CONLab Research

  • cvxRiskOpt: A package for risk-based optimization using CVXPY and CVXPYgen

    Sleiman Safaoui, Tyler H Summers Keywords: Computational methods, uncertain systems, optimization, code generation, risk-based optimization Overview Optimization modeling tools and parser solvers, such as CVXPY, make solving deterministic optimization problems a lot easier: they allow users to encode optimization problems in high-level mathematical expressions thereby reducing the need for tedious reformulations or fitting problems into canonical forms.…

  • Distributionally Robust CVaR-Based Safety Filtering for Motion Planning in Uncertain Environments

    Sleiman Safaoui, Tyler H Summers, ICRA 2024. Keywords: Risk-based planning, Wasserstein-based ambiguity set, DR-CVaR, Uncertain environments Overview Planning a trajectory for an autonomous robot in the presence of dynamics obstacles is a challenging problem. It involves having to predict how the obstacles will move and choosing a collision-free path. Modern motion planning solutions (e.g. ones based on…

  • Risk-Bounded Temporal Logic Control of Continuous-Time Stochastic Systems

    Sleiman Safaoui, Lars Lindemann, Iman Shames, Tyler H Summers, ACC 2022. Keywords: Risk-based control, stochastic systems, Temporal Logic Overview In a previous work, we studied the problem of designing discrete-time risk-bounded controls for linear systems with temporal logic specifications. Temporal logic specifications define spatial and temporal constraints to be enforced. Examples of spatial constraints: avoid sets (collision…

  • Permutation-Invariant Neural Networks for Reinforcement Learning

    Work by Yujin Tang and David Ha In a paper accepted to the upcoming NeurIPS 2021 conference, researchers at Google Brain created a reinforcement learning (RL) agent that uses a collection of sensory neural networks trained on segments of the observation space and uses an attention mechanism to communicate information between the sensory networks. The…

  • Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty

    Sleiman Safaoui*, Benjamin Gravell*, Venkatraman Renganathan*, Tyler H Summers, IEEE/RSJ IROS 2020, submitted. Keywords: Risk-based planning, stochastic systems, nonlinear systems, RRT Summary We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems. RRT* is a high-level sampling-based planning algorithm that is appealing thanks to its asymptotic guarantees of completeness (if a solution exists, it will…

  • Adversarial Training is Not Ready for Robot Learning

    Work by Mathias Lechner, Ramin Hasani, Radu Grosu, Daniela Rus, Thomas A. Henzinger This paper was recently accepted to ICRA 2021, posted to arXiv, and summarized in this VentureBeat post. Their research suggests that adversarial training of neural network-based image classifiers, when applied in robot learning settings, can have the opposite of the intended effect,…

  • Robust Learning-Based Control via Bootstrapped Multiplicative Noise

    Robust Learning-Based Control via Bootstrapped Multiplicative Noise Benjamin Gravell, Tyler Summers, L4DC 2020 Keywords: Optimal, robust, adaptive, control, reinforcement learning, system identification, stochastic parameters Summary We propose a robust adaptive control algorithm that explicitly accounts for inherent non-asymptotic uncertainties arising from models estimated with finite, noisy data. The algorithm has three components: (1) a least-squares nominal model estimator;…

  • Control Design for Risk-Based Signal Temporal Logic Specifications

    Sleiman Safaoui, Lars Lindemann, Dimos V Dimarogonas, Iman Shames, Tyler H Summers, IEEE L-CSS/CDC 2020 Keywords: Signal temporal logic, stochastic systems, constraint control, optimization Summary We present a framework for risk semantics on Signal Temporal Logic (STL) specifications for discrete-time linear dynamical systems with additive stochastic noise. Under our recursive risk semantics, risk constraints on STL formulas can be expressed…

  • Robust Linear Quadratic Regulator: Exact Tractable Reformulation

    Wouter Jongeneel, Tyler Summers, Peyman Mohajerin Esfahani, CDC 2019 Keywords: Optimal, robust, control, data, dynamic, game, Riccati, equation Summary We give novel characterizations of the uncertainty sets that arise in the robust linear quadratic regulator problem, develop Riccati equation-based solutions to optimal robust LQR problems over these sets, and give theoretical and empirical evidence that the resultant robust…

  • Learning robust control for LQR systems with multiplicative noise via policy gradient

