Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

https://web.cs.wpi.edu/~rail/files/ArgallEtAlRAS09.pdf

A survey of robot learning from demonstration

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

/pdf/reinforcement-learning-in-robotics-a-survey-4w4397qx9i.pdf

Reinforcement learning in robotics: A survey

Mr M, a patient with semantic dementia - a neurodegenerative disease that is characterized by the gradual deterioration of semantic memory - was being driven through the countryside to visit a friend and was able to remind his wife where to turn along the not-recently-travelled route. Then, pointing at the sheep in the field, he asked her "What are those things?" Prior to the onset of symptoms in his late 40s, this man had normal semantic memory. What has gone wrong in his brain to produce this dramatic and selective erosion of conceptual knowledge?

Where do you know what you know? The representation of semantic knowledge in the human brain.

http://vpnl.stanford.edu/papers/kgs_etal_TICS.06.pdf

Repetition and the brain: neural models of stimulus-specific effects

http://users.eecs.northwestern.edu/~argall/docs/03neuron_beauchamp.pdf

Integration of auditory and visual information about objects in superior temporal sulcus.

Although early sensory cortex is organized along dimensions encoded by receptor organs, little is known about the organization of higher areas in which different modalities are integrated. We investigated multisensory integration in human superior temporal sulcus using recent advances in parallel imaging to perform functional magnetic resonance imaging (fMRI) at very high resolution. These studies suggest a functional architecture in which information from different modalities is brought into close proximity via a patchy distribution of inputs, followed by integration in the intervening cortex.

/pdf/unraveling-multisensory-integration-patchy-organization-529xrk1157.pdf

Unraveling multisensory integration: patchy organization within human STS multisensory cortex.

A survey of Tactile Human-Robot Interactions

Surface-based brain imaging analysis is increasingly being used for detailed analysis of the topology of brain activation patterns and changes in cerebral gray matter. Here we present SUMA, a new interface for visualizing and performing surface-based brain imaging analysis that is tightly coupled to AFNI - a volume-based brain imaging analysis suite. The interactive part of SUMA is used for rapid and interactive surface and data visualization, access and manipulations with direct link to the volumetric data rendered in AFNI. The batch-mode part of SUMA allows for surface based operations such as geometry and data smoothing, surface to volume domain mapping in both directions and node-based statistical and computational tools. We also present methods for mapping low resolution functional data onto the cortical surface while preserving the topological information present in the volumetric data and detail an efficient procedure for performing cross-subject, surface-based analysis with minimal interpolation of the functional data.

/pdf/suma-an-interface-for-surface-based-intra-and-inter-subject-eiv6i0tb71.pdf

Brenna D. Argall

Papers

A survey of robot learning from demonstration

Integration of auditory and visual information about objects in superior temporal sulcus.

Unraveling multisensory integration: patchy organization within human STS multisensory cortex.

A survey of Tactile Human-Robot Interactions

SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI