339 citations · 423 across the 9 of their papers we have counts for
10 papers
Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordance
Zhanpeng He, Nikhil Chavan-Dafle, Jinwook Huh +2
The choice of a grasp plays a critical role in the success of downstream manipulation tasks. Consider a task of placing an object in a cluttered scene; the majority of possible gra…
Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning
Siddharth Singi, Zhanpeng He, Alvin Pan +5
In a Human-in-the-Loop paradigm, a robotic agent is able to act mostly autonomously in solving a task, but can request help from an external expert when needed. However, knowing wh…
Discovering Synergies for Robot Manipulation with Multi-Task Reinforcement Learning
Zhanpeng He, Matei Ciocarlie
Controlling robotic manipulators with high-dimensional action spaces for dexterous tasks is a challenging problem. Inspired by human manipulation, researchers have studied generati…
UMPNet: Universal Manipulation Policy Network for Articulated Objects
Zhenjia Xu, Zhanpeng He, Shuran Song
We introduce the Universal Manipulation Policy Network (UMPNet) -- a single image-based policy network that infers closed-loop action sequences for manipulating arbitrary articulat…
Learning 3D Dynamic Scene Representations for Robot Manipulation
Zhenjia Xu, Zhanpeng He, Jiajun Wu +1
3D scene representation for robot manipulation should capture three key object properties: permanency -- objects that become occluded over time continue to exist; amodal completene…
Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning
Tianjian Chen, Zhanpeng He, Matei Ciocarlie
Deep Reinforcement Learning (RL) has shown great success in learning complex control policies for a variety of applications in robotics. However, in most such cases, the hardware o…
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks
Bohan Wu, Feng Xu, Zhanpeng He +2
Recent advances in deep reinforcement learning (RL) have demonstrated its potential to learn complex robotic manipulation tasks. However, RL still requires the robot to collect a l…
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He +7
Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current…
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
Zhanpeng He, Ryan Julian, Eric Heiden +5
Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing…
Scaling simulation-to-real transfer by learning composable robot skills
Ryan Julian, Eric Heiden, Zhanpeng He +5
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simu…