Multi-object grasping is a fundamental yet challenging problem, constrained by data collection costs, varying spatial layouts, and the need for broad generalization. To address these challenges, we propose MoDex, a data-efficient framework that provides the first unified solution for universal multi-object dexterous gr...
Jing Xiong, Jia-Le Xie, Hao-Qi Yuan et al.· 0 citations
Humanoid loco-manipulation requires coordinated locomotion and manipulation informed by future scene evolution and whole-body motion, yet learning these capabilities is constrained by scarce robot demonstrations. Human video and motion datasets offer scalable supervision, but many contain only video or motion rather th...
Jun-Peng Yue, Bo-Yuan Li, Yuxuan Wang et al.· 0 citations
This framework surveys manipulation, navigation, locomotion, autonomous driving, and general embodied learning, tracing technical progressions, clarifying capability requirements, and examining datasets, benchmarks, and evaluation protocols.
Nan-Jie Yao, Hao Wang, Chong Cheng et al.· 1 citation
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