2 research outputs found
Active Area Coverage from Equilibrium
This paper develops a method for robots to integrate stability into actively
seeking out informative measurements through coverage. We derive a controller
using hybrid systems theory that allows us to consider safe equilibrium
policies during active data collection. We show that our method is able to
maintain Lyapunov attractiveness while still actively seeking out data. Using
incremental sparse Gaussian processes, we define distributions which allow a
robot to actively seek out informative measurements. We illustrate our methods
for shape estimation using a cart double pendulum, dynamic model learning of a
hovering quadrotor, and generating galloping gaits starting from stationary
equilibrium by learning a dynamics model for the half-cheetah system from the
Roboschool environment.Comment: 16 page
An Ergodic Measure for Active Learning From Equilibrium
This paper develops KL-Ergodic Exploration from Equilibrium
(), a method for robotic systems to integrate stability into
actively generating informative measurements through ergodic exploration.
Ergodic exploration enables robotic systems to indirectly sample from
informative spatial distributions globally, avoiding local optima, and without
the need to evaluate the derivatives of the distribution against the robot
dynamics. Using hybrid systems theory, we derive a controller that allows a
robot to exploit equilibrium policies (i.e., policies that solve a task) while
allowing the robot to explore and generate informative data using an ergodic
measure that can extend to high-dimensional states. We show that our method is
able to maintain Lyapunov attractiveness with respect to the equilibrium task
while actively generating data for learning tasks such, as Bayesian
optimization, model learning, and off-policy reinforcement learning. In each
example, we show that our proposed method is capable of generating an
informative distribution of data while synthesizing smooth control signals. We
illustrate these examples using simulated systems and provide simplification of
our method for real-time online learning in robotic systems