Скачать или смотреть Optimizing the Execution of Dynamic Robot Movements with Learning Control

High-speed robotics typically involves fast dynamic
trajectories with large accelerations. Kinematic optimization
using compact representations can lead to an efficient online
computation of these dynamic movements, however successful
execution requires accurate models or aggressive tracking with
high-gain feedback. Learning to track such references in a safe
and reliable way, whenever accurate models are not available,
is an open problem. Stability issues surrounding the learning
performance, in the iteration domain, can prevent the successful
implementation of model-based learning approaches. To this end,
we propose a new adaptive and cautious Iterative Learning
Control (ILC) algorithm where the stability of the control updates
is analyzed probabilistically. The covariance estimates of the
adapted local linear models are used to increase the probability
of update monotonicity, exercising caution during learning. The
resulting learning controller can be implemented efficiently using
a recursive approach. We evaluate it extensively in simulations
as well as in our robot table tennis setup for tracking dynamic
hitting movements. Testing with two seven degree of freedom
anthropomorphic robot arms, we show improved and more stable
tracking performance over high-gain PD-control, model-free ILC
(simple PD feedback type) and model-based ILC without cautious
adaptation.