7,382 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
A General Framework for Hierarchical Redundancy Resolution Under Arbitrary Constraints
The increasing interest in autonomous robots with a high number of degrees of
freedom for industrial applications and service robotics demands control
algorithms to handle multiple tasks as well as hard constraints efficiently.
This paper presents a general framework in which both kinematic (velocity- or
acceleration-based) and dynamic (torque-based) control of redundant robots are
handled in a unified fashion. The framework allows for the specification of
redundancy resolution problems featuring a hierarchy of arbitrary (equality and
inequality) constraints, arbitrary weighting of the control effort in the cost
function and an additional input used to optimize possibly remaining
redundancy. To solve such problems, a generalization of the Saturation in the
Null Space (SNS) algorithm is introduced, which extends the original method
according to the features required by our general control framework. Variants
of the developed algorithm are presented, which ensure both efficient
computation and optimality of the solution. Experiments on a KUKA LBRiiwa
robotic arm, as well as simulations with a highly redundant mobile manipulator
are reported.Comment: 19 pages, 19 figures, submitted to the IEE
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