Neuro-mechanical entrainment in a bipedal robotic walking platform

In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace

Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions

Neuro-mechanical entrainment in a bipedal robotic walking platform

In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace

Motion Imitation Based on Sparsely Sampled Correspondence

Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human's poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparse correspondence. Methods for generating these sparse correspondence samples have also been introduced. Our method is evaluated by applying the human's motion captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.Comment: accepted in Neural Network