fine manipulation or dependency on paired datasets heavily. Furthermore, leveraging foundation models to reduce the sim2real gap via intermediate representations fails to mitigate the domain shift fully and adds computational overhead during deployment. This work confronts dual challenges of data inefficiency and deployment complexity. We propose DiffuDepGrasp, a deploy-efficient sim2real framework enabling zero-shot transfer through simulation-exclusive policy training. Its core innovation, the Diffusion Depth Generator, synthesizes geometrically pristine simulation depth with learned sensor-realistic noise via two synergistic modules. The first Diffusion Depth Module leverages temporal geometric priors to enable sample-efficient training of a conditional diffusion model that captures complex sensor noise distributions, while the second Noise Grafting Module preserves metric accuracy during perceptual artifact injection. With only raw depth inputs during deployment, DiffuDepGrasp eliminates computational overhead and achieves a 95.7% average success rate on 12-object grasping with zero-shot transfer and strong generalization to unseen objects.Project website: https://diffudepgrasp.github.io/.
☆ TOPP-DWR: Time-Optimal Path Parameterization of Differential-Driven Wheeled Robots Considering Piecewise-Constant Angular Velocity Constraints
Differential-driven wheeled robots (DWR) represent the quintessential type of mobile robots and find extensive appli- cations across the robotic field. Most high-performance control approaches for DWR explicitly utilize the linear and angular velocities of the trajectory as control references. However, existing research on time-optimal path parameterization (TOPP) for mobile robots usually neglects the angular velocity and joint vel- ocity constraints, which can result in degraded control perfor- mance in practical applications. In this article, a systematic and practical TOPP algorithm named TOPP-DWR is proposed for DWR and other mobile robots. First, the non-uniform B-spline is adopted to represent the initial trajectory in the task space. Second, the piecewise-constant angular velocity, as well as joint velocity, linear velocity, and linear acceleration constraints, are incorporated into the TOPP problem. During the construction of the optimization problem, the aforementioned constraints are uniformly represented as linear velocity constraints. To boost the numerical computational efficiency, we introduce a slack variable to reformulate the problem into second-order-cone programming (SOCP). Subsequently, comparative experiments are conducted to validate the superiority of the proposed method. Quantitative performance indexes show that TOPP-DWR achieves TOPP while adhering to all constraints. Finally, field autonomous navigation experiments are carried out to validate the practicability of TOPP-DWR in real-world applications.
☆ Air-Chamber Based Soft Six-Axis Force/Torque Sensor for Human-Robot Interaction
Soft multi-axis force/torque sensors provide safe and precise force interaction. Capturing the complete degree-of-freedom of force is imperative for accurate force measurement with six-axis force/torque sensors. However, cross-axis coupling can lead to calibration issues and decreased accuracy. In this instance, developing a soft and accurate six-axis sensor is a challenging task. In this paper, a soft air-chamber type six-axis force/torque sensor with 16-channel barometers is introduced, which housed in hyper-elastic air chambers made of silicone rubber. Additionally, an effective decoupling method is proposed, based on a rigid-soft hierarchical structure, which reduces the six-axis decoupling problem to two three-axis decoupling problems. Finite element model simulation and experiments demonstrate the compatibility of the proposed approach with reality. The prototype's sensing performance is quantitatively measured in terms of static load response, dynamic load response and dynamic response characteristic. It possesses a measuring range of 50 N force and 1 Nm torque, and the average deviation, repeatability, non-linearity and hysteresis are 4.9$\%$, 2.7$\%$, 5.8$\%$ and 6.7$\%$, respectively. The results indicate that the prototype exhibits satisfactory sensing performance while maintaining its softness due to the presence of soft air chambers.
☆ Towards High-Consistency Embodied World Model with Multi-View Trajectory Videos
Embodied world models aim to predict and interact with the physical world through visual observations and actions. However, existing models struggle to accurately translate low-level actions (e.g., joint positions) into precise robotic movements in predicted frames, leading to inconsistencies with real-world physical interactions. To address these limitations, we propose MTV-World, an embodied world model that introduces Multi-view Trajectory-Video control for precise visuomotor prediction. Specifically, instead of directly using low-level actions for control, we employ trajectory videos obtained through camera intrinsic and extrinsic parameters and Cartesian-space transformation as control signals. However, projecting 3D raw actions onto 2D images inevitably causes a loss of spatial information, making a single view insufficient for accurate interaction modeling. To overcome this, we introduce a multi-view framework that compensates for spatial information loss and ensures high-consistency with physical world. MTV-World forecasts future frames based on multi-view trajectory videos as input and conditioning on an initial frame per view. Furthermore, to systematically evaluate both robotic motion precision and object interaction accuracy, we develop an auto-evaluation pipeline leveraging multimodal large models and referring video object segmentation models. To measure spatial consistency, we formulate it as an object location matching problem and adopt the Jaccard Index as the evaluation metric. Extensive experiments demonstrate that MTV-World achieves precise control execution and accurate physical interaction modeling in complex dual-arm scenarios.
