Please follow the links for brief descriptions
Convergent Human and Robot Learning for Effective Robot Skill Generation
Computational Modeling of Mirror Neurons
Sign-Representation of Boolean Functions
Dexterous Manual Manipulation
Human visuomotor learning for robot skill synthesis : Dexterous manipulation
This study explores how the human visuomotor learning ability can be utilized for obtaining dexterous manipulation and movement capabilities on robots (see also item 4 below). For example an effortless ball manipulation via realtime control of the Gifu Hand can be seen here . A more challenging task is to rotate the so called Chinese healing balls without dropping them. With training, the robot hand is integrated into human ‘body schema’ allowing the subject to perform this task with the robot hand. Here is a movie or this showing the obtained skill with this paradigm. This basic skill then can be tuned to improve performance (e.g. speed) as shown here .
Self-observation and auto-association as route to simple imitation
In the previous years, we have explored the associative memory hypothesis of imitation bootstrapping with the Gifu Hand. Click for a demo movie.
Application to Brain Machine Interface
Collaborating with Honda and neuroscientists at ATR/CNS, we employed the Gifu Hand in a brain-machine-interface (BMI) project. Using fMRI, human subjects’ brain activity are mapped to one of rock/scissors/paper hand postures that are replicated on the Gifu Hand in near real-time.Take a Google search on the project.
Human visuomotor learning for robot skill synthesis: Reaching while keeping static balance
This is the extension of the ‘human visuomotor learning for robot skill synthesis’ paradigm to full body humanoid robots. This is a collaborative work with Jan Babic at Jozef Stefan Institute, Slovenia and Joshua Hale at ATR, Japan. Here is the human human control of the robot, where the subject was asked to keep the robot balanced while tracing a trajectory with his finger. The data collected is used to derive a balanced reaching skill. Here this skill is used to have the robot trace an elliptical trajectory.
Improving the human visuomotor learning for robot skill synthesis paradigm
This platform can carry a human. The idea is this: the subject controlling a humanoid robot will ‘ride’ the platform and ‘feel’ how the robot feels in terms of the dynamics of the center of mass of the robot. Here the force control of the platform can be seen.
Representation of Boolean functions (dichotomies over the n-cube) using polynomials (higher order neurons) with a small number of monomials (fan-in).
Higher-order neurons or sigma-pi units are extensions of linear neuron models, which capture the nonlinearity in the input-output relation of a mapping using products of input variables, called the monomials. The net input to a higher-order unit is the sum of the monomials weighted by adjustable parameters. The output is obtained by the application of a predefined activation function, usually a sigmoidal function, or a threshold function to the net input. There are many aspects of this powerful model that deserves attention. My main interest is to study the number of monomials that a higher order neuron would require to solve a given classification. More generally; given a set of classification problems what is the minimum number of monomials that can solve the given problem set? Recently, I have showed that any dichotomy of the n-cube can be realized with 0.75*2n or less monomials. This is the best bound known so far. Here is the reprint that has the proof of this claim.
Motor interference: an objective tool to test the extent that a robot is perceived as human-like
It is generally accepted that (humanoid) robots will become part of out daily lives. So it is important to understand how well they will be accepted as social partners. In this direction, we have adopted the motor interference effect observed in human-human interactions to study study the human perception of robots as social partners. Motor interference refers to the differential effect of observing an action while performing a compatible or an incompatible action. An example of a compatible and incompatible movement pair is the vertical and. lateral hand movements. We have recently shown that a humanoid robot (DB) moving similar to a human elicits motor interference. We now are conducting experiments to tease apart the contribution of motion and form to this reaction. To get idea of the experiment setup click here.
Grasp Affordance Learning
Grasp Affordance refers to the intrinsic features of an object that are relevant for grasping. For example the color of pen, in general, is not part of its grasp affordance because it does not guide the grasping behavior. In macaque monkeys the parietal area AIP appears to be involved in affordance extraction. AIP with the ventral premotor cortex (F5) forms the core of the monkey grasping circuit. Recently I developed a model for AIP neurons which is based on the hypothesis that early grasping of infants (being mediated by other mechanisms) provide the learning data points for F5-AIP complex to learn a mapping from visual->motor representation. The critical test is then to see whether this visuo-motor learning leads to the emergence of unit responses that are comparable to actual AIP neurons. The simulation results show that this is correct. The future research plan is to compare the modeled AIP unit activities with AIP neuron discharge profiles in a quantitative way.
Mirror Neurons and Imitation
According to the general opinion, high level functions such as imitation, action understanding and (precursors of) language are attributed to mirror neurons. However it is not clear how much the human mirror system has evolved to support imitation and language, if indeed there is a connection between these skills and the mirror neurons. Furthermore the number of studies that take a computational viewpoint to study these hypothesis is limited. Recently, guided by my earlier modeling of mirror neurons and mental state inference mechanisms I have made a meta-analysis of the computational models (that can be seen as models of mirror neurons) and current opinions about mirror neuron function. Here is the reprint.
Older projects and links
PhD Related links