26,422 research outputs found
Pseudospectral Model Predictive Control under Partially Learned Dynamics
Trajectory optimization of a controlled dynamical system is an essential part
of autonomy, however many trajectory optimization techniques are limited by the
fidelity of the underlying parametric model. In the field of robotics, a lack
of model knowledge can be overcome with machine learning techniques, utilizing
measurements to build a dynamical model from the data. This paper aims to take
the middle ground between these two approaches by introducing a semi-parametric
representation of the underlying system dynamics. Our goal is to leverage the
considerable information contained in a traditional physics based model and
combine it with a data-driven, non-parametric regression technique known as a
Gaussian Process. Integrating this semi-parametric model with model predictive
pseudospectral control, we demonstrate this technique on both a cart pole and
quadrotor simulation with unmodeled damping and parametric error. In order to
manage parametric uncertainty, we introduce an algorithm that utilizes Sparse
Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We
implement this online learning technique on a cart pole and quadrator, then
demonstrate the use of online learning and obstacle avoidance for the dubin
vehicle dynamics.Comment: Accepted but withdrawn from AIAA Scitech 201
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Reinforcement Learning is divided in two main paradigms: model-free and
model-based. Each of these two paradigms has strengths and limitations, and has
been successfully applied to real world domains that are appropriate to its
corresponding strengths. In this paper, we present a new approach aimed at
bridging the gap between these two paradigms. We aim to take the best of the
two paradigms and combine them in an approach that is at the same time
data-efficient and cost-savvy. We do so by learning a probabilistic dynamics
model and leveraging it as a prior for the intertwined model-free optimization.
As a result, our approach can exploit the generality and structure of the
dynamics model, but is also capable of ignoring its inevitable inaccuracies, by
directly incorporating the evidence provided by the direct observation of the
cost. Preliminary results demonstrate that our approach outperforms purely
model-based and model-free approaches, as well as the approach of simply
switching from a model-based to a model-free setting.Comment: After we submitted the paper for consideration in CoRL 2017 we found
a paper published in the recent past with a similar method (see related work
for a discussion). Considering the similarities between the two papers, we
have decided to retract our paper from CoRL 201
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