Journal Title Title of Journal: Auton Robot Search In Journal Title: Abbravation: Autonomous Robots Search In Journal Abbravation: Publisher Springer US Search In Publisher: DOI 10.1002/adsc.201200538 Search In DOI: ISSN 1573-7527 Search In ISSN:
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# Towards a learnt neural body schema for dexterous coordination of action in humanoid and industrial robots

## Abstract

During any goal oriented behavior the dual processes of generation of dexterous actions and anticipation of the consequences of potential actions must seamlessly alternate. This article presents a unified neural framework for generation and forward simulation of goal directed actions and validates the architecture through diverse experiments on humanoid and industrial robots. The basic idea is that actions are consequences of an simulation process that animates the internal model of the body (namely the body schema), in the context of intended goals/constraints. Specific focus is on (a) Learning: how the internal model of the body can be acquired by any robotic embodiment and extended to coordinated tools; (b) Configurability: how diverse forward/inverse models of action can be ‘composed’ at runtime by coupling/decoupling different body (body $$+$$ tool) chains with task relevant goals and constraints represented as multi-referential force fields; and (c) Computational simplicity: how both the synthesis of motor commands to coordinate highly redundant systems and the ensuing forward simulations are realized through well-posed computations without kinematic inversions. The performance of the neural architecture is demonstrated through a range of motor tasks on a 53-DoFs robot iCub and two industrial robots performing real world assembly with emphasis on dexterity, accuracy, speed, obstacle avoidance, multiple task-specific constraints, task-based configurability. Putting into context other ideas in motor control like the Equilibrium Point Hypothesis, Optimal Control, Active Inference and emerging studies from neuroscience, the relevance of the proposed framework is also discussed.To perform any reaching movement, several joints—shoulder, elbow, wrist, fingers move cooperatively forming a synergy in a flexible and dynamic fashion. While groups of fingers may operate synergistically while playing a guitar chord, individual fingers are controlled while playing a lead. One of the basic problems of motor control is to understand how neural control structures quickly and flexibly organize and engage different parts of the body schema to cooperate synergistically in a movement sequence. The above TBG can be used to dynamically couple and decouple synergies in different ways based on task specification. In sum, by selecting two parameters of the TBG ($$t_f$$ and $$\beta$$), a family of time-varying signals can be generated. From the point of view of real-time implementation, it is possible to use any scalar function of time satisfying the properties of described above or a look-up table etc.

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