comment: 11 pages, 5 figures
☆ Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views IROS'25
Junyi Ma, Wentao Bao, Jingyi Xu, Guanzhong Sun, Yu Zheng, Erhang Zhang, Xieyuanli Chen, Hesheng Wang
Analyzing hand-object interaction in egocentric vision facilitates VR/AR applications and human-robot policy transfer. Existing research has mostly focused on modeling the behavior paradigm of interactive actions (i.e., "how to interact"). However, the more challenging and fine-grained problem of capturing the critical moments of contact and separation between the hand and the target object (i.e., "when to interact") is still underexplored, which is crucial for immersive interactive experiences in mixed reality and robotic motion planning. Therefore, we formulate this problem as temporal interaction localization (TIL). Some recent works extract semantic masks as TIL references, but suffer from inaccurate object grounding and cluttered scenarios. Although current temporal action localization (TAL) methods perform well in detecting verb-noun action segments, they rely on category annotations during training and exhibit limited precision in localizing hand-object contact/separation moments. To address these issues, we propose a novel zero-shot approach dubbed EgoLoc to localize hand-object contact and separation timestamps in egocentric videos. EgoLoc introduces hand-dynamics-guided sampling to generate high-quality visual prompts. It exploits the vision-language model to identify contact/separation attributes, localize specific timestamps, and provide closed-loop feedback for further refinement. EgoLoc eliminates the need for object masks and verb-noun taxonomies, leading to generalizable zero-shot implementation. Comprehensive experiments on the public dataset and our novel benchmarks demonstrate that EgoLoc achieves plausible TIL for egocentric videos. It is also validated to effectively facilitate multiple downstream applications in egocentric vision and robotic manipulation tasks. Code and relevant data will be released at https://github.com/IRMVLab/EgoLoc.
comment: Extended journal version of MMTwin (IROS'25)
☆ Structured Imitation Learning of Interactive Policies through Inverse Games
Generative model-based imitation learning methods have recently achieved strong results in learning high-complexity motor skills from human demonstrations. However, imitation learning of interactive policies that coordinate with humans in shared spaces without explicit communication remains challenging, due to the significantly higher behavioral complexity in multi-agent interactions compared to non-interactive tasks. In this work, we introduce a structured imitation learning framework for interactive policies by combining generative single-agent policy learning with a flexible yet expressive game-theoretic structure. Our method explicitly separates learning into two steps: first, we learn individual behavioral patterns from multi-agent demonstrations using standard imitation learning; then, we structurally learn inter-agent dependencies by solving an inverse game problem. Preliminary results in a synthetic 5-agent social navigation task show that our method significantly improves non-interactive policies and performs comparably to the ground truth interactive policy using only 50 demonstrations. These results highlight the potential of structured imitation learning in interactive settings.
