9 citations · 9 across the 9 of their papers we have counts for
9 papers
The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction
Xunzhe Zhou, Yiyang Cai, Fengyi Wang +9
Humans imitate at the level of intent: given a demonstration, we infer its goal and carry it out with whatever tools, objects, and layouts are at hand. Current robot policies inste…
RoboLineage: Agent-Native Data Lifecycle Governance Across Robot Policy Iterations
Qian Luo, Wentao Guo, Zhennan Qin +4
We present RoboLineage, an agent-native data lifecycle governance system for robot policy iteration. Modern robot policies improve through repeated data collection, review, retrain…
Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning
Qian Luo, Ruizhe Liu, Pei Zhou +2
We study how trajectory-shape diversity in demonstrations affects imitation learning (IL) performance across models, tasks, and data scales. We introduce Geometric Entropy (H_G), a…
Robust and Generalized Humanoid Motion Tracking
Yubiao Ma, Han Yu, Jiayin Xie +7
Learning a general humanoid whole-body controller is challenging because practical reference motions can exhibit noise and inconsistencies after being transferred to the robot doma…
Hyper-GoalNet: Goal-Conditioned Manipulation Policy Learning with HyperNetworks
Pei Zhou, Wanting Yao, Qian Luo +2
Goal-conditioned policy learning for robotic manipulation presents significant challenges in maintaining performance across diverse objectives and environments. We introduce Hyper-…
HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data
Ruizhe Liu, Pei Zhou, Qian Luo +4
Effective generalization in robotic manipulation requires representations that capture invariant patterns of interaction across environments and tasks. We present a self-supervised…
InfoCon: Concept Discovery with Generative and Discriminative Informativeness
Ruizhe Liu, Qian Luo, Yanchao Yang
We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks. We propose that the decision to conceptuali…
BBSEA: An Exploration of Brain-Body Synchronization for Embodied Agents
Sizhe Yang, Qian Luo, Anumpam Pani +1
Embodied agents capable of complex physical skills can improve productivity, elevate life quality, and reshape human-machine collaboration. We aim at autonomous training of embodie…
Text2Reward: Reward Shaping with Language Models for Reinforcement Learning
Tianbao Xie, Siheng Zhao, Chen Henry Wu +5
Designing reward functions is a longstanding challenge in reinforcement learning (RL); it requires specialized knowledge or domain data, leading to high costs for development. To a…