Learning Robot Policies for Whole-Body Mobility Assistance via Dynamic Human Motion Generation
cs.RO
Submitted: 2026-10-13
Updated: 2026-10-13
Comments: 9 pages, 8 figures, 3 tables
Code: https://github.com/Genesis-Embodied-AI/genesis-world
Project page: https://rchi-lab.github.io/human-gen-mobility-assistance
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- DeFM: Learning Foundation Representations from Depth for Robotics
- DepthVLA: Enhancing Vision-Language-Action Models with Depth-Aware Spatial Reasoning
- Proximal Policy Optimization Algorithms
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies