15 papers
LAMP: Latent Motion Prior-Guided Real-World Learning for Dexterous Hand Manipulation
Xinye Yang, Zhiyuan Ma, Hongze Yu +5
Real-world learning for dexterous hands remains brittle because high-dimensional hand actions amplify imitation errors and make reinforcement-learning exploration prone to contact-…
CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation
Yishuai Cai, Xinglin Chen, Yunxin Mao +6
Behavior Trees (BTs) offer a powerful paradigm for designing modular and reactive robot controllers. BT planning, an emerging field, provides theoretical guarantees for the automat…
One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
Yuwan Liu, Hongze Yu, Song Liu +5
Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification. Prior work has incorporated…
RetrDex: Efficient Object Retrieval in Cluttered Scenes with a Dexterous Hand
Fengshuo Bai, Yu Li, Jie Chu +5
Retrieving objects buried beneath clutter is both challenging and time-consuming, as complex support relationships make manipulation particularly difficult. Existing methods either…
EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos
Yifan Zhong, Zhang Chen, Tianrui Guan +13
Steerability is a defining capability of generalist robot policies, yet remains largely absent in dexterous-hand systems for lack of large-scale, language-aligned, and action-accur…
Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation
Jianing Guo, Fangzheng Chen, Zihao Mao +12
Flow matching has emerged as a standard paradigm for robotic manipulation owing to its strong expressive power for modelling complex, multimodal action distributions, alongside sim…