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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
Aerial Interaction with Tactile Sensing
Xiaofeng Guo, Guanqi He, Mohammadreza Mousaei, Junyi Geng, Guany · 2023-09-30 · via cs.RO updates on arXiv.org

While autonomous Uncrewed Aerial Vehicles (UAVs) have grown rapidly, most applications only focus on passive visual tasks. Aerial interaction aims to execute tasks involving physical interactions, which offers a way to assist humans in high-risk, high-altitude operations, thereby reducing cost, time, and potential hazards. The coupled dynamics between the aerial vehicle and manipulator, however, pose challenges for precision control. Previous research has typically employed either position control, which often fails to meet mission accuracy, or force control using expensive, heavy, and cumbersome force/torque sensors that also lack local semantic information. Conversely, tactile sensors, being both cost-effective and lightweight, are capable of sensing contact information including force distribution, as well as recognizing local textures. Existing work on tactile sensing mainly focuses on tabletop manipulation tasks within a quasi-static process. In this paper, we pioneer the use of vision-based tactile sensors on a fully-actuated UAV to improve the accuracy of the more dynamic aerial manipulation tasks. We introduce a pipeline utilizing tactile feedback for real-time force tracking via a hybrid motion-force controller and a method for wall texture detection during aerial interactions. Our experiments demonstrate that our system can effectively replace or complement traditional force/torque sensors, improving flight performance by approximately 16% in position tracking error when using the fused force estimate compared to relying on a single sensor. Our tactile sensor achieves 93.4% accuracy in real-time texture recognition and 100% post-contact. To the best of our knowledge, this is the first work to incorporate a vision-based tactile sensor into aerial interaction tasks.