Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Real-time hierarchical POMDPs for autonomous robot navigation
Robotics and Autonomous Systems
A Compliant Hybrid Zero Dynamics Controller for Stable, Efficient and Fast Bipedal Walking on MABEL
International Journal of Robotics Research
PEGASUS: a policy search method for large MDPs and POMDPs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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We propose a methodology of applying PoMDPs at a sufficiently high abstraction of a high-dimensional continuous-time partially observable hybrid system. In particular, we develop a two-layer hybrid controller, where the higher-level PoMDP-based hybrid controller learns the boundaries between various modes and appropriately switches between them. The modes partition the state-space and represent a closed-loop hybrid system with a lower-level hybrid controller. We apply this methodology onto the problem of bipedal walking on varying terrain, where the gradient change in the terrain is only partially observable (due to poor and noisy sensors.) We develop three lower-level hybrid controllers that result in robust walking on level ground, up and down ramps. The higher-level PoMDP-based hybrid controller then learns the boundary between these controllers and is used to perform appropriate controller switching. With only a coarse, discrete estimate of walking speed, the controller enables traversing terrain both with long sustained constant slopes, and with rapid changes in slope. Simulation results are presented on a 26-dimensional planar bipedal robot model that incorporates contact forces and friction.