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.
Intrinsic motivation signals prediction, familiarity (in terms of frequency of state transitions) and stability (in terms of sensor signals to its average) were investigated in @cite_63 in task-independent online visual exploration problems in simulation and on a simple robot.
@cite_63
1590585250
What dynamics can enable a robot to continuously develop new visual know-how? We present a first experimental investigation where an AIBO robot develops visual competences from scratch driven only by internal motivations. The motivational principles used by the robot are independent of any particular task. As a consequence, they can constitute the basis for a general approach to sensory-motor development.