Abstract
Robot arm control and motion planning in dynamically changing environments is a challenging task. It requires an adaptive planning algorithm that generates solutions on-the-fly, incorporating the current environmental conditions. This paper explores an alternative approach. Adaptive planning is realized in a generative Recurrent Neural Network (RNN) architecture, which produces goal-directed motor commands by means of active-inference-based, model-predictive control. As the main contribution, in this paper we show how to integrate local collision avoidance gradients into the active inference process. The result is a control mechanism that avoids arm collisions while concurrently pursuing arm goal poses. The RNN processes embodied, sensorimotor dynamics into which proximity signals from locally embedded distance sensors are injected at the respective joint locations. We demonstrate that a 3D trunk-like many-joint robot arm with up to 80 articulated degrees of freedom (DoF) can maneuver collision-free even through very challenging, dynamic obstacle constellations, evading potential collision sources while pursuing goal-directed arm pose and end-effector control.
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Otte, S., Hofmaier, L., Butz, M.V. (2018). Integrative Collision Avoidance Within RNN-Driven Many-Joint Robot Arms. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_73
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DOI: https://doi.org/10.1007/978-3-030-01424-7_73
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