Physical Information & Active Learning
How does a robot use its physical capabilities to actively explore, sense, and learn about itself and its environment? This research is about developing control policies to exploit the physical motion of robotic systems for active learning and sensing. We are interested in understanding how information can be used to drive decision-making and how one can generate control actions with respect to principles of information theory and measures of information.
Autonomous Control Synthesis based on Information and Statistical Distributions
This research is focused on understanding how information and statistics can be used to drive decisions and how one can generate control actions with respect to principles of information theory and statistics. Our current research focus is on understanding how these principles can be incorporated into the objectives for motion tasks and deriving real-time algorithms that can robustly generate efficient trajectories that still obey the system constraints and dynamics. For a quick, high-level overview of our approach, watch our video "Autonomous Robot Drawing: From Distributions to Actions Using Feedback"
Autonomous Robot Drawing
This project is focused on generating methods to translate information via controls for physical motion. By using information density distributions to synthesize controls, we can enable robots to autonomously render an image based on the visual information. The full video of all the robot drawings can be found below.
Sawyer Robot drawing a portrait of Abraham Lincoln
For more information, read:
https://arxiv.org/abs/1709.02758
Watch the NSF "Science Nation" feature video of this work here.
Autonomous Shape Estimation
This project focuses on developing an algorithm for autonomous shape exploration using sensory contact information. In particular, we are developing algorithms for real-time shape exploration that does not rely on prior knowledge of shapes or the number of objects in the workspace, but can adapt control actions in real-time as information is obtained.
Shape estimation of two different objects over time using a binary contact sensor and ergodic exploration
For more information, read:
https://arxiv.org/abs/1709.01560
Ergodic Control for Exploration
This research is focused on using principles from statistics to understand how biological systems plan movements with respect to the movement goal and how this understanding can be applied to robotics and rehabilitation strategies. In particular, we are interested in how the objective for motion tasks, for example gathering sensory information, drives the way in which a system plans movement. This research relies on the use of a mathematical concept from the field of ergodic theory, ergodicity, which provides a quantitative way of understanding the spatial statistics of a motion.
For more information, read:
https://arxiv.org/abs/1708.09352
Trajectory Optimization for Parameter Estimation
This project focuses on developing algorithms for efficiently estimating parameters of a system. By incorporating information metrics into the objective function, we are able to actively identify model parameters of a system and adapt the model and controls in order to accomplish a task. We are interested in investigating efficient ways to generate control actions in real-time and how metrics such as the Fisher information metric can be used to efficiently drive decisions to facilitate active parameter estimation.
For more information, read:
https://arxiv.org/abs/1709.03474
https://arxiv.org/abs/1709.03426
People
Todd Murphey
Ian Abraham
Ahalya Prabhakar
Andrew Wilson
Related Publications
Ergodicity reveals assistance and learning from physical human-robot interaction, Science: Robotics, vol. 4, no. 29, 04/2019/ 2019 DOI Google Scholar
Decentralized Ergodic Control Distribution-Driven Sensing and Exploration for Multiagent Systems, IEEE Robotics and Automation Letters, vol. 3, pp. 2987-2994, Oct/ 2018 DOI Google Scholar PDF Video
Active Area Coverage from Equilibrium, Workshop on the Algorithmic Foundations of Robotics WAFR, 2018 Google Scholar PDF PDF Video
Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration, IEEE Transactions on Robotics, no. 34, pp. 62-80, Jan-01-2017/ 2018 DOI Google Scholar PDF VideoVideo
Ergodic Exploration using Binary Sensing for Non-Parametric Shape Estimation, IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 827-834, 2017 Google Scholar PDF Video
Ergodic Exploration of Distributed Information, Transactions on Robotics, vol. 32, no. 1, pp. 36-52, 2016 DOI Google Scholar PDF Video
Autonomous Visual Rendering using Physical Motion, Workshop on the Algorithmic Foundations in Robotics (WAFR) 2016, 2016 Google Scholar PDF Video
Trajectory Optimization for Well-Conditioned Parameter Estimation, IEEE Transactions on Automation Science and Engineering, vol. 12, no. 1, pp. 28-36, 2015 DOI Google Scholar PDF
Trajectory Optimization for Continuous Ergodic Exploration, American Control Conference (ACC), 2013 Google Scholar PDF
Optimal Trajectory Design for Well-Conditioned Parameter Estimation, IEEE Conference on Automation Science and Engineering (CASE), pp. 13-19, 2013 DOI Google Scholar PDF
Optimal contact decisions for ergodic exploration, IEEE Int. Conf. on Decision and Control (CDC), 2012 Google Scholar
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