ICRA 2012 Paper Abstract


Paper TuD210.5

Mericli, Cetin (Carnegie Mellon University), Veloso, Manuela (Carnegie Mellon University), Akin, H. Levent (Bogazici University)

Efficient Task Execution and Refinement through Multi-Resolution Corrective Demonstration

Scheduled for presentation during the Interactive Session "Interactive Session TuD-2" (TuD210), Tuesday, May 15, 2012, 17:00−17:30, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on August 20, 2018

Keywords Learning and Adaptive Systems, Autonomous Agents


Computationally efficient task execution is very important for autonomous mobile robots endowed with limited on-board computational capabilities. Most robot control approaches assume fixed state and action representations, and use a single algorithm to map states to actions. However, not all instances of a given task require equally complex algorithms and equally detailed representations. The main motivation for this work is a desire to reduce the computational footprint of performing a task by allowing the robot to run simpler algorithms whenever possible, and resort to more complex algorithms only when needed. We contribute the Multi-Resolution Task Execution (MRTE) algorithm that utilizes human feedback to learn a mapping from a given state to an appropriate detail resolution consisting of a state and action representation, and an algorithm. We then present Model Plus Correction (M+C), an algorithm that complements an existing robot controller with corrective human feedback to further improve the task execution performance. Finally, we introduce Multi-Resolution Model Plus Correction (MRM+C) as a combination of MRTE and M+C. We provide formal definitions of MRTE, M+C, and MRM+C, showing how they relate to general robot control problem and Learning from Demonstration (LfD) methods. We present detailed experimental results demonstrating the effectiveness of proposed methods on a simulated goal-directed humanoid obstacle avoidance task.



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