RI: Medium: Collaborative Research: Experience-Based Planning: A Framework for Lifelong Planning

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Robots need to improve their behavior over time, yet produce consistent behavior in order to allow humans to predict their actions, which is necessary to develop trust in their behavior or even cooperate with them. Furthermore, many tasks repeat, such as opening drawers. This project develops technology that addresses these issues by viewing planning as a lifelong process and exploiting the structure of human environments for efficiency, for example that drawers typically open in similar ways. This research collaboration is developing a framework for lifelong planning based on experience graphs that aims to improve performance of planning over time by exploiting past experiences when solving similar planning tasks. The concept is novel because experiences are used to guide the heuristic search as opposed to be used for mere replay or adaptation. The idea that makes this possible is a novel heuristic search-based framework that can take advantage of prior experiences and still provide rigorous guarantees on completeness and path quality. The team studies how experiences can be utilized effectively during planning, how planning should gather experiences, how it should prune redundant experiences and how it can obtain experiences from demonstrations. Applications include everyday household tasks and low-volume manufacturing tasks. The software developed in this collaborative research is being integrated into the SBPL library, one of the core libraries in ROS. The project also incorporates educational activities as well as activities that help to bridge the research communities in robotics and artificial intelligence, two separate communities despite their common interest in autonomous systems.