Reaching to grasp is a fundamental human action. In this thesis, I present a new computational platform for simulating reach-to-grasp actions and exploring how they are learned and performed. There are three innovations in the platform. Firstly, many existing platforms for simulating reach-to-grasp actions hard code a definition of ;;a stable grasp;;. My aim was to create a platform based on an existing physics engine (the JMonkey game engine), so that the stable grasp is represented using general-purpose definitions of force and friction. Secondly, the platform implements a new model of the soft finger pads of the human hand, and of the finger;;s tactile mechanoreceptors. Each finger pad is modelled as a lattice of solid objects connected by springs to create a deformable surface. This allows a good simulation of the mechanoreceptors which respond to local finger pad deformations, and to other kinds of tactile stimulus. Finally, the platform supports a novel model of the motor controller responsible for generating reach-to-grasp actions. Most existing computational models assume that the hand;;s trajectory to the target is precomputed in advance, but there is evidence that this does not happen in the primate reach/grasp neural pathway. The motor controller in my system is a combination of a low-level feedback controller which tries to move the hand and arm into a learned goal state, and a high-level controller which perturbs or moves this goal state to create a virtual target location for the low level controller to reach towards. Combining these controllers allows for complex actions to be learned, without precomputing trajectories.
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A computational platform for simulating reach-to-grasp actions: modelling physics, touch receptors and motor control mechanisms
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