565 citations · 4.4k across the 119 of their papers we have counts for
9 papers · 1 filter
Rearrangement: A Challenge for Embodied AI
Dhruv Batra, Angel X. Chang, Sonia Chernova +9
We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techn…
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Ben Eysenbach +3
In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast reinforcement learning (RL) strategies that transfer to similar tasks. Howe…
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
The history of learning for control has been an exciting back and forth between two broad classes of algorithms: planning and reinforcement learning. Planning algorithms effectivel…
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
Henry Zhu, Abhishek Gupta, Aravind Rajeswaran +2
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators. However, such h…
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee +1
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by…
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
Łukasz Kidziński, Sharada P. Mohanty, Carmichael Ong +5
Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulat…