753 research outputs found
Task-Driven Estimation and Control via Information Bottlenecks
Our goal is to develop a principled and general algorithmic framework for
task-driven estimation and control for robotic systems. State-of-the-art
approaches for controlling robotic systems typically rely heavily on accurately
estimating the full state of the robot (e.g., a running robot might estimate
joint angles and velocities, torso state, and position relative to a goal).
However, full state representations are often excessively rich for the specific
task at hand and can lead to significant computational inefficiency and
brittleness to errors in state estimation. In contrast, we present an approach
that eschews such rich representations and seeks to create task-driven
representations. The key technical insight is to leverage the theory of
information bottlenecks}to formalize the notion of a "task-driven
representation" in terms of information theoretic quantities that measure the
minimality of a representation. We propose novel iterative algorithms for
automatically synthesizing (offline) a task-driven representation (given in
terms of a set of task-relevant variables (TRVs)) and a performant control
policy that is a function of the TRVs. We present online algorithms for
estimating the TRVs in order to apply the control policy. We demonstrate that
our approach results in significant robustness to unmodeled measurement
uncertainty both theoretically and via thorough simulation experiments
including a spring-loaded inverted pendulum running to a goal location.Comment: 9 pages, 4 figures, abridged version accepted to ICRA2019;
Incorporates changes in final conference submissio
- …