Ron Alterovitz, Dinesh Manocha, Jennifer WomackChristopher Bowen, Jeff Ichnowski, Jia Pan

Departments of Computer Science and Occupational Science and TherapyThe 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 x̄+(t) and covariance matrix̄

Σ(t) of each motion feature•Covariances imply task constraints:

•Low variance ⇒ consistent across demos ⇒ must reproduce in ex̄ecution

•High variance ⇒ not important ⇒ can violate to enable collision avoidance

•Formulate cost metric using covariances (Mahalanobis distance)

x̄y

z

•Learning from Demonstrations•Enable domain ex̄perts

(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 ex̄plicit

programming of task constraints•Our method: Demonstration-Guided Motion

Planning (DGMP)•Combines robot learning with fast motion

planning•Learns task constraints from kinesthetic

demonstrations•Ex̄ecutes 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

zspoon

-zcup

time

yhand

-yspoon

scoop from bowl

drop sugar

keep spoon level

spoon over cup

•Use fast sampling-based algorithm to ex̄plore 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

UNC Computational Robotics Lab: http://robotics.cs.unc.eduUNC 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