24,725 research outputs found
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
IMPLEMENTATION OF A LOCALIZATION-ORIENTED HRI FOR WALKING ROBOTS IN THE ROBOCUP ENVIRONMENT
This paper presents the design and implementation of a human–robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human–robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularized software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information. </jats:p
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