Abstract 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 signal cognitive 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.
Early work on intrinsically motivated learning not using the typically reinforcement learning framework used the prediction error of sensory inputs for self-localization tasks @cite_69 . In an online setup, the system explored novel and interesting stimuli to learn a representation of the environment. By using this intrinsic motivation signal, the system developed structures for perception, representation and actions in a neural network model. Actions were chosen such that the expected increase of knowledge was maximized. The approach was evaluated in a gridworld domain and on a simple mobile robot platform.
@cite_69
2010133763
Abstract We demonstrate by a schematic model of an unexperienced animal exploring an environment that it is possible to evolve structures for perception, representation and action simultaneously from a single criterion, namely the error in predicting future sensory inputs. In order to organize successful representations of the environment actions are chosen which are expected to maximize the increase of knowledge. Initially trivial behaviors are generated that allow to learn to recognize places, whereas subsequently virtually random movements indicate that an invariant representation of the environment has emerged.