GigaBrain-WBC-0.5 is a Behavior World Model that uses a causal Transformer to predict next actions, states, and a distribution over latent behavior commands for humanoid whole-body control. It incorporates an automatic terrain-annotation pipeline to recover 3D contact geometry from motion data, allowing the model to learn how terrain and objects influence dynamics. The system detects implausible commands online, retracts them onto learned behaviors, and achieves high success rates in terrain interaction, command robustness, and fall recovery, with promising hardware trials on different robots.
By Ziyang Cheng, Tianshu Tang, Jinxin Lan, Xinze Chen, Yuhan Gong, Zhichao Liu, Changzhong Wu, Yahao Mao, Zongyan Deng, Mingxuan Ma, Huasen Xi, Yilong Liu, Yutong Wu, Xiaofeng Wang, Yang Wang, Yun Ye, Guan Huang, Xiaojie Jin, Zheng Zhu, Jiwen Lu
arXiv:2606. 08253v1 Announce Type: cross Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately.
By Alessandro Montenegro, Shihao Li, Puze Liu, Alberto Maria Metelli, Jan Peters
arXiv:2510. 08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities.
By Zhenyu Zhao, Hongyi Jing, Xiawei Liu, Jiageng Mao, Abha Jha, Hanwen Yang, Rong Xue, Sergey Zakharov, Vitor Guizilini, Yue Wang
arXiv:2606. 27581v1 Announce Type: cross Abstract: Current humanoid reinforcement-learning policies excel at free-space motions but struggle with contact-rich tasks, as pure kinematic tracking cannot resolve the physical ambiguities of interacting with objects and uneven terrain.
By Sirui Chen, Shibo Zhao, Zhen Wu, Jiaman Li, Guanya Shi, C. Karen Liu
ULTRA is a unified framework for autonomous humanoid whole-body locomotion and manipulation that overcomes limitations of prior methods by combining a physics-driven neural retargeting algorithm with a multimodal controller. The retargeting algorithm translates large-scale motion capture data into physically plausible humanoid motions, while the controller learns to handle both dense motion references and sparse task specifications using a range of sensory inputs, from accurate motion-capture states to noisy egocentric vision. In simulation and on a real Unitree G1 humanoid, ULTRA demonstrates improved generalization and robustness, enabling coordinated whole-body behavior from sparse intent without relying on test-time reference motions.
By Xialin He, Sirui Xu, Xinyao Li, Runpei Dong, Liuyu Bian, Yu-Xiong Wang, Liang-Yan Gui
arXiv:2607. 07370v1 Announce Type: cross Abstract: In embodied intelligence systems, the motion controller serves as the critical bridge between semantic reasoning and physical execution.
By Xufeng Zhao, Fuzhi Yang, Jianhui Chen, Li Gao, Zhang Meng, Jie Gao, Yao Zheng, Wenyu Liu, Menglin Yang, Minqi Gu, Yaru Zhao, Honglin Han, Shihui Su, Zixiao Tang, Liu Liu, Mu Xu, Yang Cai, Wenbin Tang