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no code implementations • 15 Feb 2024 • Jingqi Li, Anand Siththaranjan, Somayeh Sojoudi, Claire Tomlin, Andrea Bajcsy

Autonomous agents should be able to coordinate with other agents without knowing their intents ahead of time.

no code implementations • 18 Jan 2024 • Marsalis Gibson, David Babazadeh, Claire Tomlin, Shankar Sastry

Even though image maps may contribute slightly to the prediction output of both models, this result reveals that rather than being robust to adversarial image perturbations, trajectory predictors are susceptible to image attacks.

1 code implementation • 2 Oct 2023 • Katie Kang, Amrith Setlur, Claire Tomlin, Sergey Levine

Rather than extrapolating in arbitrary ways, we observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD.

1 code implementation • 14 Jul 2023 • Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan, Jamie Shotton, Alex Kendall, Claire Tomlin, Christopher P. Burgess

Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features.

no code implementations • 4 Apr 2023 • Jingqi Li, Chih-Yuan Chiu, Lasse Peters, Fernando Palafox, Mustafa Karabag, Javier Alonso-Mora, Somayeh Sojoudi, Claire Tomlin, David Fridovich-Keil

To accommodate this, we decompose the approximated game into a set of smaller games with few constraints for each sampled scenario, and propose a decentralized, consensus-based ADMM algorithm to efficiently compute a generalized Nash equilibrium (GNE) of the approximated game.

no code implementations • 2 Apr 2023 • Anish Muthali, Haotian Shen, Sampada Deglurkar, Michael H. Lim, Rebecca Roelofs, Aleksandra Faust, Claire Tomlin

We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning.

no code implementations • 1 Dec 2022 • Thomas T. Zhang, Katie Kang, Bruce D. Lee, Claire Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni

In particular, we consider a setting where learning is split into two phases: (a) a pre-training step where a shared $k$-dimensional representation is learned from $H$ source policies, and (b) a target policy fine-tuning step where the learned representation is used to parameterize the policy class.

no code implementations • 21 Jun 2022 • Katie Kang, Paula Gradu, Jason Choi, Michael Janner, Claire Tomlin, Sergey Levine

Learned models and policies can generalize effectively when evaluated within the distribution of the training data, but can produce unpredictable and erroneous outputs on out-of-distribution inputs.

2 code implementations • 15 Apr 2022 • Benjamin W. Blonder, Michael H. Lim, Zachary Sunberg, Claire Tomlin

Using several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs; and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices.

1 code implementation • 18 Jan 2022 • Andreea Bobu, Marius Wiggert, Claire Tomlin, Anca D. Dragan

To get around this issue, recent deep Inverse Reinforcement Learning (IRL) methods learn rewards directly from the raw state but this is challenging because the robot has to implicitly learn the features that are important and how to combine them, simultaneously.

no code implementations • 27 Nov 2021 • Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.

no code implementations • 22 Sep 2021 • Anish Muthali, Forrest Laine, Claire Tomlin

However, for detections generated from neural-network processed camera inputs, these measurement error statistics are not sufficient to represent the primary source of errors, namely a dissimilarity between run-time sensor input and the training data upon which the detector was trained.

no code implementations • 15 Sep 2021 • Michael H. Lim, Andy Zeng, Brian Ichter, Maryam Bandari, Erwin Coumans, Claire Tomlin, Stefan Schaal, Aleksandra Faust

Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications.

no code implementations • 11 Nov 2020 • Forrest Laine, Claire Tomlin

In particular, we propose training an ensemble of density models, considering data to be anomalous if the data is anomalous with respect to any member of the ensemble.

1 code implementation • 4 Nov 2020 • Somil Bansal, Claire Tomlin

Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the ability to deal with state and input constraints.

no code implementations • 26 Oct 2020 • Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire Tomlin, S. Shankar Sastry

In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings.

1 code implementation • 23 Jun 2020 • Andreea Bobu, Marius Wiggert, Claire Tomlin, Anca D. Dragan

When the correction cannot be explained by these features, recent work in deep Inverse Reinforcement Learning (IRL) suggests that the robot could ask for task demonstrations and recover a reward defined over the raw state space.

1 code implementation • 20 Mar 2020 • Varun Tolani, Somil Bansal, Aleksandra Faust, Claire Tomlin

Videos describing our approach and experiments, as well as a demo of HumANav are available on the project website.

no code implementations • L4DC 2020 • Anjian Li, Somil Bansal, Georgios Giovanis, Varun Tolani, Claire Tomlin, Mo Chen

In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed.

no code implementations • 6 Mar 2019 • Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, Claire Tomlin

Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories.

no code implementations • 20 Feb 2019 • Oscar Sondermeijer, Roel Dobbe, Daniel Arnold, Claire Tomlin, Tamás Keviczky

Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids.

no code implementations • 19 Feb 2019 • Vicenc Rubies-Royo, Roberto Calandra, Dusan M. Stipanovic, Claire Tomlin

To use neural networks in safety-critical settings it is paramount to provide assurances on their runtime operation.

no code implementations • 27 Sep 2018 • Esther Rolf, David Fridovich-Keil, Max Simchowitz, Benjamin Recht, Claire Tomlin

We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions.

no code implementations • 14 Jun 2018 • Roel Dobbe, Oscar Sondermeijer, David Fridovich-Keil, Daniel Arnold, Duncan Callaway, Claire Tomlin

We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information.

no code implementations • NeurIPS 2017 • Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Claire Tomlin

We consider a model of game-theoretic learning based on online mirror descent (OMD) with asynchronous and delayed feedback information.

1 code implementation • 5 Nov 2017 • Omid Ardakanian, Vincent W. S. Wong, Roel Dobbe, Steven H. Low, Alexandra von Meier, Claire Tomlin, Ye Yuan

Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis.

no code implementations • 24 Oct 2017 • Jingge Zhu, Ye Pu, Vipul Gupta, Claire Tomlin, Kannan Ramchandran

As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.

no code implementations • 10 Sep 2017 • Somil Bansal, Roberto Calandra, Kurtland Chua, Sergey Levine, Claire Tomlin

Reinforcement Learning is divided in two main paradigms: model-free and model-based.

no code implementations • NeurIPS 2017 • Roel Dobbe, David Fridovich-Keil, Claire Tomlin

Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination.

no code implementations • 18 Jul 2017 • Eric Mazumdar, Roy Dong, Vicenç Rúbies Royo, Claire Tomlin, S. Shankar Sastry

We formulate a multi-armed bandit (MAB) approach to choosing expert policies online in Markov decision processes (MDPs).

Systems and Control

no code implementations • NeurIPS 2016 • Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen

We study a general adversarial online learning problem, in which we are given a decision set X' in a reflexive Banach space X and a sequence of reward vectors in the dual space of X.

no code implementations • 8 Nov 2016 • Vicenç Rubies Royo, Claire Tomlin

The majority of methods used to compute approximations to the Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) rely on the discretization of the state space to perform dynamic programming updates.

no code implementations • 21 Oct 2016 • Ye Yuan, Steven Low, Omid Ardakanian, Claire Tomlin

We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes).

no code implementations • 3 Jun 2016 • Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen

Under the assumption of uniformly continuous rewards, we obtain explicit anytime regret bounds in a setting where the decision set is the set of probability distributions on a compact metric space $S$ whose Radon-Nikodym derivatives are elements of $L^p(S)$ for some $p > 1$.

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