One of the key challenges in building robots for household or industrial settings is the need to master the control of high-degree-of-freedom systems such as mobile manipulators. Reinforcement learning has been a promising avenue for acquiring robot control policies, however, scaling to complex systems has proved tricky. In their work SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL, Jiaheng Hu, Peter Stone and Roberto Martín-Martín introduce a method that renders real-world reinforcement learning feasible for complex embodiments. We caught up with Jiaheng to find out more.
What is the topic of the research in your paper and why is it an interesting area for study?
This paper is about how robots (in particular, household robots like mobile manipulators) can autonomously acquire skills via interacting with the physical world (i.e. real-world reinforcement learning). Reinforcement learning (RL) is a general learning framework for learning from trial-and-error interaction with an environment, and has huge potential in allowing robots to learn tasks without humans hand-engineering the solution. RL for robotics is a very exciting field, as it can open possibilities for robots to self-improve in a scalable way, towards the creation of general-purpose household robots that can assist people in our everyday lives.
