Whole-Body Dexterity (WBD) is a framework that enables robots to use their entire body and appendages as contact surfaces for grasping and manipulating objects. The approach generates a dataset of feasible whole-body grasps, trains reinforcement‑learning experts on a repositioning objective, and distills them into a single policy that relies on proprioception and object geometry. The distilled policy achieves zero‑shot transfer to a real Unitree G1 robot, successfully completing complex tasks such as stowing a box under one arm and moving it from a shelf to a table, with a 90% success rate in real‑world trials.
By Conor Mc Gartoll, An Dang, Pranay Thangeda, Arjun Gupta, Carolina Higuera, Nima Fazeli, Mustafa Mukadam, Manikantan Nambi
arXiv:2509. 06296v2 Announce Type: replace-cross Abstract: Traditional on-policy reinforcement learning (RL) controllers for quadrupedal locomotion often suffer from low data efficiency, requiring millions of interactions with simulated environments to achieve stable control.
By Francisco Affonso, Felipe Tommaselli, Jo\~ao H. Al\'essio, Vivian S. Medeiros, Mateus V. Gasparino, Girish Chowdhary, Marcelo Becker
arXiv:2607. 18135v1 Announce Type: cross Abstract: Learning-based approaches to locomotion have risen in popularity in recent years, showing the capability for complex legged locomotion and whole-body control.
By Jordan Dowdy, Jean Chagas Vaz
arXiv:2610.01260v1 Announce Type: cross
Abstract: Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinfor...
By Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger
OmniMimic is a training framework that expands limited animal demonstration data into a single multi‑gait policy for omnidirectional quadruped locomotion. It uses temporal reversal, constrained dynamics completion, and sagittal reflection to generate kinematic and physical supervision beyond the observed directions, then progressively expands command ranges and employs a shared actor with soft‑gated gait‑specialized residual experts. In simulation, OmniMimic improves foot‑position accuracy by 12.9% and velocity‑tracking error by 63.1% over the APEX baseline across four gaits.
By Sheng Wu, Guoqiang Zhao, Zhe Yang, Fei Teng, Zhikun Zhou, Yanlin Yang, Zheng Fang, Hong Zheng, Yaonan Wang, Kailun Yang
arXiv:2608. 12063v1 Announce Type: cross Abstract: Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, manual process of dense reward shaping.
By Martin Schuck, Maks Sorokin, Simone Manni, Duy Ta, Angela P. Schoellig, Marco Hutter, Simon Le Cleac'H, Jan Br\"udigam