Introduction: The Third International Conference on Epigenetic Robotics

This paper summarizes the paper and poster contributions to the Third International Workshop on Epigenetic Robotics. The focus of this workshop is on the cross-disciplinary interaction of developmental psychology and robotics. Namely, the general goal in this area is to create robotic models of the psychological development of various behaviors. The term "epigenetic" is used in much the same sense as the term "developmental" and while we could call our topic "developmental robotics", developmental robotics can be seen as having a broader interdisciplinary emphasis. Our focus in this workshop is on the interaction of developmental psychology and robotics and we use the phrase "epigenetic robotics" to capture this focus

Robot End Effector Tracking Using Predictive Multisensory Integration

We propose a biologically inspired model that enables a humanoid robot to learn how to track its end effector by integrating visual and proprioceptive cues as it interacts with the environment. A key novel feature of this model is the incorporation of sensorimotor prediction, where the robot predicts the sensory consequences of its current body motion as measured by proprioceptive feedback. The robot develops the ability to perform smooth pursuit-like eye movements to track its hand, both in the presence and absence of visual input, and to track exteroceptive visual motions. Our framework makes a number of advances over past work. First, our model does not require a fiducial marker to indicate the robot hand explicitly. Second, it does not require the forward kinematics of the robot arm to be known. Third, it does not depend upon pre-defined visual feature descriptors. These are learned during interaction with the environment. We demonstrate that the use of prediction in multisensory integration enables the agent to incorporate the information from proprioceptive and visual cues better. The proposed model has properties that are qualitatively similar to the characteristics of human eye-hand coordination

Life-Space Foam: a Medium for Motivational and Cognitive Dynamics

General stochastic dynamics, developed in a framework of Feynman path integrals, have been applied to Lewinian field--theoretic psychodynamics, resulting in the development of a new concept of life--space foam (LSF) as a natural medium for motivational and cognitive psychodynamics. According to LSF formalisms, the classic Lewinian life space can be macroscopically represented as a smooth manifold with steady force-fields and behavioral paths, while at the microscopic level it is more realistically represented as a collection of wildly fluctuating force-fields, (loco)motion paths and local geometries (and topologies with holes). A set of least-action principles is used to model the smoothness of global, macro-level LSF paths, fields and geometry. To model the corresponding local, micro-level LSF structures, an adaptive path integral is used, defining a multi-phase and multi-path (multi-field and multi-geometry) transition process from intention to goal-driven action. Application examples of this new approach include (but are not limited to) information processing, motivational fatigue, learning, memory and decision-making.Comment: 25 pages, 2 figures, elsar

Initialization of latent space coordinates via random linear projections for learning robotic sensory-motor sequences

Robot kinematic data, despite being high-dimensional, is highly correlated, especially when considering motions grouped in certain primitives. These almost linear correlations within primitives allow us to interpret motions as points drawn close to a union of low-dimensional affine subspaces in the space of all motions. Motivated by results of embedding theory, in particular, generalizations of the Whitney embedding theorem, we show that random linear projection of motor sequences into low-dimensional space loses very little information about the structure of kinematic data. Projected points offer good initial estimates for values of latent variables in a generative model of robot sensory-motor behavior primitives. We conducted a series of experiments in which we trained a Recurrent Neural Network to generate sensory-motor sequences for a robotic manipulator with 9 degrees of freedom. Experimental results demonstrate substantial improvement in generalization abilities for unobserved samples during initialization of latent variables with a random linear projection of motor data over initialization with zero or random values. Moreover, latent space is well-structured such that samples belonging to different primitives are well separated from the onset of the training process

Data-driven learning for robot physical intelligence

The physical intelligence, which emphasizes physical capabilities such as dexterous manipulation and dynamic mobility, is essential for robots to physically coexist with humans. Much research on robot physical intelligence has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this dissertation, a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation is proposed. This method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. Besides, the power of crowdsourcing is brought to tackle case-specific engineering problem in the robot physical intelligence. Crowdsourcing has demonstrated great potential in recent development of artificial intelligence. Constant learning from a large group of human mentors breaks the limit of learning from one or a few mentors in individual cases, and has achieved success in image recognition, translation, and many other cyber applications. A robot learning scheme that allows a robot to synthesize new physical skills using knowledge acquired from crowdsourced human mentors is proposed. The work is expected to provide a long-term and big-scale measure to produce advanced robot physical intelligence