2 citations · 3 across the 6 of their papers we have counts for
7 papers
Grounded in Time: A Multi-Source Dataset and Benchmark for Temporal Grounding in Robotic Manipulation
Yi Wang, Yang Yang, Guangqi Xu +9
Robotic manipulation often requires inferring task-relevant states from past interactions when the current observation alone is insufficient to determine the appropriate action. De…
CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
Kailun Huang, Zikang Xie, Yanzhe Xie +8
Humans primarily rely on walking and running to traverse complex terrains. Similarly, humanoid robots should be able to smoothly transition between walking and running while mainta…
Iterative Shaping of Multi-Particle Aggregates based on Action Trees and VLM
Hoi-Yin Lee, Peng Zhou, Anqing Duan +2
In this paper, we address the problem of manipulating multi-particle aggregates using a bimanual robotic system. Our approach enables the autonomous transport of dispersed particle…
Non-Prehensile Tool-Object Manipulation by Integrating LLM-Based Planning and Manoeuvrability-Driven Controls
Hoi-Yin Lee, Peng Zhou, Anqing Duan +3
The ability to wield tools was once considered exclusive to human intelligence, but it's now known that many other animals, like crows, possess this capability. Yet, robotic system…
Revolutionizing Packaging: A Robotic Bagging Pipeline with Constraint-aware Structure-of-Interest Planning
Jiaming Qi, Peng Zhou, Pai Zheng +4
Bagging operations, common in packaging and assisted living applications, are challenging due to a bag's complex deformable properties. To address this, we develop a robotic system…
Bimanual Deformable Bag Manipulation Using a Structure-of-Interest Based Neural Dynamics Model
Peng Zhou, Pai Zheng, Jiaming Qi +5
The manipulation of deformable objects by robotic systems presents a significant challenge due to their complex and infinite-dimensional configuration spaces. This paper introduces…
Explicit-Implicit Subgoal Planning for Long-Horizon Tasks with Sparse Reward
Fangyuan Wang, Anqing Duan, Peng Zhou +4
The challenges inherent in long-horizon tasks in robotics persist due to the typical inefficient exploration and sparse rewards in traditional reinforcement learning approaches. To…