Wed, March 15th, 11AM Volen 101
Behavior-based control, which exploits the dynamics of collections of concurrent, interacting processes coupled to the external world, is both biologically relevant and effective for problems featuring local information, uncertainty, and non-stationarity. In this talk we describe methods we have developed for principled behavior-based control and learning in two problem domains: multi-robot team coordination and humanoid imitation.
In the multi-robot domain the key challenges involve reconciling individual and group-level goals and achieving scalable, on-line real-time learning. How to do all of this in a distributed behavior-based way in a timely and consistent fashion? We will describe our results in making distributed, behavior-based systems perform in a well-behaved fashion on problems of behavior selection at the individual and group level, communication for dynamic task allocation, and on-line model learning. We will describe the use of Pareto-optimality and satisficing to make behavior selection both principled and timely, the robust publish/subscribe messaging paradigm for distributed communication, and augmented Markov models for on-line real-time model building for adaptation. We will demonstrate the results of these methods on groups of locally-controlled but globally efficient cooperative mobile robots performing distributed collection, multiple-target-tracking and capture, and object manipulation.
In the humanoid control domain the key challenges are the high dimensionality of the problem and the choice of representation and modularity that properly integrates the perceptual and motor systems. We describe an imitation system modeled on psychophysical and neuroscience evidence for selective movement attention, mirror neurons, and motor primitives. The system employs direct sensory-motor mappings within the behavior-based framework to address how to understand, segment, and map the observed movement onto the existing motor system. The same biologically motivated structure serves for recognition, classification, prediction, and learning. We will demonstrate the results of this model on a 20 DOF dynamic humanoid imitating human dance and sports movements from visual data.
Host: Jordan Pollack