Integrating answer set programming and POMDPs for knowledge representation and reasoning in robotics

Mobile robots equipped with multiple sensors and deployed in real-world domains frequently find it difficult to efficiently process all sensor inputs, operate without any human input and have possibly-relevant domain knowledge in advance. At the same time, robots cannot be equipped with all relevant domain knowledge in advance, and humans are unlikely to have the time and expertise to provide elaborate and accurate feedback.

This dissertation presents a novel architecture for knowledge representation and reasoning in robotics. These challenges are addressed by integrating high-level logical inference with low-level probabilistic sequential decision-making. Answer Set Programming (ASP), a non-monotonic logic programming paradigm, is used to represent, reason with and revise domain knowledge obtained from sensor inputs and high-level human feedback. In parallel, a novel hierarchical decomposition of partially observable Markov decision processes (POMDPs) uses adaptive observation functions, constrained convolutional policies and automatic belief propagation to automatically adapt visual sensing and information processing to the task at hand. This POMDP hierarchy serves as the first key contribution of this dissertation.

The second key contribution is the merging strategy of ASP-based logical inference with POMDP-based probabilistic belief. This dissertation presents a principled generation of prior beliefs from the knowledge base represented by ASP and the prior beliefs are then merged with POMDP beliefs using Bayesian updates to adapt sensing and acting to the tasks at hand. In addition, the entropy of belief states is used to determine the need for human feedback and hence robots ask questions only when needed. At last, robots are enabled to learn from positive and negative observations to identify the situations where the current task should no longer be pursed.

As a result, mobile robots are able to represent and reason with domain knowledge, retain capabilities for many different tasks, direct sensing to relevant locations and determine the sequence of sensing and processing algorithms best suited to any given task, using human feedback based on need and availability. Furthermore, the architecture is augmented with a communication layer to enable belief sharing and collaboration in a team of robots. All algorithms are evaluated in simulation and on physical robots localizing target objects in indoor domains.