Learning to Sequence Multiple Tasks with Competing Constraints Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on singlestroke movements, without taking into account the problem of multitasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robotxe2x80x99s capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multitasks sequencing with emphasis on handling the socalled competing constraints, which emerge due to the existence of the concurrent constraints from Cartesian and joint trajectories. Specifically, we explore the null space of the robot from an informationtheoretic perspective in order to maintain imitation fidelity during transition between consecutive tasks. The effectiveness of the proposed method is validated through simulated and real experiments on the iCub humanoid robot.

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.