Data-Driven Measurement Models for Active Localization in Sparse Environments

Abraham, Ian; Mavrommati, Anastasia; Murphey, Todd D.

doi:10.15607/rss.2018.xiv.045

Cited by 9 publications

(11 citation statements)

References 43 publications

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“…• that (12) does in fact reduce (4) • that ( 12) imposes a bound on the conditions in (6) • and that a robotic system subject to (12) has a notion of Lyapunov attractiveness Theoretical Analysis: We first illustrate that our approach for computing (12) does reduce (4).…”

Section: Proof See Appendix Amentioning

confidence: 99%

An Ergodic Measure for Active Learning From Equilibrium

Abraham

Prabhakar

Murphey

2021

IEEE Trans. Automat. Sci. Eng.

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This paper develops KL-Ergodic Exploration from Equilibrium (KL-E 3 ), 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 offpolicy 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.

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Section: Proof See Appendix Amentioning

confidence: 99%

An Ergodic Measure for Active Learning From Equilibrium

Abraham

Prabhakar

Murphey

2021

IEEE Trans. Automat. Sci. Eng.

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“…Here, robots often need to operate in environments where light levels prevent long-range visual monitoring, which demands the use of active learning tools in order to construct motion plans that incrementally adapt to the robot's uncertain measurements [103][104][105]. In [106], the authors use control and Gaussian process regression to model, map, and actively sample the distribution of phytoplankton in the This example shows the active identification of an unknown geometry in the environment, using binary contact measurements as the measurement modality [107]. By developing data-driven models of objects, robots can search for and recognize obstacles or tools without needing analytic or CAD models.…”

Section: Mappingmentioning

confidence: 99%

“…As an informative example of this kind of learning problem, we share some results from our own work. In [111], we considered nonparametric shape estimation using contactbased sensors to actively learn the shapes of obstacles in the robot's environment, which we then extended towards data-driven mapping and localization [107]. Figure 2 shows a three dimensional set of objects whose shapes are being reconstructed from binary contact measurements made by a simulated mobile robot.…”

Section: Shapementioning

confidence: 99%

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Active Learning in Robotics: A Review of Control Principles

Taylor,

Berrueta,

Murphey

2021

Preprint

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Active learning is a decision-making process. In both abstract and physical settings, active learning demands both analysis and action. This is a review of active learning in robotics, focusing on methods amenable to the demands of embodied learning systems. Robots must be able to learn efficiently and flexibly through continuous online deployment. This poses a distinct set of control-oriented challenges-one must choose suitable measures as objectives, synthesize realtime control, and produce analyses that guarantee performance and safety with limited knowledge of the environment or robot itself. In this work, we survey the fundamental components of robotic active learning systems. We discuss classes of learning tasks that robots typically encounter, measures with which they gauge the information content of observations, and algorithms for generating action plans. Moreover, we provide a variety of examples-from environmental mapping to nonparametric shape estimation-that highlight the qualitative differences between learning tasks, information measures, and control techniques. We conclude with a discussion of control-oriented open challenges, including safety-constrained learning and distributed learning.

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“…Here, a team of robots might engage in sensing tasks immediately preceding or in parallel with firefighters entering a building. 1 Disaster response has similar features, but the time-sensitivity is driven by scale. In the case of a widespread disaster, teams may have ample time to inspect individual buildings, but when viewed as a whole, the number of locations that must be inspected and the paucity of responders motivate rapid action.…”

Section: Introductionmentioning

confidence: 99%

Sensor Planning for Large Numbers of Robots

Corah

2021

Preprint

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For my parents, David and Barbara, who supported and encouraged my interest in robotics at every step of this journey. I also have many to thank in the Resilient Intelligent Systems Lab. My officemates, Ellen and Derek were ever-present companions. Ellen, I believe our fish tank set new standards for office decor as well as undergraduate achievement. Derek, aside from being my office compatriot, you were also an early mentor when I was still an intern. Thank you for that time. And, Vibhav, you were my replacement, but I preferred being students together. Kshitij and Cormac, we collaborated on some of the best work that is not in this thesis. Thank you Curt for keeping my robots alive before I learned to avoid them. And, thanks Wennie for doing the hard work to develop systems for exploration and mapping. Vishnu, thank you for demonstrating the first feasible solution to the graduation problem. Your leadership was invaluable. Shaurya, thank you for being someone who I can talk to about research and everything else. Also, John, the pandemic has taken our pizza, and I want it back. Thanks to everyone else in the lab who has made my time here brighter, including Alex, Xuning, Aditya, Arjav, Mosam, Moses, Tim, Matt, Mike, Erik, and anyone else who I may have forgotten. Also, Karen Widmaier, you put in a good deal of hard work for our lab, and you make our hearts warmer.Many thanks to my friends in the Robotics Institute. Achal and Radhika, Dhruv and Cara, and now I am compelled to mention Xuning again, thank you for the adventures that brought so much joy to this time. Shushman, you are a caring friend. I did not introduce you to my family; I introduced my family to you. Thank you Reuben, you speak softly, but your words carry great weight. Nick, you have done great things for the RI, but you also do great things for frogs.Thanks to everyone else who contributed to my time here. Rachel Burcin, I am only one of a vast number of people who have been significantly impacted by your tireless enthusiasm administrating RISS. I could not keep up with you. Reid, you welcomed me to CMU twice, and I hope to someday become more like you. Thank you Michael Erdmann, you allowed me to babble about submodular functions in front of 16-811. Thanks Sankalp, your words in front of same class introduced me to same topic. My thanks also to Roie for validating my interest in 3-increasing functions and for our too infrequent exchanges on the topic.Thank you Nate for guiding my journey in robotics at CMU. You accepted my stubbornness and pushed me to excellence. Thanks also to the rest of my committee, Anupam, Katia, and Mac for direction and advice. I hope these first few pages are to your liking; for the next hundred, well I am not Asimov.

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Data-Driven Measurement Models for Active Localization in Sparse Environments

Cited by 9 publications

References 43 publications

An Ergodic Measure for Active Learning From Equilibrium

An Ergodic Measure for Active Learning From Equilibrium

Active Learning in Robotics: A Review of Control Principles

Sensor Planning for Large Numbers of Robots

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