comment: Presented at the "Workshop on Generative Modeling Meets Human-Robot Interaction" at Robotics: Science and Systems 2025. Workshop website: https://sites.google.com/view/gai-hri/
♻ ☆ GRIM: Task-Oriented Grasping with Conditioning on Generative Examples AAAI-26
Task-Oriented Grasping (TOG) requires robots to select grasps that are functionally appropriate for a specified task - a challenge that demands an understanding of task semantics, object affordances, and functional constraints. We present GRIM (Grasp Re-alignment via Iterative Matching), a training-free framework that addresses these challenges by leveraging Video Generation Models (VGMs) together with a retrieve-align-transfer pipeline. Beyond leveraging VGMs, GRIM can construct a memory of object-task exemplars sourced from web images, human demonstrations, or generative models. The retrieved task-oriented grasp is then transferred and refined by evaluating it against a set of geometrically stable candidate grasps to ensure both functional suitability and physical feasibility. GRIM demonstrates strong generalization and achieves state-of-the-art performance on standard TOG benchmarks. Project website: https://grim-tog.github.io
comment: Accepted to AAAI-26 (Oral). Project website: https://grim-tog.github.io
♻ ☆ On the Surprising Effectiveness of Spectral Clipping in Learning Stable Linear and Latent-Linear Dynamical Systems
When learning stable linear dynamical systems from data, three important properties are desirable: i) predictive accuracy, ii) verifiable stability, and iii) computational efficiency. Unconstrained minimization of prediction errors leads to high accuracy and efficiency but cannot guarantee stability. Existing methods to enforce stability often preserve accuracy, but do so only at the cost of increased computation. In this work, we investigate if a seemingly-naive procedure can simultaneously offer all three desiderata. Specifically, we consider a post-hoc procedure in which we surgically manipulate the spectrum of the linear system after it was learned using unconstrained least squares. We call this approach spectral clipping (SC) as it involves eigen decomposition and subsequent reconstruction of the system matrix after any eigenvalues whose magnitude exceeds one have been clipped to one (without altering the eigenvectors). We also show that SC can be readily combined with Koopman operators to learn nonlinear dynamical systems that can generate stable predictions of nonlinear phenomena, such as those underlying complex dexterous manipulation skills involving multi-fingered robotic hands. Through comprehensive experiments involving two different applications and publicly available benchmark datasets, we show that this simple technique can efficiently learn highly-accurate predictive dynamics that are provably-stable. Notably, we find that SC can match or outperform strong baselines while being orders-of-magnitude faster. Finally, we find that SC can learn stable robot policies even when the training data includes unsuccessful or truncated demonstrations. Our code and datasets can be found at https://github.com/GT-STAR-Lab/spec_clip.
♻ ☆ Bench2FreeAD: A Benchmark for Vision-based End-to-end Navigation in Unstructured Robotic Environments
Most current end-to-end (E2E) autonomous driving algorithms are built on standard vehicles in structured transportation scenarios, lacking exploration of robot navigation for unstructured scenarios such as auxiliary roads, campus roads, and indoor settings. This paper investigates E2E robot navigation in unstructured road environments. First, we introduce two data collection pipelines - one for real-world robot data and another for synthetic data generated using the Isaac Sim simulator, which together produce an unstructured robotics navigation dataset -- FreeWorld Dataset. Second, we fine-tuned an efficient E2E autonomous driving model -- VAD -- using our datasets to validate the performance and adaptability of E2E autonomous driving models in these environments. Results demonstrate that fine-tuning through our datasets significantly enhances the navigation potential of E2E autonomous driving models in unstructured robotic environments. Thus, this paper presents the first dataset targeting E2E robot navigation tasks in unstructured scenarios, and provides a benchmark based on vision-based E2E autonomous driving algorithms to facilitate the development of E2E navigation technology for logistics and service robots. The project is available on Github.
comment: 7 pages, 9 figures
♻ ☆ Benchmarking LLM Privacy Recognition for Social Robot Decision Making
While robots have previously utilized rule-based systems or probabilistic models for user interaction, the rapid evolution of large language models (LLMs) presents new opportunities to develop LLM-powered robots for enhanced human-robot interaction (HRI). To fully realize these capabilities, however, robots need to collect data such as audio, fine-grained images, video, and locations. As a result, LLMs often process sensitive personal information, particularly within private environments, such as homes. Given the tension between utility and privacy risks, evaluating how current LLMs manage sensitive data is critical. Specifically, we aim to explore the extent to which out-of-the-box LLMs are privacy-aware in the context of household robots. In this work, we present a set of privacy-relevant scenarios developed using the Contextual Integrity (CI) framework. We first surveyed users' privacy preferences regarding in-home robot behaviors and then examined how their privacy orientations affected their choices of these behaviors (N = 450). We then provided the same set of scenarios and questions to state-of-the-art LLMs (N = 10) and found that the agreement between humans and LLMs was generally low. To further investigate the capabilities of LLMs as potential privacy controllers, we implemented four additional prompting strategies and compared their results. We discuss the performance of the evaluated models as well as the implications and potential of AI privacy awareness in human-robot interaction.
