Towards More Realistic HumanRobot Conversation: A Seq2Seqbased Body Gesture Interaction System This paper presents a novel system that enables intelligent robots to exhibit realistic body gestures while communicating with humans. The proposed system consists of a listening model and a speaking model used in corresponding conversational phases. Both models are adapted from the sequencetosequence (seq2seq) architecture to synthesize body gestures represented by the movements of twelve upperbody keypoints. All the extracted 2D keypoints are firstly 3Dtransformed, then rotated and normalized to discard irrelevant information. Substantial videos of human conversations from Youtube are collected and preprocessed to train the listening and speaking models separately, after which the two models are evaluated using metrics of mean squared error (MSE) and cosine similarity on the test dataset. The tuned system is implemented to drive a virtual avatar as well as Pepper, a physical humanoid robot, to demonstrate the improvement on conversational interaction abilities of our method in practice.

TeachMe: Threephase learning framework for robotic motion imitation based on interactive teaching and reinforcement learning Motion imitation is a fundamental communication skill for a robot; especially, as a nonverbal interaction with a human. Owing to kinematic configuration differences between the human and the robot, it is challenging to determine the appropriate mapping between the two pose domains. Moreover, technical limitations while extracting 3D motion details, such as wrist joint movements from human motion videos, results in significant challenges in motion retargeting. Explicit mapping over different motion domains indicates a considerably inefficient solution. To solve these problems, we propose a threephase reinforcement learning scheme to enable a NAO robot to learn motions from human pose skeletons extracted from video inputs. Our learning scheme consists of three phases: (i) phase one for learning preparation, (ii) phase two for a simulationbased reinforcement learning, and (iii) phase three for a humanintheloopbased reinforcement learning. In phase one, embeddings of the motions of a human skeleton and robot are learned by an autoencoder. In phase two, the NAO robot learns a rough imitation skill using reinforcement learning that translates the learned embeddings. In the last phase, the robot learns motion details that were not considered in the previous phases by interactively setting rewards based on direct teaching instead of the method used in the previous phase. Especially, it is to be noted that a relatively smaller number of interactive inputs are required for motion details in phase three when compared to the large volume of training sets required for overall imitation in phase two. The experimental results demonstrate that the proposed method improves the imitation skills efficiently for hand waving and saluting motions obtained from NTUDB.