    Benjamin Gravell, Peyman Mohajerin Esfahani, Tyler Summers, IEEE Transactions on Automatic Control (TAC) 2020 Keywords: Optimal, robust, control, reinforcement learning, policy, gradient, optimization, nonconvex, gradient domination, Polyak-Lojasiewicz, inequality, concentration, bound Summary We show that the linear quadratic regulator with multiplicative noise (LQRm) objective is gradient dominated, and thus applying policy gradient results in global convergence to the globally optimum control policy with polynomial…

  • Explorations in Dynamics

    We came across these interesting works that are somewhat tangential to our interests, but fascinating all the same. Physics in N dimensions Marc ten Bosch developed a formulation for rigid body dynamics that is independent of the dimension of the space, which is described using geometric algebra. An interesting issue that was also solved was…

  • Respect the Unstable

    Inaugural Bode Lecture by Dr. Gunter Stein, CDC, 1989 Keywords: Stability, robustness, fundamental limitations, Bode integral, sensitivity, waterbed effect Summary In this classic entertaining lecture delivered by Dr. Gunter Stein, fundamental limitations arising in control pertaining to stability, performance, and robustness are addressed. In particular, the Bode integral is advocated as a fundamental “conservation law”…

  • Multi-Sensor Fusion in Automated Driving: A Survey

    Work by Zhangjing Wang, Yu Wu, and Qingqing Niu, IEEE Access Volume: 8, 2020 Keywords: Multi Sensor Fusion, Autonomous Driving, Tracking, Data Association Summary The authors present a survey for multi-source and heterogeneous information fusion for autonomous driving vehicles. They discuss three main topics: Sensors and communications: identifying the most popular sensors and communication schemes…

  • Optimal Inequalities in Probability Theory: A Convex Optimization Approach

    Work by Dimitris Bertsimas & Ioana Popescu, SIAM Journal on Optimization, Volume: 15, Number: 3, Pages: 780-804, 2005 Keywords: Bounds in Probability Theory , Higher Order Moment Based SDP Summary The authors investigate the problem of obtaining best possible bounds on the probability that a certain random variable belongs in a given set, given information…

  • Metrics for Signal Temporal Logic Formulae

    Work by Curtis Madsen, Prashant Vaidyanathan, Sadra Sadraddini, Cristian-Ioan Vasile, Nicholas A. DeLateur, Ron Weiss, Douglas Densmore, and Calin Belta, IEEE CDC 2018 Keywords: Signal Temporal Logic, Metric Spaces Summary The authors discuss how STL formulae can admit a metric space under mild assumptions. They present two metrics: the Pompeiu-Hausdorff (PH) and the Symmetric Difference…

  • A Survey of Distributed Optimization

    Work by Tao Yang, et al., Annual Reviews in Control 2020 Discussion by Yi Guo, February 18, 2020 Keywords: Control review, distributed optimization, algorithm design Summary In distributed optimization of multi-agent systems, agents cooperate to minimize a global function which is a sum of local objective functions. Motivated by applications including power systems, sensor networks,…

  • Shrinking Horizon Model Predictive Control With Signal Temporal Logic Constraints Under Stochastic Disturbances

    Work by Samira S. Farahani, Rupak Majumdar, Vinayak S. Prabhu, and Sadegh Soudjani, IEEE Transactions on Automatic Control August 2019 Keywords: Signal temporal logic, model predictive control, stochastic disturbances Summary The authors discuss a shrinking horizon model predictive control (SH-MPC) problem to generate control inputs for a discrete-time linear system under additive stochastic disturbance (either…

  • Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak- Lojasiewicz Condition

    Work by Hamed Karimi, Julie Nutini, and Mark Schmidt, ECML PKDD 2016 Keywords: Nonconvex, optimization, Polyak-Lojasiewicz inequality, gradient domination, convergence, global Summary The authors re-explore the Polyak-Lojasiewicz inequality first analyzed by Polyak in 1963 by deriving convergence results under various descent methods for various modern machine learning tasks and establishing equivalences and sufficiency-necessity relationships with…

  • Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem

    Work by Hesameddin Mohammadi, Armin Zare, Mahdi Soltanolkotabi, and Mihailo R. Jovanovic, IEEE TAC 2020 (under review) / CDC 2019 Keywords: Data-driven control, gradient descent, gradient-flow dynamics, linear quadratic regulator, model-free control, nonconvex optimization, Polyak-Lojasiewicz inequality, random search method, reinforcement learning, sample complexity Summary This work extends results on convergence of policy gradient methods for discrete-time…

  • Intro to Reinforcement Learning

    Summary In this tutorial we walk through a basic introduction to reinforcement learning.

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