comment: 18 pages, 7 figures. Dakota Sullivan and Shirley Zhang contributed equally to this work
♻ ☆ Certified Coil Geometry Learning for Short-Range Magnetic Actuation and Spacecraft Docking Application
This paper presents a learning-based framework for approximating an exact magnetic-field interaction model, supported by both numerical and experimental validation. High-fidelity magnetic-field interaction modeling is essential for achieving exceptional accuracy and responsiveness across a wide range of fields, including transportation, energy systems, medicine, biomedical robotics, and aerospace robotics. In aerospace engineering, magnetic actuation has been investigated as a fuel-free solution for multi-satellite attitude and formation control. Although the exact magnetic field can be computed from the Biot-Savart law, the associated computational cost is prohibitive, and prior studies have therefore relied on dipole approximations to improve efficiency. However, these approximations lose accuracy during proximity operations, leading to unstable behavior and even collisions. To address this limitation, we develop a learning-based approximation framework that faithfully reproduces the exact field while dramatically reducing computational cost. The proposed method additionally provides a certified error bound, derived from the number of training samples, ensuring reliable prediction accuracy. The learned model can also accommodate interactions between coils of different sizes through appropriate geometric transformations, without retraining. To verify the effectiveness of the proposed framework under challenging conditions, a spacecraft docking scenario is examined through both numerical simulations and experimental validation.
comment: Submitted to IEEE Robotics and Automation Letters
♻ ☆ Sequential Autonomous Exploration-Based Precise Mapping for Mobile Robots through Stepwise and Consistent Motions
This paper proposes a 2-D autonomous exploration and mapping framework for LiDAR-based SLAM mobile robots, designed to address the major challenges on low-cost platforms, including process instability, map drift, and increased risks of collisions and deadlocks. For frontier search, the local-global sampling architecture based on Rapidly-exploring Random Trees (RRTs) is employed. For local exploration, the proposed Self-Convergent RRT (SC-RRT) efficiently covers the reachable space within a finite time while the robot remains stationary, without relying on motion-induced sampling diversity. In addition, traversability checks during RRT expansion and global RRT pruning upon map updates eliminate unreachable frontiers, reducing potential collisions and deadlocks. For frontier point navigation, a stepwise consistent motion strategy is employed to generate motion trajectories that are more amenable to stable scan matching. The resulting straight-segment and in-place-rotation pattern improves scan-matching robustness and effectively suppresses map drift on resource-constrained platforms. For the process control, the framework serializes frontier point selection and navigation, avoiding oscillations caused by frequent goal changes in conventional parallelized processes. The waypoint retracing mechanism is incorporated to generate repeated observations, triggering loop closure detection and backend optimization in graph-based SLAM, thereby improving map consistency. Experiments in challenging simulated and real-world environments validate the effectiveness of the framework. Compared with baseline methods, the proposed framework achieves higher mapping success rates and stronger robustness on resource-constrained robots and maintains consistent mapping quality across various LiDAR field-of-view (FoV) configurations.
comment: 9 pages, 10 figures. This work has been submitted to the IEEE for possible publication
♻ ☆ Model Predictive Inferential Control of Neural State-Space Models for Autonomous Vehicle Motion Planning
Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this paper, we investigate how to achieve MPC-based motion planning when a neural state-space model represents the vehicle dynamics. As the neural state-space model will lead to highly complex, nonlinear and nonconvex optimization landscapes, mainstream gradient-based MPC methods will struggle to provide viable solutions due to heavy computational load. In a departure, we propose the idea of model predictive inferential control (MPIC), which seeks to infer the best control decisions from the control objectives and constraints. Following this idea, we convert the MPC problem for motion planning into a Bayesian state estimation problem. Then, we develop a new implicit particle filtering/smoothing approach to perform the estimation. This approach is implemented as banks of unscented Kalman filters/smoothers and offers high sampling efficiency, fast computation, and estimation accuracy. We evaluate the MPIC approach through a simulation study of autonomous driving in different scenarios, along with an exhaustive comparison with gradient-based MPC. The simulation results show that the MPIC approach has considerable computational efficiency despite complex neural network architectures and the capability to solve large-scale MPC problems for neural state-space models.
♻ ☆ MonoDream: Monocular Vision-Language Navigation with Panoramic Dreaming
Shuo Wang, Yongcai Wang, Zhaoxin Fan, Yucheng Wang, Maiyue Chen, Kaihui Wang, Zhizhong Su, Wanting Li, Xudong Cai, Yeying Jin, Deying Li
Vision-Language Navigation (VLN) tasks often leverage panoramic RGB and depth inputs to provide rich spatial cues for action planning, but these sensors can be costly or less accessible in real-world deployments. Recent approaches based on Vision-Language Action (VLA) models achieve strong results with monocular input, yet they still lag behind methods using panoramic RGB-D information. We present MonoDream, a lightweight VLA framework that enables monocular agents to learn a Unified Navigation Representation (UNR). This shared feature representation jointly aligns navigation-relevant visual semantics (e.g., global layout, depth, and future cues) and language-grounded action intent, enabling more reliable action prediction. MonoDream further introduces Latent Panoramic Dreaming (LPD) tasks to supervise the UNR, which train the model to predict latent features of panoramic RGB and depth observations at both current and future steps based on only monocular input. Experiments on multiple VLN benchmarks show that MonoDream consistently improves monocular navigation performance and significantly narrows the gap with panoramic-based agents.
