163 citations · 420 across the 27 of their papers we have counts for
5 papers · 1 filter
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization
Youngwoon Lee, Joseph J. Lim, Anima Anandkumar +1
Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a polic…
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme +3
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual…
Learning to Synthesize Programs as Interpretable and Generalizable Policies
Dweep Trivedi, Jesse Zhang, Shao-Hua Sun +1
Recently, deep reinforcement learning (DRL) methods have achieved impressive performance on tasks in a variety of domains. However, neural network policies produced with DRL method…
Demonstration-Guided Reinforcement Learning with Learned Skills
Karl Pertsch, Youngwoon Lee, Yue Wu +1
Demonstration-guided reinforcement learning (RL) is a promising approach for learning complex behaviors by leveraging both reward feedback and a set of target task demonstrations.…
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…