MoveAE: Modifying Affective Robot Movements Using Classifying Variational Autoencoders

We propose a method for modifying affective robot movements using neural networks. Social robots use gestures and other movements to express their internal states. However, a robot’s interactive capabilities are hindered by the predominant use of a limited set of preprogrammed or hand-animated behaviors, which can be repetitive and predictable, making sustained human-robot interactions difficult to maintain. To address this, we developed a method for modifying existing emotive robot movements by using neural networks. We use hand-crafted movement samples and a classifying variational autoencoder trained on these samples. Our method then allows for adjustment of affective movement features by using simple arithmetic in the network’s latent embedding space. We present the implementation and evaluation of this approach and show that editing in the latent space can modify the emotive quality of the movements while preserving recognizability and legibility in many cases. This supports neural networks as viable tools for creating and modifying expressive robot behaviors. CCS CONCEPTS • Artificial intelligence→Cognitive robotics; • Machine learning→ neural networks; • Human-centered computing→HCI theory, concepts and models. ACM Reference Format: Michael Suguitan, Randy Gomez, and Guy Hoffman. 2020. MoveAE: Modifying Affective Robot Movements Using Classifying Variational Autoencoders. In Proceedings of the 2020 ACM/IEEE International Conference on HumanRobot Interaction (HRI’20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374807