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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
Event Detection for Active Lower Limb Prosthesis
J. D. Clark, P. Ellison · 2025-07-24 · via cs.RO updates on arXiv.org

Accurate event detection is key to the successful design of semi-passive and powered prosthetics. Kinematically, the natural knee is complex, with translation and rotation components that have a substantial impact on gait characteristics. When simplified to a pin joint, some of this behaviour is lost. This study investigates the role of cruciate ligament stretch in event detection. A bicondylar knee design was used, constrained by analogues of the anterior and posterior cruciate ligaments. This offers the ability to characterize knee kinematics by the stretch of the ligaments. The ligament stretch was recorded using LVDTs parallel to the ligaments of the Russell knee on a bent knee crutch. Which was used to capture data on a treadmill at 3 speeds. This study finds speed dependence within the stretch of the cruciate ligaments, prominently around 5\% and 80\% of the gait cycle for the posterior and anterior. The cycle profile remains consistent with speed; therefore, other static events such as the turning point feature at around 90\% and 95\% of the cycle, for the posterior and anterior, respectively, could be used as a predictive precursor for initial contact. Likewise at 90\% and 95\%, another pair of turning points that in this case could be used to predict foot flat. This concludes that the use of a bicondylar knee design could improve the detection of events during the gait cycle, and therefore could increase the accuracy of subsequent controllers for powered prosthetics.