Graph-based trajectory planning through programming by demonstration

As robots are utilized in a growing number of applications, the ability to teach them to perform tasks safely and accurately becomes ever more critical. Programming by demonstration offers an expressive means for teaching while being accessible to domain experts who may be novices in robotics. This work investigates a programming by demon- stration approach to learning motion trajectories for robotic manipulator tasks. Using a graph constructed to determine correspondences between multiple imperfect demonstrations, the robot learner plans novel trajectories that safely and smoothly generalize the teacher's behavior, while attenuating those imperfections. The learner also actively detects instances of diverging strategy between examples, requesting advice for resolving these ambiguities. We demonstrate our approach in example domains with a 7 degree-of-freedom manipulator.

Authors

• Nik A. Melchior
• Reid G. Simmons

Keywords

• Trajectory
• Planning
• Bifurcation
• Interpolation
• Programming
• Robot sensing systems
• Smoothing
• Domain Experts
• Robot Manipulator
• Robot Learning
• Dimensionality Reduction
• Active Learning
• Essential Features
• Horizontal Axis
• User Study
• Workspace
• Latent Space
• Path Planning
• Low-dimensional Space
• Geometric Constraints
• Neighboring Points
• Homotopy
• Multiple Examples
• Geodesic Distance
• Robot Model
• Mean Curvature
• Continuous Deformation
• Signed Distance Function
• Graph Metrics
• Physical Obstacles
• Plan Quality
• Regions Of Space