Musculoskeletal (MSK) humanoids provide a physiologically grounded embodiment for studying full-body motor control, but their high-dimensional muscle actuation, delayed activation dynamics, and redundant muscle--tendon structures make learning substantially harder than torque-driven humanoid control. Existing MSK bench...
Meng-Tao Ou, Zongzheng Zhang, Zheng-Hao Xiao et al.· 0 citations
EgoHumanoid-V2 is presented, the first egocentric human-to-humanoid skill transfer framework for coordinated whole-body loco-manipulation, which improves end-effector pose accuracy while preserving whole-body coordination and uses robot-arm rendering and training-time image augmentation to reduce the visual embodiment...
Jin Chen, Yi-Ming Jiang, Chong-Yang Xu et al.· 0 citations
P2P-T is introduced, from Pixel to Poses for Tool Manipulation, a data-efficient, object-centric framework that learns tool use directly from human demonstrations and achieves a 73% improvement over the previous state of the art in execution performance on complex, real-world tool manipulation tasks that currently rema...
Bang-Jun Wang, Long-Yan Wu, Yu-Kun Wei et al.· 0 citations
HandEdit serves as a critical resource at the intersection of image editing and robotics: it advances embodiment-aware editing models while enabling scalable dexterous robotic learning from abundant human video data, paving the way for more generalizable Embodied AI.
Zhen-Jie Yang, Xingyu Jiao, Guopeng Zhong et al.· 5 citations
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