部分観測下の潜在信念空間計画のための微分動的計画法
PODDP: Partially Observable Differential Dynamic Programming for Latent Belief Space Planning
離散潜在状態の不確かさを持つPOMDPに対し、観測の木構造上で条件付き計画を最適化する効率的なDDPアルゴリズムを提案し、階層版も導出した。自動車線変更タスクで頑健な対話的軌道生成が可能なことを示した。
著者: Dicong Qiu, Yibiao Zhao, Chris L. Baker
分類: cs.RO, cs.AI
原文アブストラクト
Autonomous agents are limited in their ability to observe the world state. Partially observable Markov decision processes (POMDPs) formally model the problem of planning under world state uncertainty, but POMDPs with continuous actions and nonlinear dynamics suitable for robotics applications are challenging to solve. In this paper, we present an efficient differential dynamic programming (DDP) algorithm for belief space planning in POMDPs with uncertainty over a discrete latent state, and continuous states, actions, observations, and nonlinear dynamics. This representation allows planning of dynamic trajectories which are sensitive to structured uncertainty over discrete latent world states. We develop dynamic programming techniques to optimize a contingency plan over a tree of possible observations and belief space trajectories, and also derive a hierarchical version of the algorithm. Our method is applicable to problems with uncertainty over the cost or reward function (e.g., the configuration of goals or obstacles), uncertainty over the dynamics (e.g., the dynamical mode of a hybrid system), and uncertainty about interactions, where other agents' behavior is conditioned on latent intentions. Benchmarks show that our algorithm outperforms popular heuristic approaches to planning under uncertainty, and results from an autonomous lane changing task demonstrate that our algorithm can synthesize robust interactive trajectories.
