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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
Predicting center of mass position in non-cyclic activiti...
Mohsen Alizadeh Noghani, Edgar Bolívar-Nieto · 2024-11-26 · via cs.RO updates on arXiv.org

The whole-body center of mass (CoM) plays an important role in quantifying human movement. Prediction of future CoM trajectory, modeled as a point mass under influence of external forces, can be a surrogate for inferring intent. Given the current CoM position and velocity, predicting the future CoM position by forward integration requires a forecast of CoM accelerations during the prediction horizon. However, it is unclear how assumptions about the acceleration, prediction horizon length, and information from ground reaction forces (GRFs), which provide the instantaneous acceleration, affect the prediction. We study these factors by analyzing data of 10 healthy young adults performing 14 non-cyclic activities. We assume that the acceleration during a horizon will be 1) zero, 2) remain constant, or 3) converge to zero as a cubic trajectory, and perform predictions for horizons of 125 to 625 milliseconds. We quantify the prediction performance by comparing the position error and accuracy of identifying the main direction of displacement against trajectories obtained from a whole-body marker set. For all the assumed accelerations profiles, position errors grow quadratically with horizon length ($R^2 > 0.930$) while the accuracy of the predicted direction decreases linearly ($R^2>0.615$). Post-hoc tests reveal that the constant and cubic profiles, which utilize the GRFs, outperform the zero-acceleration assumption in position error ($p<0.001$, Cohen's $d>3.23$) and accuracy ($p<0.034$, Cohen's $d>1.44)$ at horizons of 125 and 250$\,ms$. The results provide evidence for benefits of incorporating GRFs into predictions and point to 250$\,ms$ as a threshold for horizon length in predictive applications.