Probabilistic Inference in Planning for Partially Observable Long Horizon Problems

TL;DR

This paper presents an online planning and execution method for long horizon tasks in partially observable environments, using belief inference and hybrid constraint satisfaction to improve robot performance in realistic scenarios.

Contribution

It introduces a novel hybrid constraint satisfaction approach for joint inference of action parameters in partially observable planning tasks.

Findings

Outperforms state-of-the-art methods in kitchen simulation tasks.

Efficiently solves long horizon partially observable planning problems.

Successfully updates beliefs and replans during execution.

Abstract

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.