Learning agent's spatial configuration from sensorimotor invariants

The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness. In order to develop truly autonomous robots, we must step away from this intuition and let robotic agents develop their own way of perceiving. The robot should start from scratch and gradually develop perceptual notions, under no prior assumptions, exclusively by looking into its sensorimotor experience and identifying repetitive patterns and invariants. One of the most fundamental perceptual notions, space, cannot be an exception to this requirement. In this paper we look into the prerequisites for the emergence of simplified spatial notions on the basis of a robot's sensorimotor flow. We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment. The environment-independent definition of space can be approached by looking into the functions that link the motor commands to changes in exteroceptive inputs. In a sufficiently rich environment, the kernels of these functions correspond uniquely to the spatial configuration of the agent's exteroceptors. We simulate a redundant robotic arm with a retina installed at its end-point and show how this agent can learn the configuration space of its retina. The resulting manifold has the topology of the Cartesian product of a plane and a circle, and corresponds to the planar position and orientation of the retina.Comment: 26 pages, 5 images, published in Robotics and Autonomous System

Guest Editorial Active Learning and Intrinsically Motivated Exploration in Robots: Advances and Challenges

International audienceLEARNING techniques are increasingly being used in today's complex robotic systems. Robots are expected to deal with a large variety of tasks using their high-dimensional and complex bodies, to manipulate objects and also, to interact with humans in an intuitive and friendly way. In this new setting, not all relevant information is available at design time, and robots should typically be able to learn, through self-ex- perimentation or through human–robot interaction, how to tune their innate perceptual-motor skills or to learn, cumulatively, novel skills that were not preprogrammed initially. In a word, robots need to have the capacity to develop in an open-ended manner and in an open-ended environment, in a way that is analogous to human development which combines genetic and epigenetic factors. This challenge is at the center of the developmental robotics field. Among the various technical challenges that are raised by these issues, exploration is paramount. Self-experimentation and learning by interacting with the physical and social world is essential to acquire new knowledge and skills

Body Schema Acquisition through Active Learning

Abstract — We present an active learning algorithm for the problem of body schema learning, i.e. estimating a kinematic model of a serial robot. The learning process is done online using Recursive Least Squares (RLS) estimation, which outperforms gradient methods usually applied in the literature. In addiction, the method provides the required information to apply an active learning algorithm to find the optimal set of robot configurations and observations to improve the learning process. By selecting the most informative observations, the proposed method minimizes the required amount of data. We have developed an efficient version of the active learning algorithm to select the points in real-time. The algorithms have been tested and compared using both simulated environments and a real humanoid robot. I