2 citations · 5 across the 13 of their papers we have counts for
14 papers
AdaTempo: Learning Shared Relative Tempo from Demonstrations for Faster Robot Manipulation
Jiale Cao, Yike Niu, Zhengrong Xue +1
Visuomotor policies trained via imitation learning often inherit the unnecessarily slow timing of teleoperated demonstrations. Yet uniform speedup is unreliable because different p…
MemoryWAM: Efficient World Action Modeling with Persistent Memory
Sizhe Yang, Juncheng Mu, Tianming Wei +8
Robust robotic manipulation in the real world requires not only an understanding of the current observation, but also memory and dynamics modeling. World action models (WAMs) posse…
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
Jiawei Zhang, Kaizhe Hu, Yingqian Huang +3
Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.…
ArrayTac: A Closed-Loop Piezoelectric Tactile Platform for Continuously Tunable Rendering of Shape, Stiffness, and Friction
Tianhai Liang, Shiyi Guo, Baiye Cheng +3
Human touch depends on the integration of shape, stiffness, and friction, yet existing tactile displays cannot render these cues together as continuously tunable, high-fidelity sig…
X-Distill: Cross-Architecture Vision Distillation for Visuomotor Learning
Maanping Shao, Feihong Zhang, Gu Zhang +3
Visuomotor policies often leverage large pre-trained Vision Transformers (ViTs) for their powerful generalization capabilities. However, their significant data requirements present…
MoE-DP: An MoE-Enhanced Diffusion Policy for Robust Long-Horizon Robotic Manipulation with Skill Decomposition and Failure Recovery
Baiye Cheng, Tianhai Liang, Suning Huang +5
Diffusion policies have emerged as a powerful framework for robotic visuomotor control, yet they often lack the robustness to recover from subtask failures in long-horizon, multi-s…
HDP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning
Yiyang Lu, Yufeng Tian, Zhecheng Yuan +4
Visuomotor policy learning has witnessed substantial progress in robotic manipulation, with recent approaches predominantly relying on generative models to model the action distrib…
DemoSpeedup: Accelerating Visuomotor Policies via Entropy-Guided Demonstration Acceleration
Lingxiao Guo, Zhengrong Xue, Zijing Xu +1
Imitation learning has shown great promise in robotic manipulation, but the policy's execution is often unsatisfactorily slow due to commonly tardy demonstrations collected by huma…
DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning
Zhengrong Xue, Shuying Deng, Zhenyang Chen +3
Visuomotor policies have shown great promise in robotic manipulation but often require substantial amounts of human-collected data for effective performance. A key reason underlyin…
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning
Suning Huang, Zheyu Zhang, Tianhai Liang +6
Visual deep reinforcement learning (RL) enables robots to acquire skills from visual input for unstructured tasks. However, current algorithms suffer from low sample efficiency, li…
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features
Chengkai Hou, Zhengrong Xue, Bingyang Zhou +3
Detecting 3D keypoints with semantic consistency is widely used in many scenarios such as pose estimation, shape registration and robotics. Currently, most unsupervised 3D keypoint…
AToM-Bot: Embodied Fulfillment of Unspoken Human Needs with Affective Theory of Mind
Wei Ding, Fanhong Li, Ziteng Ji +2
We propose AToM-Bot, a novel task generation and execution framework for proactive robot-human interaction, which leverages the human mental and physical state inference capabiliti…
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation
Chongkai Gao, Zhengrong Xue, Shuying Deng +4
We present RiEMann, an end-to-end near Real-time SE(3)-Equivariant Robot Manipulation imitation learning framework from scene point cloud input. Compared to previous methods that r…
OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation
Yuerong Li, Zhengrong Xue, Huazhe Xu
Temporal action segmentation is typically achieved by discovering the dramatic variances in global visual descriptors. In this paper, we explore the merits of local features by pro…