9 papers
In-Context Reinforcement Learning under Non-Stationarity: A Survey
A Run, Ziluo Ding
The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest in in-context reinforcement l…
HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation
Shuanghao Bai, Meng Li, Xinyuan Lv +14
Humans achieve complex manipulation through coordinated whole-body control, whereas most Vision-Language-Action (VLA) models treat robot body parts largely independently, making hi…
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…
SENTINEL: A Fully End-to-End Language-Action Model for Humanoid Whole Body Control
Yuxuan Wang, Haobin Jiang, Shiqing Yao +2
Existing humanoid control systems often rely on teleoperation or modular generation pipelines that separate language understanding from physical execution. However, the former is e…
Seeing the Unseen in Low-light Spike Streams
Liwen Hu, Yang Li, Mianzhi Liu +5
Spike camera, a type of neuromorphic sensor with high-temporal resolution, shows great promise for high-speed visual tasks. Unlike traditional cameras, spike camera continuously ac…
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…