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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Passive motion paradigm: an alternative to optimal control.

Vishwanathan Mohan¹, Pietro Morasso

¹Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia Genoa, Italy.

Frontiers in Neurorobotics

|December 31, 2011

Summary

Optimal Control Theory (OCT) is widely used in neuroscience and robotics. A new Passive Motion Paradigm (PMP) offers a softer, more biomechanically grounded approach to understanding motor control and cognition.

Keywords:

cognitive architecture covert actions humanoid robots iCub optimal control theory passive motion paradigm synergy formation

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Published on: September 11, 2019

Area of Science:

Neuroscience and Robotics
Computational Neuroscience
Motor Control

Background:

Optimal Control Theory (OCT) is a leading framework for studying neural control of movement and motor cognition in behavioral neuroscience and humanoid robotics.
OCT addresses challenges like the "degrees of freedom (DoFs) problem" by quantifying goals as "cost functions" and using engineering control techniques.
Existing approaches may not fully capture the nuances of biological systems.

Purpose of the Study:

To introduce and elaborate on the Passive Motion Paradigm (PMP) as an alternative to OCT for understanding action and motor cognition.
To present PMP as a computational theory grounded in biomechanics and cybernetics.
To explore PMP's potential for designing advanced cognitive architectures.

Main Methods:

Proposing the Passive Motion Paradigm (PMP) as a computational framework.
Describing PMP's core idea: actions as internal simulations animating the body schema with force field dynamics.
Highlighting PMP's ability to link motor redundancy with task constraints dynamically, bypassing explicit kinematic inversion and cost function computation.

Main Results:

PMP offers a "softer" approach, aligning better with biomechanics and cybernetics of action.
Internal simulation within PMP dynamically links motor redundancy and task constraints, solving the DoFs problem without explicit computation.
PMP provides insights into action feasibility, consequences, understanding, and meaning, extending beyond motor output shaping.

Conclusions:

The Passive Motion Paradigm (PMP) presents a novel framework for motor cognition, integrating insights from neuroscience and embodied robotics.
PMP offers a more biologically plausible alternative to engineering-centric Optimal Control Theory (OCT).
PMP provides a foundation for developing more sophisticated cognitive architectures in robotics and artificial intelligence.