no code implementations • 22 Sep 2023 • Max H. Cohen, Pio Ong, Gilbert Bahati, Aaron D. Ames
Optimization-based safety filters, such as control barrier function (CBF) based quadratic programs (QPs), have demonstrated success in controlling autonomous systems to achieve complex goals.
no code implementations • 13 Sep 2023 • Tamas G. Molnar, Aaron D. Ames
The increasing complexity of control systems necessitates control laws that guarantee safety w. r. t.
1 code implementation • 31 Aug 2023 • Tamas G. Molnar, Gabor Orosz, Aaron D. Ames
Finally, we combine the analysis of safety measures and the corresponding stability charts to synthesize safety-critical connected cruise controllers using CBFs.
no code implementations • 28 Apr 2023 • Preston Culbertson, Ryan K. Cosner, Maegan Tucker, Aaron D. Ames
Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances.
no code implementations • 18 Apr 2023 • Pio Ong, Aaron D. Ames
Our first trigger in the scheme monitors safety constraints encoded by barrier functions, and thereby ensures safety without the need to alter the nominal controller--and when the boundary of the safety constraint is approached, the controller drives the system to the region where control actions are not needed.
no code implementations • 18 Apr 2023 • Gilbert Bahati, Pio Ong, Aaron D. Ames
A common assumption on the deployment of safeguarding controllers on the digital platform is that high sampling frequency translates to a small violation of safety.
no code implementations • 7 Apr 2023 • Prithvi Akella, Apurva Badithela, Richard M. Murray, Aaron D. Ames
By filtering control inputs to maintain the positivity of this function, we ensure that the system trajectory satisfies the desired STL specification.
no code implementations • 20 Mar 2023 • Anil Alan, Tamas G. Molnar, Aaron D. Ames, Gábor Orosz
In this context, robust control barrier functions -- in a variety of forms -- have been used to obtain safety guarantees for uncertain systems.
no code implementations • 6 Mar 2023 • Tamas G. Molnar, Aaron D. Ames
Guaranteeing safe behavior on complex autonomous systems -- from cars to walking robots -- is challenging due to the inherently high dimensional nature of these systems and the corresponding complex models that may be difficult to determine in practice.
no code implementations • 15 Feb 2023 • Ryan K. Cosner, Preston Culbertson, Andrew J. Taylor, Aaron D. Ames
To this end, we leverage Control Barrier Functions (CBFs) which guarantee that a robot remains in a ``safe set'' during its operation -- yet CBFs (and their associated guarantees) are traditionally studied in the context of continuous-time, deterministic systems with bounded uncertainties.
1 code implementation • 21 Dec 2022 • Ryan K. Cosner, Yisong Yue, Aaron D. Ames
Imitation learning (IL) is a learning paradigm which can be used to synthesize controllers for complex systems that mimic behavior demonstrated by an expert (user or control algorithm).
no code implementations • 12 Dec 2022 • Prithvi Akella, Skylar X. Wei, Joel W. Burdick, Aaron D. Ames
Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face.
no code implementations • 19 Sep 2022 • Prithvi Akella, Wyatt Ubellacker, Aaron D. Ames
To develop more useful models, we quantify the inaccuracy with which a given model represents a system of interest, so that we may leverage this quantity to facilitate controller synthesis and verification.
no code implementations • 16 Sep 2022 • Anil Alan, Tamas G. Molnar, Ersin Das, Aaron D. Ames, Gabor Orosz
This work provides formal safety guarantees for control systems with disturbance.
no code implementations • 11 Jul 2022 • Andrew Singletary, Aiden Swann, Ivan Dario Jimenez Rodriguez, Aaron D. Ames
TBCs reduce conservatism when compared to traditional backup controllers and can be directly applied to multi-agent coordination to guarantee safety.
no code implementations • 16 Jun 2022 • Adam K. Kiss, Tamas G. Molnar, Aaron D. Ames, Gabor Orosz
The theory of control barrier functions, that provides formal safety guarantees for delay-free systems, is extended to systems with state delay.
