Task-aware variations in robot motion

Michael J. Gielniak, C. Karen Liu, Andrea L. Thomaz
2011 2011 IEEE International Conference on Robotics and Automation  
Social robots can benefit from motion variance because non-repetitive gestures will be more natural and intuitive for human partners. We introduce a new approach for synthesizing variance, both with and without constraints, using a stochastic process. Based on optimal control theory and operational space control, our method can generate an infinite number of variations in real-time that resemble the kinematic and dynamic characteristics from the single input motion sequence. We also introduce a
more » ... stochastic method to generate smooth but nondeterministic transitions between arbitrary motion variants. Furthermore, we quantitatively evaluate taskaware variance against random white torque noise, operational space control, style-based inverse kinematics, and retargeted human motion to prove that task-aware variance generates human-like motion. Finally, we demonstrate the ability of task-aware variance to maintain velocity and time-dependent features that exist in the input motion.
doi:10.1109/icra.2011.5980348 dblp:conf/icra/GielniakLT11 fatcat:rhcoojb6sfgslmj4haonvhucsy