Efficient task execution and refinement through multi-resolution corrective demonstration

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.

Authors

• Çetin Meriçli
• Manuela Veloso
• H. Levent Akin

Keywords

• Computational modeling
• Task Execution
• Efficient Task Execution
• Autonomic System
• State Representation
• Action Representation
• Robot Control
• Detailed Representation
• Humanoid
• Corrective Feedback
• Automated Guided Vehicles
• Detailed Resolution
• Inverse Reinforcement Learning
• Fixed State
• System State
• Performance Of Algorithm
• Free Space
• Finer Resolution
• Corrective Actions
• Walking Distance
• Definition Of State
• Current Resolution
• Walking Direction
• Obstacle Location
• Human Education
• Executive Skills