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 single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robot's capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multi-tasks sequencing with emphasis on handling the so-called 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 information-theoretic 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.

Learning to Sequence Multiple Tasks with Competing Constraints / Duan, A; Camoriano, R; Ferigo, D; Huang, Y; Calandriello, D; Rosasco, L; Pucci, D. - ELETTRONICO. - (2019), pp. 2672-2678. (Intervento presentato al convegno 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 tenutosi a Macau, China nel 03-08 November 2019) [10.1109/IROS40897.2019.8968496].

Learning to Sequence Multiple Tasks with Competing Constraints

Camoriano R;
2019

Abstract

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 single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robot's capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multi-tasks sequencing with emphasis on handling the so-called 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 information-theoretic 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.
2019
978-1-7281-4004-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2982138