Task-Driven Estimation and Control via Information Bottlenecks
Task-Driven Estimation and Control via Information Bottlenecks
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DOI：
10.1109/icra.2019.8794213
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发表时间：
2018-09
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通讯作者：
Vincent Pacelli;Anirudha Majumdar
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作者：
Vincent Pacelli;Anirudha Majumdar
我们的目标是为机器人系统的任务驱动估计和控制开发一个原则性和通用的算法框架。最先进的控制机器人系统的方法通常严重依赖于准确估计机器人的全部状态（例如，一个跑步的机器人可能会估计关节的角度和速度、躯干状态和相对于目标的位置）。然而，对于手头的特定任务来说，完整状态表示通常过于丰富，并且可能导致严重的计算效率低下和状态估计错误的脆弱性。相比之下，我们提出了一种避开这种丰富表示的方法，并寻求创建任务驱动的表示。关键的技术见解是利用信息瓶颈理论，根据测量表示的最小性的信息理论量来形式化“任务驱动表示”的概念。我们提出了一种新的迭代算法，用于自动合成（离线）任务驱动表示（根据一组任务相关变量（trv）给出）和作为trv函数的性能控制策略。为了应用控制策略，我们提出了在线估计trv的算法。我们证明了我们的方法在理论上和通过全面的仿真实验（包括运行到目标位置的弹簧负载倒立摆）对未建模的测量不确定性具有显著的鲁棒性。
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 presentonline 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.