68 citations · 177 across the 11 of their papers we have counts for
7 papers · 1 filter
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Abhishek Gupta, Vikash Kumar, Corey Lynch +2
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable,…
Deep Dynamics Models for Learning Dexterous Manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine +1
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also not…
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
Michael Ahn, Henry Zhu, Kristian Hartikainen +4
ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement…
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Ofir Nachum, Michael Ahn, Hugo Ponte +2
Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit m…
Dynamics-Aware Unsupervised Discovery of Skills
Archit Sharma, Shixiang Gu, Sergey Levine +2
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms…
Learning Latent Plans from Play
Corey Lynch, Mohi Khansari, Ted Xiao +4
Acquiring a diverse repertoire of general-purpose skills remains an open challenge for robotics. In this work, we propose self-supervising control on top of human teleoperated play…