no code implementations • 29 May 2022 • Tamas G. Molnar, Anil Alan, Adam K. Kiss, Aaron D. Ames, Gabor Orosz
Safe longitudinal control is discussed for a connected automated truck traveling behind a preceding connected vehicle.
no code implementations • 21 Apr 2022 • Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi, Joel W. Burdick, Aaron D. Ames
The dramatic increase of autonomous systems subject to variable environments has given rise to the pressing need to consider risk in both the synthesis and verification of policies for these systems.
no code implementations • 6 Apr 2022 • Pio Ong, Gilbert Bahati, Aaron D. Ames
Motivated by the event-triggered control paradigm, where state-dependent triggers are utilized in a sample-and-hold context, we generalize this concept to include state triggers where the controller is off thereby creating a framework for intermittent control.
no code implementations • 1 Apr 2022 • Andrew J. Taylor, Pio Ong, Tamas G. Molnar, Aaron D. Ames
Complex control systems are often described in a layered fashion, represented as higher-order systems where the inputs appear after a chain of integrators.
1 code implementation • 1 Apr 2022 • Noel Csomay-Shanklin, Andrew J. Taylor, Ugo Rosolia, Aaron D. Ames
Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with different controllers running at multiple time scales.
no code implementations • 29 Mar 2022 • Andrew Singletary, Mohamadreza Ahmadi, Aaron D. Ames
To this end, we introduce risk control barrier functions (RCBFs), which are compositions of barrier functions and dynamic, coherent risk measures.
no code implementations • 22 Mar 2022 • Andrew J. Taylor, Victor D. Dorobantu, Ryan K. Cosner, Yisong Yue, Aaron D. Ames
Existing design paradigms do not address the gap between theory (controller design with continuous time models) and practice (the discrete time sampled implementation of the resulting controllers); this can lead to poor performance and violations of safety for hardware instantiations.
no code implementations • 4 Mar 2022 • Prithvi Akella, Mohamadreza Ahmadi, Aaron D. Ames
With the growing interest in deploying robots in unstructured and uncertain environments, there has been increasing interest in factoring risk into safety-critical control development.
no code implementations • 22 Feb 2022 • Prithvi Akella, Aaron D. Ames
In this letter, we detail our randomized approach to safety-critical system verification.
1 code implementation • 5 Feb 2022 • Ivan Dario Jimenez Rodriguez, Aaron D. Ames, Yisong Yue
Our approach, called LyaNet, is based on a novel Lyapunov loss formulation that encourages the inference dynamics to converge quickly to the correct prediction.
no code implementations • 12 Jan 2022 • Andrew Singletary, Aiden Swann, Yuxiao Chen, Aaron D. Ames
This paper details the theory and implementation behind practically ensuring safety of remotely piloted racing drones.
no code implementations • 4 Jan 2022 • Prithvi Akella, Wyatt Ubellacker, Aaron D. Ames
This dual approach has the added benefit of quantifying the Sim2Real Gap between a system simulator and its hardware counterpart.
no code implementations • 15 Dec 2021 • Tamas G. Molnar, Adam K. Kiss, Aaron D. Ames, Gábor Orosz
Endowing nonlinear systems with safe behavior is increasingly important in modern control.
no code implementations • 22 Oct 2021 • Prithvi Akella, Aaron D. Ames
This letter offers a novel approach to Test and Evaluation of pre-existing controllers from a control barrier function and dynamics perspective.
1 code implementation • 10 Sep 2021 • Yuxiao Chen, Ugo Rosolia, Wyatt Ubellacker, Noel Csomay-Shanklin, Aaron D. Ames
Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered.
no code implementations • 9 Sep 2021 • Mohamadreza Ahmadi, Ugo Rosolia, Michel D. Ingham, Richard M. Murray, Aaron D. Ames
In this paper, we consider the problem of designing policies for MDPs and POMDPs with objectives and constraints in terms of dynamic coherent risk measures, which we refer to as the constrained risk-averse problem.
no code implementations • 9 Sep 2021 • Prithvi Akella, Ugo Rosolia, Aaron D. Ames
As a result, the test and evaluation ideal would be to verify the efficacy of a system simulator and use this verification result to make a statement on true system performance.
