The organization of behavior: A neuropsychological theory

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

Also referred to as learning by imitation, tutelage, or apprenticeship learning, Programming by Demonstration (PbD) develops methods by which new skills can be transmitted to a robot. This book examines methods by which robots learn new skills through human guidance. Taking a practical perspective, it covers a broad range of applications, including service robots. The text addresses the challenges involved in investigating methods by which PbD is used to provide robots with a generic and adaptive model of control. Drawing on findings from robot control, human-robot interaction, applied machine learning, artificial intelligence, and developmental and cognitive psychology, the book contains a large set of didactic and illustrative examples. Practical and comprehensive machine learning source codes are available on the books companion website: http://www.programming-by-demonstration.org

Robot Programming by Demonstration

When natural scenes are viewed, a multitude of objects that are stable in their environments are brought in and out of view by eye movements. The posterior parietal cortex is crucial for the analysis of space, visual attention and movement 1 . Neurons in one of its subdivisions, the lateral intraparietal area (LIP), have visual responses to stimuli appearing abruptly at particular retinal locations (their receptive fields)2. We have tested the responses of LIP neurons to stimuli that entered their receptive field by saccades. Neurons had little or no response to stimuli brought into their receptive field by saccades, unless the stimuli were behaviourally significant. We established behavioural significance in two ways: either by making a stable stimulus task-relevant, or by taking advantage of the attentional attraction of an abruptly appearing stimulus. Our results show that under ordinary circumstances the entire visual world is only weakly represented in LIP. The visual representation in LIP is sparse, with only the most salient or behaviourally relevant objects being strongly represented.

The representation of visual salience in monkey parietal cortex

This paper presents a method to learn discrete robot motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e., time-invariant) dynamical system (DS) and define sufficient conditions to ensure global asymptotic stability at the target. We propose a learning method, which is called Stable Estimator of Dynamical Systems (SEDS), to learn the parameters of the DS to ensure that all motions closely follow the demonstrations while ultimately reaching and stopping at the target. Time-invariance and global asymptotic stability at the target ensures that the system can respond immediately and appropriately to perturbations that are encountered during the motion. The method is evaluated through a set of robot experiments and on a library of human handwriting motions.

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Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models

We presented and evaluated an approach based on HMM, GMR, and dynamical systems to allow robots to acquire new skills by imitation. Using HMM allowed us to get rid of the explicit time dependency that was considered in our previous work [12], by encapsulating precedence information within the statistical representation. In the context of separated learning and reproduction processes, this novel formulation was systematically evaluated with respect to our previous approach, LWR [20], LWPR [21], and DMPs [13]. We finally presented applications on different kinds of robots to highlight the flexibility of the proposed approach in three different learning by imitation scenarios.

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Learning and Reproduction of Gestures by Imitation

We show that complex visual tasks, such as position- and size-invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while they freely interact with their environments. We describe the application of this methodology in three sets of experiments, namely, shape discrimination, car driving, and robot navigation. We show that these systems develop sensitivity to a number of oriented, retinotopic, visual-feature-oriented edges, corners, height, and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system, resembling strategies observed in simple insects.

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Coevolution of active vision and feature selection

We present an algorithm enabling a humanoid robot to visually learn its body schema, knowing only the number of degrees of freedom in each limb. By "body schema" we mean the joint positions and orientations and thus the kinematic function. The learning is performed by visually observing its end-effectors when moving them. With simulations involving a body schema of more than 20 degrees of freedom, results show that the system is scalable to a high number of degrees of freedom. Real robot experiments confirm the practicality of our approach. Our results illustrate how subjective space representation can develop as a result of sensorimotor contingencies.

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Online learning of the body schema

http://users.eecs.northwestern.edu/~argall/docs/12ras_sauser.pdf

Eric L. Sauser

Papers

Learning and Reproduction of Gestures by Imitation

Coevolution of active vision and feature selection

Online learning of the body schema

Iterative learning of grasp adaptation through human corrections

2006 Special issue: Parallel and distributed neural models of the ideomotor principle: An investigation of imitative cortical pathways