Robot programming from demonstration, feedback and transfer

This paper presents a novel approach for robot instruction for assembly tasks. We consider that robot programming can be made more efficient, precise and intuitive if we leverage the advantages of complementary approaches such as learning from demonstration, learning from feedback and knowledge transfer. Starting from low-level demonstrations of assembly tasks, the system is able to extract a high-level relational plan of the task. A graphical user interface (GUI) allows then the user to iteratively correct the acquired knowledge by refining high-level plans, and low-level geometrical knowledge of the task. This combination leads to a faster programming phase, more precise than just demonstrations, and more intuitive than just through a GUI. A final process allows to reuse high-level task knowledge for similar tasks in a transfer learning fashion. Finally we present a user study illustrating the advantages of this approach.

Authors

• Yoan Mollard
• Thibaut Munzer
• Andrea Baisero
• Marc Toussaint
• Manuel Lopes 0001

Keywords

• Assembly
• Graphical user interfaces
• Service robots
• Computational modeling
• Context
• Training
• Robot Programming
• Knowledge Transfer
• User Study
• Graphical User Interface
• High-level Tasks
• Learning Process
• Latent Variables
• Learning Task
• Sequence Of Actions
• Sequential Steps
• Robotic System
• Path Planning
• Knowledge Creation
• Markov Decision Process
• 3D Simulation
• Conditional Random Field
• Object Pairs
• Real Robot
• Task Representations
• Inverse Reinforcement Learning
• Cold Start
• Rigid Constraints
• Precise Planning
• Execution Steps
• Median Time
• Geometric Constraints
• Prismatic
• Degrees Of Freedom