TY - JOUR AU - Mueller, Carl L. PY - 2020/04/03 Y2 - 2024/03/28 TI - Abstract Constraints for Safe and Robust Robot Learning from Demonstration JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 10 SE - Doctoral Consortium Track DO - 10.1609/aaai.v34i10.7136 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7136 SP - 13728-13729 AB - <p>My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called <em>Concept Constrained Learning from Demonstration</em>. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.</p> ER -