5 papers
Being-M0.7: A Latent World-Action Model for Humanoid Robots
Junpeng Yue, Boyuan Li, Yuxuan Wang +11
Humanoid loco-manipulation requires coordinated locomotion and manipulation informed by future scene evolution and whole-body motion, yet learning these capabilities is constrained…
General Humanoid Whole-Body Control via Pretraining and Fast Adaptation
Zepeng Wang, Jiangxing Wang, Shiqing Yao +8
Learning a general whole-body controller for humanoid robots remains challenging due to the diversity of motion distributions, the difficulty of fast adaptation, and the need for r…
JAEGER: Dual-Level Humanoid Whole-Body Controller
Ziluo Ding, Haobin Jiang, Yuxuan Wang +7
This paper presents JAEGER, a dual-level whole-body controller for humanoid robots that addresses the challenges of training a more robust and versatile policy. Unlike traditional…
RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
Junpeng Yue, Zepeng Wang, Yuxuan Wang +7
This paper focuses on a critical challenge in robotics: translating text-driven human motions into executable actions for humanoid robots, enabling efficient and cost-effective lea…
From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
Yuxuan Wang, Ming Yang, Ziluo Ding +5
Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in trainin…