1 code implementation • 28 Apr 2021 • Ryan K. Cosner, Andrew W. Singletary, Andrew J. Taylor, Tamas G. Molnar, Katherine L. Bouman, Aaron D. Ames
The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements.
no code implementations • 22 Apr 2021 • Yuxiao Chen, Mrdjan Jankovic, Mario Santillo, Aaron D. Ames
The backup control barrier function (CBF) was recently proposed as a tractable formulation that guarantees the feasibility of the CBF quadratic programming (QP) via an implicitly defined control invariant set.
no code implementations • 16 Apr 2021 • Ugo Rosolia, Aaron D. Ames
First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time.
no code implementations • 26 Mar 2021 • Mohamadreza Ahmadi, Anushri Dixit, Joel W. Burdick, Aaron D. Ames
We consider the stochastic shortest path planning problem in MDPs, i. e., the problem of designing policies that ensure reaching a goal state from a given initial state with minimum accrued cost.
1 code implementation • 8 Mar 2021 • Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model.
1 code implementation • 11 Dec 2020 • Ugo Rosolia, Andrew Singletary, Aaron D. Ames
In this paper we present a hierarchical multi-rate control architecture for nonlinear autonomous systems operating in partially observable environments.
no code implementations • 4 Dec 2020 • Mohamadreza Ahmadi, Ugo Rosolia, Michel D. Ingham, Richard M. Murray, Aaron D. Ames
We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints.
no code implementations • 21 Nov 2020 • Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean, Benjamin Recht, Yisong Yue, Aaron D. Ames
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains.
1 code implementation • 10 Nov 2020 • Maegan Tucker, Noel Csomay-Shanklin, Wen-Loong Ma, Aaron D. Ames
This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning.
1 code implementation • 9 Nov 2020 • Kejun Li, Maegan Tucker, Erdem Biyik, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters.
no code implementations • 6 Nov 2020 • Yuxiao Chen, Ugo Rosolia, Chuchu Fan, Aaron D. Ames, Richard Murray
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots.
1 code implementation • 30 Oct 2020 • Sarah Dean, Andrew J. Taylor, Ryan K. Cosner, Benjamin Recht, Aaron D. Ames
The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions.
no code implementations • 22 Sep 2020 • Tamas G. Molnar, Andrew W. Singletary, Gabor Orosz, Aaron D. Ames
We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions (such as quarantining or social distancing) as control input.
1 code implementation • 11 Apr 2020 • Richard Cheng, Mohammad Javad Khojasteh, Aaron D. Ames, Joel W. Burdick
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles.
1 code implementation • 13 Mar 2020 • Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space.
no code implementations • 8 Feb 2020 • Chang Gao, Rachel Gehlhar, Aaron D. Ames, Shih-Chii Liu, Tobi Delbruck
Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behaviors based upon the human's experience.
no code implementations • 5 Feb 2020 • Mohamadreza Ahmadi, Arun A. Viswanathan, Michel D. Ingham, Kymie Tan, Aaron D. Ames
Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade.
no code implementations • 27 Sep 2019 • Mohamadreza Ahmadi, Masahiro Ono, Michel D. Ingham, Richard M. Murray, Aaron D. Ames
We consider the problem of designing policies for partially observable Markov decision processes (POMDPs) with dynamic coherent risk objectives.
no code implementations • 25 Apr 2019 • Jacob Reher, Wen-Loong Ma, Aaron D. Ames
The control of bipedal robotic walking remains a challenging problem in the domains of computation and experiment, due to the multi-body dynamics and various sources of uncertainty.
Robotics
no code implementations • 18 Mar 2019 • Andrew J. Taylor, Victor D. Dorobantu, Meera Krishnamoorthy, Hoang M. Le, Yisong Yue, Aaron D. Ames
The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective.
no code implementations • 4 Mar 2019 • Andrew J. Taylor, Victor D. Dorobantu, Hoang M. Le, Yisong Yue, Aaron D. Ames
Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics.
no code implementations • 12 Aug 2016 • Michael X. Grey, Aaron D. Ames, C. Karen Liu
Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments.