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This paper presents a novel system that enables intelligent robots to exhibitrealistic body gestures while communicating with humans. The proposed systemconsists of a listening model and a speaking model used in correspondingconversational phases. Both models are adapted from the sequence-to-sequence(seq2seq) architecture to synthesize body gestures represented by the movementsof twelve upper-body keypoints. All the extracted 2D keypoints are firstly3D-transformed, then rotated and normalized to discard irrelevant information.Substantial videos of human conversations from Youtube are collected andpreprocessed to train the listening and speaking models separately, after whichthe two models are evaluated using metrics of mean squared error (MSE) andcosine similarity on the test dataset. The tuned system is implemented to drivea virtual avatar as well as Pepper, a physical humanoid robot, to demonstratethe improvement on conversational interaction abilities of our method inpractice.

Towards More Realistic Human-Robot Conversation: A Seq2Seq-based Body Gesture Interaction System