Abstract
This paper proposes an interaction learning method suited for semi-autonomous robots that work with or assist a human partner. The method aims at generating a collaborative trajectory of the robot as a function of the current action of the human. The trajectory generation is based on action recognition and prediction of the human movement given intermittent observations of his/her positions under unknown speeds of execution; a problem typically found when using motion capture systems in occluded scenarios. Of particular interest, the ability to predict the human movement while observing the initial part of the trajectory, allows for faster robot reactions. The method is based on probabilistically modelling the coupling between human-robot movement primitives and eliminates the need of time-alignment of the training data while being scalable in relation to the number of tasks. We evaluated the method using a 7-DoF lightweight robot arm equipped with a 5-finger hand in a multi-task collaborative assembly experiment, also comparing results with our previous method based on time-aligned trajectories.
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Notes
- 1.
The interested reader is referred to our previous works for additional and detailed literature review in respect to their corresponding contributions.
- 2.
Although not used in this paper, the ProMP framework also provides means to compute the feedback controller and the interested reader is referred to [15].
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Acknowledgements
The research leading to these results has received funding from the European Community’s Seventh Framework Programmes (FP7-ICT-2013-10) under grant agreement 610878 (3rdHand) and (FP7-ICT-2009-6) under grant agreement 270327 (ComPLACS); and from the project BIMROB of the Forum für interdisziplinäre Forschung (FiF) of the TU Darmstadt.
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Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J. (2018). A Probabilistic Framework for Semi-autonomous Robots Based on Interaction Primitives with Phase Estimation. In: Bicchi, A., Burgard, W. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-60916-4_15
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