Name
Title | ||
|---|---|---|
Learning, Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction |
Abstract | ||
|---|---|---|
This study proposes a novel imitation learning approach for the stochastic generation of human-like rhythmic wave gestures and their modulation for effective non-verbal communication through a probabilistic formulation using joint angle data from human demonstrations. This is achieved by learning and modulating the overall expression characteristics of the gesture (e.g., arm posture, waving frequency and amplitude) in the frequency domain. The method was evaluated on simulated robot experiments involving a robot with a manipulator of 6 degrees of freedom. The results show that the method provides efficient encoding and modulation of rhythmic movements and ensures variability in their execution.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3371382.3378286 | HRI '20: ACM/IEEE International Conference on Human-Robot Interaction
Cambridge
United Kingdom
March, 2020 |
Keywords | DocType | ISSN |
imitation learning, movement primitives, social robots | Conference | 2167-2121 |
ISBN | Citations | PageRank |
978-1-4503-7057-8 | 0 | 0.34 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michail Panteris | 1 | 0 | 0.34 |
| Simon Manschitz | 2 | 0 | 0.34 |
| Sylvain Calinon | 3 | 1897 | 117.63 |