A robot learning from demonstration framework to perform force-based manipulation tasks

Journal Article (2013)


Intelligent Service Robotics







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This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.


intelligent robots, learning (artificial intelligence), robot programming.

Author keywords

pProgramming by demonstration, Imitation learning, haptic perception, mutual information, HMM, GMR, robotic manipulation

Scientific reference

L. Rozo, P. Jiménez and C. Torras. A robot learning from demonstration framework to perform force-based manipulation tasks. Intelligent Service Robotics, 6(1): 33-51, 2013.