1. Ron Alterovitz, Dinesh Manocha, Jennifer Womack Christopher Bowen, Jeff Ichnowski, Jia Pan Departments of Computer Science and Occupational Science and Therapy The University of North Carolina at Chapel Hill Ron Alterovitz, Dinesh Manocha, Jennifer Womack Christopher Bowen, Jeff Ichnowski, Jia Pan Departments of Computer Science and Occupational Science and Therapy The University of North Carolina at Chapel Hill Collect set of kinesthetic demonstrations Record motion features over time: Joint angles θ Coordinates of task-relevant landmarks relative to robot hand and body Time align trajectories using DTW Compute mean x ̄ (t) and covariance matrix Σ(t) of each motion feature Covariances imply task constraints: Low variance ⇒ consistent across demos ⇒ must reproduce in execution High variance ⇒ not important ⇒ can violate to enable collision avoidance Formulate cost metric using covariances (Mahalanobis distance) Collect set of kinesthetic demonstrations Record motion features over time: Joint angles θ Coordinates of task-relevant landmarks relative to robot hand and body Time align trajectories using DTW Compute mean x ̄ (t) and covariance matrix Σ(t) of each motion feature Covariances imply task constraints: Low variance ⇒ consistent across demos ⇒ must reproduce in execution High variance ⇒ not important ⇒ can violate to enable collision avoidance Formulate cost metric using covariances (Mahalanobis distance) x x y y z z Learning from Demonstrations Enable domain experts (e.g. non-programmers) to teach robots new skills Prior methods often fail in environments with new obstacles Motion Planning Compute collision-free robot motions Prior methods require explicit programming of task constraints Our method: Demonstration-Guided Motion Planning (DGMP) Combines robot learning with fast motion planning Learns task constraints from kinesthetic demonstrations Executes learned tasks in cluttered environments Learning from Demonstrations Enable domain experts (e.g. non-programmers) to teach robots new skills Prior methods often fail in environments with new obstacles Motion Planning Compute collision-free robot motions Prior methods require explicit programming of task constraints Our method: Demonstration-Guided Motion Planning (DGMP) Combines robot learning with fast motion planning Learns task constraints from kinesthetic demonstrations Executes learned tasks in cluttered environments Computing Robot Motions for Home Healthcare Assistance Results: Spoon transfer task Results: Wiping Table Motivation: Robot Assistance in the Home Approach: Integrate Robot Learning + Fast Motion Planning Methods: Learning Task Constraints Methods: Fast Robot Motion Planning time z spoon -z cup time y hand -y spoon scoop from bowl drop sugar keep spoon level spoon over cup Use fast sampling-based algorithm to explore the robot’s configuration space and build roadmap of feasible trajectories Integrate with learned task constraints (DGMP) Real-time motion planning for high-DOF robots Optimization-based planning in dynamic environments Handle model uncertainty GPUs and Multi-core CPUs for parallel planning Hierarchical methods for high (20-30) DOF robots Use fast sampling-based algorithm to explore the robot’s configuration space and build roadmap of feasible trajectories Integrate with learned task constraints (DGMP) Real-time motion planning for high-DOF robots Optimization-based planning in dynamic environments Handle model uncertainty GPUs and Multi-core CPUs for parallel planning Hierarchical methods for high (20-30) DOF robots Personal robots have the potential to assist with activities of daily living Enable disabled/elderly individuals to stay in own homes Challenges: Robot must learn to assist with household tasks Must perform tasks autonomously in unstructured, cluttered environments Personal robots have the potential to assist with activities of daily living Enable disabled/elderly individuals to stay in own homes Challenges: Robot must learn to assist with household tasks Must perform tasks autonomously in unstructured, cluttered environments UNC Computational Robotics Lab: http://robotics.cs.unc.edu UNC GAMMA Group: http://gamma.cs.unc.edu/ITOMP/ Research supported by NSF award IIS-1117127 UNC Computational Robotics Lab: http://robotics.cs.unc.edu UNC GAMMA Group: http://gamma.cs.unc.edu/ITOMP/ Research supported by NSF award IIS-1117127 DGMP success rate: ~80% when task-relevant objects and obstacles are randomly placed in the workspace