Humanoid robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids other body contacts with the environment. To extend manipulation beyond the hands, we present PanTact, a system that learns humanoid whole-body dexterity, the capability...
Xiao-Meng Xu, Han Zhang, Zhian Ruan et al.· 0 citations
Learning a single perceptive policy for diverse humanoid loco-manipulation requires unifying high-level task decisions without losing the state-dependent physical responses that make each interaction succeed. This makes the representation between high-level planning and whole-body control critical: kinematic or contact...
Precise end-effector tracking during humanoid whole-body motion is challenging due to floating-base oscillations, gravity, dynamic coupling, and locomotion-induced disturbances. We propose ResGAC, a whole-body humanoid controller for precise end-effector pose tracking that combines geometric admittance control (GAC) wi...
Joohwan Seo, Xiao-Feng Guo, Jin-Kun Cao et al.· 0 citations
Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. However, collecting diverse, high-quality interaction videos, such as clips that clearly show a person's full body and unoccluded interactions with objects, poses a practical...
Zihan Wang, Zhen Wu, Pieter Abbeel et al.· 0 citations
A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model generates executable trajectories by conditioning on task goal constraints. However, trajectory-based methods rely on costly supervision, scale poorly with sequence len...
Si-Long Yong, Anji Liu, Cun-Xi Dai et al.· 0 citations
Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital world. However, bringing this success to the physical world remains challenging. On the one hand, direct generation methods (e.g., Code as Policies) often suffer from the L...
Jiaheng Hu, Roberto Martín-Martín, Peter Stone et al.· 2 citations· ⚡1
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely con...
Safe-Stop is proposed, a task-agnostic framework that pairs a learned stop policy with learned stoppability estimators, and yields decisions that are robust without sacrificing reactivity.
Jun-Feng Long, Pieter Abbeel, K. Sreenath et al.· 0 citations
This work introduces ABC, a fully open-source stack for manipulation with behavior cloning, and offers a co-training recipe that produces correlated simulation and real-world evaluation, offering a reliable proxy for ablating model-design and training decisions before costly real-world evaluation.
Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh et al.· arXiv.org· 4 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
