Title: Simultaneous active parameter estimation and control using sampling-based Bayesian reinforcement learning,
Abstract: Robots performing manipulation tasks must operate under uncertainty about
both their pose and the dynamics of the system. In order to remain robust to
modeling error and shifts in payload dynamics, agents must simultaneously
perform estimation and control tasks. However, the optimal estimation actions
are often not the optimal actions for accomplishing the control tasks, and thus
agents trade between exploration and exploitation. This work frames the problem
as a Bayes-adaptive Markov decision process and solves it online using Monte
Carlo tree search and an extended Kalman filter to handle Gaussian process
noise and parameter uncertainty in a continuous space. MCTS selects control
actions to reduce model uncertainty and reach the goal state nearly optimally.
Certainty equivalent model predictive control is used as a benchmark to compare
performance in simulations with varying process noise and parameter
uncertainty.