13 citations · 49 across the 13 of their papers we have counts for
14 papers
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…
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…
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…
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…
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
Bo Liu, Yifeng Zhu, Chongkai Gao +4
Lifelong learning offers a promising paradigm of building a generalist agent that learns and adapts over its lifespan. Unlike traditional lifelong learning problems in image and te…
A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter
Kechun Xu, Shuqi Zhao, Zhongxiang Zhou +4
We focus on the task of language-conditioned grasping in clutter, in which a robot is supposed to grasp the target object based on a language instruction. Previous works separately…
VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors
Yifeng Zhu, Abhishek Joshi, Peter Stone +1
We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop visuomotor policies for robot manipulation. Our approach constructs object-centric represe…
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments
Mingyo Seo, Ryan Gupta, Yifeng Zhu +3
We tackle the problem of perceptive locomotion in dynamic environments. In this problem, a quadrupedal robot must exhibit robust and agile walking behaviors in response to environm…
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…
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation
Yifeng Zhu, Peter Stone, Yuke Zhu
We tackle real-world long-horizon robot manipulation tasks through skill discovery. We present a bottom-up approach to learning a library of reusable skills from unsegmented demons…
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…
Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs
Yifeng Zhu, Jonathan Tremblay, Stan Birchfield +1
We present a visually grounded hierarchical planning algorithm for long-horizon manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning and low-l…
Fast Uncertainty Quantification for Deep Object Pose Estimation
Guanya Shi, Yifeng Zhu, Jonathan Tremblay +4
Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transf…
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Sihang Guo, Ruohan Zhang, Bo Liu +4
Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are…