Dexterous manipulation with reinforcement learning: Efficient, general, and low-cost

In this post, we demonstrate how deep reinforcement learning (deep RL) can be used to learn how to control dexterous hands for a variety of manipulation tasks. We discuss how such methods can learn to make use of low-cost hardware, can be implemented efficiently, and…

Towards a virtual stuntman

Motion control problems have become standard benchmarks for reinforcement learning, and deep RL methods have been shown to be effective for a diverse suite of tasks ranging from manipulation to locomotion. However, characters trained with deep RL often exhibit unnatural behaviours, bearing artifacts such as…