Probabilistic Inference in Planning for Partially Observable Long Horizon Problems
This addresses the challenge of partial observability in robotics for service tasks, representing an incremental improvement over existing methods.
The paper tackles the problem of autonomous service robots performing long horizon tasks in partially observable environments by proposing an online planning and execution approach that grounds symbolic actions using a Hybrid Constraint Satisfaction Problem solved with Belief Propagation, achieving efficient performance in a realistic kitchen simulation and outperforming a state-of-the-art method adaptation.
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.