204 citations · 1k across the 49 of their papers we have counts for
14 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…
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factor…
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation
Josiah Wong, Viktor Makoviychuk, Anima Anandkumar +1
Learning performant robot manipulation policies can be challenging due to high-dimensional continuous actions and complex physics-based dynamics. This can be alleviated through int…
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks
Soroush Nasiriany, Huihan Liu, Yuke Zhu
Realistic manipulation tasks require a robot to interact with an environment with a prolonged sequence of motor actions. While deep reinforcement learning methods have recently eme…
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong +7
Imitating human demonstrations is a promising approach to endow robots with various manipulation capabilities. While recent advances have been made in imitation learning and batch…
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…