1 citations · 1 across the 5 of their papers we have counts for
9 papers
How Should a Camera-First AI Agent Work?
Xiaofan Wu, Xi Zeng, Miaoxia Chen +8
Camera-first agents must act on the visual world as it unfolds. We argue that the central obstacle for future camera-first agents is not recognition ability - multimodal models (vi…
NEXUS: Perceptive Whole-Body Control for Terrain-Adaptive Teleoperation
Xiangyu Miao, Junsong Wu, Jiyuan Shi +6
Whole-body teleoperation requires a humanoid robot to reproduce a human operator's behavior even when their terrains differ. This demands that the robot perceive local terrain and…
PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations
Yang Zhang, Jiangyuan Zhao, Chenyou Fan +11
Vision-Language-Action (VLA) models advance robotic control via strong visual-linguistic priors. However, existing VLAs predominantly frame pretraining as supervised behavior cloni…
TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control
Weiji Xie, Jiakun Zheng, Jinrui Han +4
Recent advances in humanoid whole-body motion tracking have enabled the execution of diverse and highly coordinated motions on real hardware. However, existing controllers are comm…
KungfuBot2: Learning Versatile Motion Skills for Humanoid Whole-Body Control
Jinrui Han, Weiji Xie, Jiakun Zheng +4
Learning versatile whole-body skills by tracking various human motions is a fundamental step toward general-purpose humanoid robots. This task is particularly challenging because a…
KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills
Weiji Xie, Jinrui Han, Jiakun Zheng +6
Humanoid robots are promising to acquire various skills by imitating human behaviors. However, existing algorithms are only capable of tracking smooth, low-speed human motions, eve…
Information-Theoretic Reward Decomposition for Generalizable RLHF
Liyuan Mao, Haoran Xu, Amy Zhang +2
A generalizable reward model is crucial in Reinforcement Learning from Human Feedback (RLHF) as it enables correctly evaluating unseen prompt-response pairs. However, existing rewa…
Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning
Weiji Xie, Chenjia Bai, Jiyuan Shi +4
Humans possess delicate dynamic balance mechanisms that enable them to maintain stability across diverse terrains and under extreme conditions. However, despite significant advance…
Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training
Haoran He, Chenjia Bai, Ling Pan +3
Learning a generalist embodied agent capable of completing multiple tasks poses challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In contrast, a…