Search Results for author: Michael Ahn

Found 6 papers, 3 papers with code

Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

2 code implementations27 Apr 2020 Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu

Can we instead develop efficient reinforcement learning methods that acquire diverse skills without any reward function, and then repurpose these skills for downstream tasks?

Model Predictive Control reinforcement-learning +2

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

1 code implementation25 Sep 2019 Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar

ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D'Kitty is a four-legged robot that facilitates learning agile legged locomotion tasks.

Continuous Control reinforcement-learning +1

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

no code implementations13 Aug 2019 Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, Vikash Kumar

Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation.

Reinforcement Learning (RL)

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