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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Funabot-Upper: McKibben Actuated Haptic Suit Inducing Kin...
Haru Fukatsu, Ryoji Yasuda, Yuki Funabora, Shinji Doki · 2025-11-20 · via cs.RO updates on arXiv.org

This paper presents Funabot-Upper, a wearable haptic suit that enables users to perceive 14 upper-body motions, including those of the trunk, shoulder, elbow, and wrist. Inducing kinesthetic perception through wearable haptic devices has attracted attention, and various devices have been developed in the past. However, these have been limited to verifications on single body parts, and few have applied the same method to multiple body parts as well. In our previous study, we developed a technology that uses the contraction of artificial muscles to deform clothing in three dimensions. Using this technology, we developed a haptic suit that induces kinesthetic perception of 7 motions in multiple upper body. However, perceptual mixing caused by stimulating multiple human muscles has occurred between the shoulder and the elbow. In this paper, we established a new, simplified design policy and developed a novel haptic suit that induces kinesthetic perceptions in the trunk, shoulder, elbow, and wrist by stimulating joints and muscles independently. We experimentally demonstrated the induced kinesthetic perception and examined the relationship between stimulation and perceived kinesthetic perception under the new design policy. Experiments confirmed that Funabot-Upper successfully induces kinesthetic perception across multiple joints while reducing perceptual mixing observed in previous designs. The new suit improved recognition accuracy from 68.8% to 94.6% compared to the previous Funabot-Suit, demonstrating its superiority and potential for future haptic applications.