Using humanoid robots to study human behavior

Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other

Probabilistic Inference for Fast Learning in Control

We provide a novel framework for very fast model-based reinforcement learning in continuous state and action spaces. The framework requires probabilistic models that explicitly characterize their levels of confidence. Within this framework, we use flexible, non-parametric models to describe the world based on previously collected experience. We demonstrate learning on the cart-pole problem in a setting where we provide very limited prior knowledge about the task. Learning progresses rapidly, and a good policy is found after only a hand-full of iterations

On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes

This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern Recognition (PR) applications, and have numerous applications because of their known error bounds. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a priori target classes (values) of selected neighbors to, for example, predict the target class of the tested sample. Recently, an implementation of the k-NN, named as the Locally Linear Reconstruction (LLR) [11], has been proposed. The salient feature of the latter is that by invoking a quadratic optimization process, it is capable of systematically setting model parameters, such as the number of neighbors (specified by the parameter, k) and the weights. However, the LLR takes more time than other conventional methods when it has to be applied to classification tasks. To overcome this problem, we propose a strategy of using a PRS to efficiently compute the optimization problem. In this paper, we demonstrate, first of all, that by completely discarding the points not included by the PRS, we can obtain a reduced set of sample points, using which, in turn, the quadratic optimization problem can be computed far more expediently. The values of the corresponding indices are comparable to those obtained with the original training set (i.e., the one which considers all the data points) even though the computations required to obtain the prototypes and the corresponding classification accuracies are noticeably less. The proposed method has been tested on artificial and real-life data sets, and the results obtained are very promising, and has potential in PR applications

Adaptive Optimal Feedback Control with Learned Internal Dynamics Models

Optimal Feedback Control (OFC) has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. Recent developments, such as the Iterative Linear Quadratic Gaussian (ILQG) algorithm, have focused on the case of non-linear, but still analytically available, dynamics. For realistic control systems, however, the dynamics may often be unknown, difficult to estimate, or subject to frequent systematic changes. In this chapter, we combine the ILQG framework with learning the forward dynamics for simulated arms, which exhibit large redundancies, both, in kinematics and in the actuation. We demonstrate how our approach can compensate for complex dynamic perturbations in an online fashion. The specific adaptive framework introduced lends itself to a computationally more efficient implementation of the ILQG optimisation without sacrificing control accuracy – allowing the method to scale to large DoF systems

You did it on purpose! Towards intentional embodied agents

The paper describes a road-map towards intentional behavior in artificial systems. We catch the developmental path in two dimensions, a social and an intentional dimension. Starting out with a babbling phase, development continues over an exploratory phase without social interactions and a phase in which action-level imitation is used. The pinnacle of development is the intentional imitation of goals. An experiment, together with preliminary results, is presented for each developmental phase