Conference Proceedings

An Algorithm and User Study for Teaching Bilateral Manipulation via Iterated Best Response Demonstrations

Bilateral Iterated Best Response (BIBR) reduces supervisor burden when training multiple manipulators, by rolling out an estimated robot policy for one arm while the human demonstrates for the other, iteratively updating the estimated policy. BIBR learns policies with increased success rate, and shorter and smoother trajectories.

Carolyn Chen, Sanjay Krishnan, Michael Laskey, Roy Fox, and Ken Goldberg, CASE 2017

A Multi-Agent Control Framework for Co-Adaptation in Brain-Computer Interfaces

We model the process by which the brain and the computer in a brain-computer interface (BCI) co-adapt to one another. We show in this simplified Linear-Quadratic-Gaussian (LQG) model how the brain’s neural encoding can adapt to the task of controlling the computer, at the same time that the computer’s adaptive decoder can adapt to the task of estimating the intention signal, leading to improvement in the system’s performance. We then propose an encoder-aware decoder adaptation scheme, which allows the computer to drive improvement forward faster by anticipating the brain’s adaptation.

Josh Merel*, Roy Fox*, Tony Jebara, and Liam Paninski, NIPS 2013
* Equal contribution