activity
20202024
most citedHierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs

13 citations · 15 across the 5 of their papers we have counts for

collaborators

9 papers

cs.RO2024

Vision-based Manipulation from Single Human Video with Open-World Object Graphs

Yifeng Zhu, Arisrei Lim, Peter Stone +1

This work presents an object-centric approach to learning vision-based manipulation skills from human videos. We investigate the problem of robot manipulation via imitation in the…

cs.RO2023

LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery

Weikang Wan, Yifeng Zhu, Rutav Shah +1

We introduce LOTUS, a continual imitation learning algorithm that empowers a physical robot to continuously and efficiently learn to solve new manipulation tasks throughout its lif…

cs.RO2023

Learning Generalizable Manipulation Policies with Object-Centric 3D Representations

Yifeng Zhu, Zhenyu Jiang, Peter Stone +1

We introduce GROOT, an imitation learning method for learning robust policies with object-centric and 3D priors. GROOT builds policies that generalize beyond their initial training…

cs.RO2023

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning

Xiaohan Zhang, Yifeng Zhu, Yan Ding +4

In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A well-developed state space…

cs.RO2022

Visually Grounded Task and Motion Planning for Mobile Manipulation

Xiaohan Zhang, Yifeng Zhu, Yan Ding +3

Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve…

cs.RO2021

Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik +2

Grasp detection in clutter requires the robot to reason about the 3D scene from incomplete and noisy perception. In this work, we draw insight that 3D reconstruction and grasp lear…