Search Results for author: Aaron D. Ames

Found 55 papers, 13 papers with code

Characterizing Smooth Safety Filters via the Implicit Function Theorem

no code implementations22 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.

Composing Control Barrier Functions for Complex Safety Specifications

no code implementations13 Sep 2023 Tamas G. Molnar, Aaron D. Ames

The increasing complexity of control systems necessitates control laws that guarantee safety w. r. t.

On the Safety of Connected Cruise Control: Analysis and Synthesis with Control Barrier Functions

1 code implementation31 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.

Input-to-State Stability in Probability

no code implementations28 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.

Intermittent Safety Filters for Event-Triggered Safety Maneuvers with Application to Satellite Orbit Transfers

no code implementations18 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.

Violation-Free Inter-Sampling Safety: from Control Barrier Functions to Tunable Controllers with Input-to-State Safety Guarantees

no code implementations18 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.

Lipschitz Continuity of Signal Temporal Logic Robustness Measures: Synthesizing Control Barrier Functions from One Expert Demonstration

no code implementations7 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.

Open-Ended Question Answering

Parameterized Barrier Functions to Guarantee Safety under Uncertainty

no code implementations20 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.

Safety-Critical Control with Bounded Inputs via Reduced Order Models

no code implementations6 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.

Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions

no code implementations15 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.

End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions

1 code implementation21 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).

Imitation Learning

Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data

no code implementations12 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.

Safety-Critical Controller Verification via Sim2Real Gap Quantification

no code implementations19 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.

Safe Drone Flight with Time-Varying Backup Controllers

no code implementations11 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.

Control Barrier Functionals: Safety-critical Control for Time Delay Systems

no code implementations16 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.

Input-to-State Safety with Input Delay in Longitudinal Vehicle Control

no code implementations29 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.

Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification

no code implementations21 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.

Stability and Safety through Event-Triggered Intermittent Control with Application to Spacecraft Orbit Stabilization

no code implementations6 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.

Safe Backstepping with Control Barrier Functions

no code implementations1 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.

Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions

1 code implementation1 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.

Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk Control Barrier Functions

no code implementations29 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.

Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models

no code implementations22 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.

A Scenario Approach to Risk-Aware Safety-Critical System Verification

no code implementations4 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.

A Barrier-Based Scenario Approach to Verify Safety-Critical Systems

no code implementations22 Feb 2022 Prithvi Akella, Aaron D. Ames

In this letter, we detail our randomized approach to safety-critical system verification.

LyaNet: A Lyapunov Framework for Training Neural ODEs

1 code implementation5 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.

Adversarial Robustness

Onboard Safety Guarantees for Racing Drones: High-speed Geofencing with Control Barrier Functions

no code implementations12 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.

Test and Evaluation of Quadrupedal Walking Gaits through Sim2Real Gap Quantification

no code implementations4 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.

Bayesian Optimization

Safety-Critical Control with Input Delay in Dynamic Environment

no code implementations15 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.

Disturbance Bounds for Signal Temporal Logic Task Satisfaction: A Dynamics Perspective

no code implementations22 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.

Interactive multi-modal motion planning with Branch Model Predictive Control

1 code implementation10 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.

Autonomous Vehicles Motion Planning

Risk-Averse Decision Making Under Uncertainty

no code implementations9 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.

Decision Making Decision Making Under Uncertainty

Learning Performance Bounds for Safety-Critical Systems

no code implementations9 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.

Bayesian Optimization Translation

Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State

1 code implementation28 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.

Backup Control Barrier Functions: Formulation and Comparative Study

no code implementations22 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.

Iterative Model Predictive Control for Piecewise Systems

no code implementations16 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.

Risk-Averse Stochastic Shortest Path Planning

no code implementations26 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.

Unified Multi-Rate Control: from Low Level Actuation to High Level Planning

1 code implementation11 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.

Constrained Risk-Averse Markov Decision Processes

no code implementations4 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.

Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty

no code implementations21 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.

Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots

1 code implementation10 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.

Reactive motion planning with probabilistic safety guarantees

no code implementations6 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.

Autonomous Vehicles Motion Planning

Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

1 code implementation30 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.

Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays

no code implementations22 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.

Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

1 code implementation11 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.


Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

1 code implementation13 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.

Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator

no code implementations8 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.

Partially Observable Games for Secure Autonomy

no code implementations5 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.

Decision Making

Risk-Averse Planning Under Uncertainty

no code implementations27 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.

Dynamic Walking with Compliance on a Cassie Bipedal Robot

no code implementations25 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.


A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability

no code implementations18 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.

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

no code implementations4 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.

Traversing Environments Using Possibility Graphs for Humanoid Robots

no code implementations12 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.

Motion Planning

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