72 citations · 131 across the 9 of their papers we have counts for
7 papers · 1 filter
Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding
Grace Zhang, Linghan Zhong, Youngwoon Lee +1
The ability to transfer a policy from one environment to another is a promising avenue for efficient robot learning in realistic settings where task supervision is not available. T…
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Jun Yamada, Youngwoon Lee, Gautam Salhotra +5
Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in envi…
To Follow or not to Follow: Selective Imitation Learning from Observations
Youngwoon Lee, Edward S. Hu, Zhengyu Yang +1
Learning from demonstrations is a useful way to transfer a skill from one agent to another. While most imitation learning methods aim to mimic an expert skill by following the demo…
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
Youngwoon Lee, Edward S. Hu, Zhengyu Yang +2
The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks. The environment is designed to adv…
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…
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
Hejia Zhang, Eric Heiden, Stefanos Nikolaidis +2
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives…