Multilateral demonstrations can be difficult for human supervisors to proved because they require divided attention. We propose Bilateral Iterated Best Response (BIBR), a new algorithm that reduces supervisor burden by iteratively demonstrating each manipulator unilaterally while rolling out an estimated robot policy for the other manipulator. We present a web-based user study of a two-agent gridworld domain. We confirm prior work that bilateral demonstrations are noisier and longer when the task is asymmetric, and show that BIBR improves success rate in the asymmetric task, while learning policies that have shorter and smoother trajectories.
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.
* Equal contribution