部分観測長期間タスクのための確率的推論に基づく計画手法
Probabilistic Inference in Planning for Partially Observable Long Horizon Problems
部分観測環境での長期間タスク遂行のため、記号行動を連続パラメータに接地する推論をハイブリッド制約満足問題として定式化し、信念伝播で解くオンライン計画・実行手法を提案した。
著者: Alphonsus Adu-Bredu, Nikhil Devraj, Pin-Han Lin, Zhen Zeng, Odest Chadwicke Jenkins
分類: cs.RO
原文アブストラクト
For autonomous service robots to successfully perform long horizon tasks in the real world, they must act intelligently in partially observable environments. Most Task and Motion Planning approaches assume full observability of their state space, making them ineffective in stochastic and partially observable domains that reflect the uncertainties in the real world. We propose an online planning and execution approach for performing long horizon tasks in partially observable domains. Given the robot's belief and a plan skeleton composed of symbolic actions, our approach grounds each symbolic action by inferring continuous action parameters needed to execute the plan successfully. To achieve this, we formulate the problem of joint inference of action parameters as a Hybrid Constraint Satisfaction Problem (H-CSP) and solve the H-CSP using Belief Propagation. The robot executes the resulting parameterized actions, updates its belief of the world and replans when necessary. Our approach is able to efficiently solve partially observable tasks in a realistic kitchen simulation environment. Our approach outperformed an adaptation of the state-of-the-art method across our experiments.
