Imitation Learning with Baxter Robot using Hi-Fives

Ganga Rama Koteswara Rao, P. V. Sgar, Siva Naga Prasad Mannem, C. Prasad, Naresh Cherukuri, Vamsidhar Talasila
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Abstract

The goal of this paper is to successfully learn hi-fives for human-robot interaction. The proposed research work has used the Imitation Learning approach by incorporating Bayesian Interaction Primitives [1]. Through expert-guided demonstrations, the robot has been trained to learn relationships between human and robot trajectories. The research study has demonstrated that, the robot is able to complete the interaction with a human and successfully issue a hi-five. Also, the Bayesian Interaction Primitives are implemented to teach a Baxter Robot to give hi-five through imitation learning. Additionally, the trajectories are compared with human biomechanics data.
使用hi - five的Baxter机器人模仿学习
本文的目标是成功地学习人机交互中的击掌动作。提出的研究工作使用了模仿学习方法,结合贝叶斯交互原语[1]。通过专家指导的演示,机器人已经被训练来学习人类和机器人轨迹之间的关系。研究表明,机器人能够完成与人的互动,并成功地击掌。同时,实现贝叶斯交互原语,通过模仿学习来教Baxter机器人击掌。此外,将这些轨迹与人类生物力学数据进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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