arXiv:2607. 00642v1 Announce Type: new Abstract: Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models.
By Roberto Capobianco (Sony AI, Zurich, Switzerland), Harm van Seijen (Sony AI, North America, various locations), Nolan D. Bard (Sony AI, North America, various locations), Neil Burch (Sony AI, North America, various locations), Fatima Davelouis (Sony AI, North America, various locations), Josh Davidson (Sony AI, North America, various locations), Alisa Devlic (Sony AI, Zurich, Switzerland), Yunshu Du (Sony AI, North America, various locations), Ishan Durugkar (Sony AI, North America, various locations), Siddhant Gangapurwala (Sony AI, North America, various locations), Daniel Hernandez (Sony AI, North America, various locations), G. Zacharias Holland (Sony AI, North America, various locations), Sahil Jain (Sony AI, North America, various locations), Kenta Kawamoto (Sony AI, Tokyo, Japan), Raksha Kumaraswamy (Sony AI, North America, various locations), Patrick MacAlpine (Sony AI, North America, various locations), Dustin R. Morrill (Sony AI, North America, various locations), Declan Oller (Sony AI, North America, various locations), Francesco Riccio (Sony AI, Zurich, Switzerland), Akanksha Saran (Sony AI, North America, various locations), Craig Sherstan (Sony AI, Tokyo, Japan), Kaushik Subramanian (Sony AI, Zurich, Switzerland), Thomas J. Walsh (Sony AI, North America, various locations), Samuel Barrett (Sony AI, North America, various locations), Kizza N. Frisbee (Sony AI, North America, various locations), Mady Govil (Sony AI, North America, various locations), Johannes G\"unther (Sony AI, North America, various locations), Varun R. Kompella (Sony AI, North America, various locations), James A. MacGlashan (Sony AI, North America, various locations), Maxwell Svetlik (Sony AI, North America, various locations), Michael D. Thomure (Sony AI, North America, various locations), Jaden B. Travnik (Sony AI, North America, various locations), Kevin Waugh (Sony AI, North America, various locations), Elahe Aghapour (Sony AI, North America, various locations), Florian Fuchs (Sony AI, Zurich, Switzerland), Andreanne Lemay (Sony AI, North America, various locations), Shruti Mishra (Sony AI, Zurich, Switzerland), Takuma Seno (Sony AI, Tokyo, Japan), Peter Stone (Sony AI, North America, various locations), Michael Spranger (Sony AI, Tokyo, Japan), Peter R. Wurman (Sony AI, North America, various locations)
arXiv:2606. 11092v1 Announce Type: cross Abstract: Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets.
By Yichao Zhong, Yidan Lu, Yuhang Lu, Tianyang Tang, Haoguang Mai, Yixuan Pan, Tianyu Li, Li Chen, Jingbo Wang, Zhongyu Li, Peng Lu, Hongyang Li
arXiv:2603.19305v3 Announce Type: replace-cross
Abstract: Humanoid robots are expected to execute agile and expressive whole-body motions in real-world settings. Existing text-to-motion generation mo...
By Jiacheng Bao, Haoran Yang, Yucheng Xin, Junhong Liu, Yuecheng Xu, Han Liang, Pengfei Han, Xiaoguang Ma, Dong Wang, Bin Zhao
arXiv:2602.08370v2 Announce Type: replace-cross
Abstract: Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unl...
By Yeke Chen, Shihao Dong, Xiaoyu Ji, Jingkai Sun, Zeren Luo, Liu Zhao, Jiahui Zhang, Wanyue Li, Ji Ma, Bowen Xu, Yimin Han, Xuanyi Li, Yudong Zhao, Liyun Li, Peng Lu
arXiv:2506. 12851v3 Announce Type: replace-cross Abstract: Humanoid robots are promising to acquire various skills by imitating human behaviors.
By Weiji Xie, Jinrui Han, Jiakun Zheng, Huanyu Li, Xinzhe Liu, Jiyuan Shi, Weinan Zhang, Chenjia Bai, Xuelong Li
arXiv:2606. 30362v1 Announce Type: cross Abstract: While current Behavior Foundation Models (BFMs) provide robust control priors for humanoids, they only execute pre-defined reference motions.
By Xiao Chen, Weishuai Zeng, Xiaojie Niu, Zirui Wang, Jianan Li, Huayi Wang, Furui Xu, Jiahe Chen, Weixiang Zhong, Lihe Ding, Kailin Li, Jiangmiao Pang, Tai Wang, Tianfan Xue, Jingbo Wang
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:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.
By Zhaoyuan Gu, Yipu Chen, Zimeng Chai, Alfred Cueva, Thong Nguyen, Yifan Wu, Huishu Xue, Minji Kim, Isaac Legene, Fukang Liu, KyoungMok Kim, Ayan Barula, Yongxin Chen, Ye Zhao
arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.
By Jan Ole von Hartz, Adrian R\"ofer, Joschka Boedecker, Abhinav Valada
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:2609.38172v1 Announce Type: cross
Abstract: Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. How...
By Zihan Wang, Zhen Wu, Pieter Abbeel, Rocky Duan, Jitendra Malik, Carmelo Sferrazza, C. Karen Liu, Guanya Shi, Angjoo Kanazawa
arXiv:2605.01234v2 Announce Type: replace
Abstract: We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides $140+$ hours of reconstructed singles and doubles gameplay from mon...
By Nima Rahmanian, Daniel Kienzle, Thomas Gossard, Dvij Kalaria, Rainer Lienhart, Shankar Sastry