73 citations · 202 across the 39 of their papers we have counts for
9 papers · 1 filter
Tired Actor: Fatigue-Informed Character Control
Shengyuan Zhang, Xinpeng Liu, Muchun Niu +5
Replicating human behavior with physics simulation has been a long-expected goal in character animation. Existing efforts have achieved impressive performance in imitating a wide s…
ChronoFlow-Policy: Unifying Past-Current-Future Interaction Flow in Visuomotor Policy Learning
Bokai Lin, Yifu Xu, Xinyu Zhan +6
Visual signals play a crucial role in policy learning by enabling models to capture object motion and interaction dynamics. Just as humans reason about actions using both past expe…
LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment
Yifu Xu, Bokai Lin, Xinyu Zhan +4
Scaling up robot learning is hindered by the scarcity of robotic demonstrations, whereas human videos offer a vast, untapped source of interaction data. However, bridging the embod…
L1 Sample Flow for Efficient Visuomotor Learning
Weixi Song, Zhetao Chen, Tao Xu +6
Denoising-based models, such as diffusion and flow matching, have been a critical component of robotic manipulation for their strong distribution-fitting and scaling capacity. Conc…
SIME: Enhancing Policy Self-Improvement with Modal-level Exploration
Yang Jin, Jun Lv, Wenye Yu +3
Self-improvement requires robotic systems to initially learn from human-provided data and then gradually enhance their capabilities through interaction with the environment. This i…
Dense Policy: Bidirectional Autoregressive Learning of Actions
Yue Su, Xinyu Zhan, Hongjie Fang +5
Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, ha…
Motion Before Action: Diffusing Object Motion as Manipulation Condition
Yue Su, Xinyu Zhan, Hongjie Fang +3
Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning…
Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition
Shengcheng Luo, Quanquan Peng, Jun Lv +4
Employing a teleoperation system for gathering demonstrations offers the potential for more efficient learning of robot manipulation. However, teleoperating a robot arm equipped wi…
HumanVLA: Towards Vision-Language Directed Object Rearrangement by Physical Humanoid
Xinyu Xu, Yizheng Zhang, Yong-Lu Li +2
Physical Human-Scene Interaction (HSI) plays a crucial role in numerous applications. However, existing HSI techniques are limited to specific object dynamics and privileged inform…