23,671 research outputs found
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration
In this paper, we present a robotic model-based reinforcement learning method
that combines ideas from model identification and model predictive control. We
use a feature-based representation of the dynamics that allows the dynamics
model to be fitted with a simple least squares procedure, and the features are
identified from a high-level specification of the robot's morphology,
consisting of the number and connectivity structure of its links. Model
predictive control is then used to choose the actions under an optimistic model
of the dynamics, which produces an efficient and goal-directed exploration
strategy. We present real time experimental results on standard benchmark
problems involving the pendulum, cartpole, and double pendulum systems.
Experiments indicate that our method is able to learn a range of benchmark
tasks substantially faster than the previous best methods. To evaluate our
approach on a realistic robotic control task, we also demonstrate real time
control of a simulated 7 degree of freedom arm.Comment: 8 page
Feedback MPC for Torque-Controlled Legged Robots
The computational power of mobile robots is currently insufficient to achieve
torque level whole-body Model Predictive Control (MPC) at the update rates
required for complex dynamic systems such as legged robots. This problem is
commonly circumvented by using a fast tracking controller to compensate for
model errors between updates. In this work, we show that the feedback policy
from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable
alternative to bridge the gap between the low MPC update rate and the actuation
command rate. We propose to augment the DDP approach with a relaxed barrier
function to address inequality constraints arising from the friction cone. A
frequency-dependent cost function is used to reduce the sensitivity to
high-frequency model errors and actuator bandwidth limits. We demonstrate that
our approach can find stable locomotion policies for the torque-controlled
quadruped, ANYmal, both in simulation and on hardware.Comment: Paper accepted to IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019
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