Speaker(s): Nikhil Devraj
Topic: A Brief Overview of Deep RL in Robotics
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world.
This discussion focused predominantly on the following questions:
(1) What is deep RL and how does it relate to robotics?
(2) What are some examples of studies done with Deep RL in robotics?
(3) What are major challenges faced by researchers who apply deep RL to robotics?
This discussion is heavily inspired by Ibarz et al., although it does not dive into that level of detail.
How to Train Your Robot with Deep Reinforcement Learning - Lessons We’ve Learned
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Reinforcement learning in robotics: a survey