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Paper WeP201.4

Gielniak, Michael Joseph (Georgia Institute of Technology), Liu, Karen (Georgia Tech), Thomaz, Andrea Lockerd (Georgia Institute of Technology)

Task-Aware Variations in Robot Motion

Scheduled for presentation during the Regular Sessions "Cognitive Human-Robot Interaction" (WeP201), Wednesday, May 11, 2011, 16:10−16:25, Room 3B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 10, 2019

Keywords Robot Companions and Social Human-Robot Interaction, Gesture, Posture and Facial Expressions

Abstract

Social robots can benefit from motion variance because non-repetitive gestures will be more natural and intuitive for the human partner. We introduce a new approach for synthesizing variance in both the presence and absence of 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 stochastic method to generate smooth but nondeterministic transitions between arbitrary motion variants. Furthermore, we quantitatively evaluate task-aware 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 of the task that exist in the input motion. Our method provides the following advantages. (1) Real-time. (2) Single-exemplar. (3) Generic to arbitrary motion without parameter tuning. (4) Human-like variants. (5) Handles velocity and time-dependent task features.

 

 

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