68 citations · 177 across the 11 of their papers we have counts for
4 papers · 1 filter
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Archit Sharma, Michael Ahn, Sergey Levine +3
Reinforcement learning provides a general framework for learning robotic skills while minimizing engineering effort. However, most reinforcement learning algorithms assume that a w…
The Ingredients of Real-World Robotic Reinforcement Learning
Henry Zhu, Justin Yu, Abhishek Gupta +5
The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversig…
A Game Theoretic Framework for Model Based Reinforcement Learning
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data. However, desig…
Benchmarking In-Hand Manipulation
Silvia Cruciani, Balakumar Sundaralingam, Kaiyu Hang +3
The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the system's ability to change the pose…