♻ ☆ TopAY: Efficient Trajectory Planning for Differential Drive Mobile Manipulators via Topological Paths Search and Arc Length-Yaw Parameterization
Differential drive mobile manipulators combine the mobility of wheeled bases with the manipulation capability of multi-joint arms, enabling versatile applications but posing considerable challenges for trajectory planning due to their high-dimensional state space and nonholonomic constraints. This paper introduces TopAY, an optimization-based planning framework designed for efficient and safe trajectory generation for differential drive mobile manipulators. The framework employs a hierarchical initial value acquisition strategy, including topological paths search for the base and parallel sampling for the manipulator. A polynomial trajectory representation with arc length-yaw parameterization is also proposed to reduce optimization complexity while preserving dynamic feasibility. Extensive simulation and real-world experiments validate that TopAY achieves higher planning efficiency and success rates than state-of-the-art method in dense and complex scenarios. The source code is released at https://github.com/TopAY-Planner/TopAY .
comment: 8 pages, 5 figures
♻ ☆ Scalable Policy Evaluation with Video World Models
Training generalist policies for robotic manipulation has shown great promise, as they enable language-conditioned, multi-task behaviors across diverse scenarios. However, evaluating these policies remains difficult because real-world testing is expensive, time-consuming, and labor-intensive. It also requires frequent environment resets and carries safety risks when deploying unproven policies on physical robots. Manually creating and populating simulation environments with assets for robotic manipulation has not addressed these issues, primarily due to the significant engineering effort required and the often substantial sim-to-real gap, both in terms of physics and rendering. In this paper, we explore the use of action-conditional video generation models as a scalable way to learn world models for policy evaluation. We demonstrate how to incorporate action conditioning into existing pre-trained video generation models. This allows leveraging internet-scale in-the-wild online videos during the pre-training stage, and alleviates the need for a large dataset of paired video-action data, which is expensive to collect for robotic manipulation. Our paper examines the effect of dataset diversity, pre-trained weight and common failure cases for the proposed evaluation pipeline. Our experiments demonstrate that, across various metrics, including policy ranking and the correlation between actual policy values and predicted policy values, these models offer a promising approach for evaluating policies without requiring real-world interactions.
♻ ☆ Task-Driven Implicit Representations for Automated Design of LiDAR Systems
Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary constraints. To achieve this, we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling. We then synthesize new LiDAR systems by modeling sensors as parametric distributions in 6D space and fitting these distributions to our learned implicit density using expectation-maximization, enabling efficient, constraint-aware LiDAR system design. We validate our method on diverse tasks in 3D vision, enabling automated LiDAR system design across real-world-inspired applications in face scanning, robotic tracking, and object detection.
♻ ☆ A Skeleton-Based Topological Planner for Exploration in Complex Unknown Environments ICRA 2025
The capability of autonomous exploration in complex, unknown environments is important in many robotic applications. While recent research on autonomous exploration have achieved much progress, there are still limitations, e.g., existing methods relying on greedy heuristics or optimal path planning are often hindered by repetitive paths and high computational demands. To address such limitations, we propose a novel exploration framework that utilizes the global topology information of observed environment to improve exploration efficiency while reducing computational overhead. Specifically, global information is utilized based on a skeletal topological graph representation of the environment geometry. We first propose an incremental skeleton extraction method based on wavefront propagation, based on which we then design an approach to generate a lightweight topological graph that can effectively capture the environment's structural characteristics. Building upon this, we introduce a finite state machine that leverages the topological structure to efficiently plan coverage paths, which can substantially mitigate the back-and-forth maneuvers (BFMs) problem. Experimental results demonstrate the superiority of our method in comparison with state-of-the-art methods. The source code will be made publicly available at: https://github.com/Haochen-Niu/STGPlanner.
comment: 7 pages, 7 figures. Accepted to be presented at the ICRA 2025