Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics
1 Learning \& Adaptive Systems Group, Department of Computer Science, ETH Zurich, Switzerland. 2 Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada.
Felix Berkenkamp, Andreas Krause, Angela P. Schoellig
Robotic algorithms typically depend on various parameters, the choice of which significantly affects the robot's performance. While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually on the real system to achieve the best performance. Optimization algorithms, such as Bayesian optimization, have been used to automate this process. However, these methods may evaluate unsafe parameters during the optimization process that lead to safety-critical system failures. Recently, a safe Bayesian optimization algorithm, called SafeOpt, has been developed, which guarantees that the performance of the system never falls below a critical value; that is, safety is defined based on the performance function. However, coupling performance and safety is often not desirable in robotics. For example, high-gain controllers might achieve low average tracking error (performance), but can overshoot and violate input constraints. In this paper, we present a generalized algorithm that allows for multiple safety constraints separate from the objective. Given an initial set of safe parameters, the algorithm maximizes performance but only evaluates parameters that satisfy safety for all constraints with high probability. To this end, it carefully explores the parameter space by exploiting regularity assumptions in terms of a Gaussian process prior. Moreover, we show how context variables can be used to safely transfer knowledge to new situations and tasks. We provide a theoretical analysis and demonstrate that the proposed algorithm enables fast, automatic, and safe optimization of tuning parameters in experiments on a quadrotor vehicle.
West Virginia University（西弗吉尼亚大学）；Ford Greenfield Labs（福特Greenfield实验室）；University of Michigan（密歇根大学）；Ford Research and Innovation Center（福特研究与创新中心）
In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches for safety assurance. Our safety system consists of two modules namely handcrafted safety and dynamically-learned safety. The handcrafted safety module is a heuristic safety rule based on common driving practice that ensure a minimum relative gap to a traffic vehicle. On the other hand, the dynamically-learned safety module is a data-driven safety rule that learns safety patterns from driving data. Specifically, the …
Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics
Department of Engineering, King’s College London（工程系，伦敦国王学院）；Department of Intelligent Systems, KTH Royal Institute of Technology（智能系统系，皇家理工学院）；Department of Automation Technology, ABB Corporate Research Sweden（自动化技术系，ABB瑞典公司）；Institute for Digital Communications, FriedrichAlexander-Universität Erlangen-Nürnberg（数字通信研究所，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）
Despite the successful demonstration of autonomous vehicles (AVs), such as self-driving cars, ensuring AV safety remains a challenging task. Although some actors influence an AV's driving decisions more than others, current approaches pay equal attention to each actor on the road. An actor's influence on the AV's decision can be characterized in terms of its ability to decrease the number of safe navigational choices for the AV. In this work, we propose a safety threat indicator (STI) using counterfactual reasoning to estimate the importance of each actor on the road with respect to its influe…
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；City University of Hong Kong（香港城市大学）；Institute of Unmanned Aerial Vehicle Systems, Lingchuan Industry（凌川工业无人机系统研究所）
School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子与信息工程学院）；School of Data and Computer Science, Sun Yat-sen University（中山大学数据与计算机学院）
Safety and tracking stability are crucial for safety-critical systems such as self-driving cars, autonomous mobile robots, industrial manipulators. To efficiently control safety-critical systems to ensure their safety and achieve tracking stability, accurate system dynamic models are usually required. However, accurate system models are not always available in practice. In this paper, a learning-based safety-stability-driven control (LBSC) algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model…
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Zhongguancun Laboratory（中关村实验室）；Beihang University（北京航空航天大学）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；A*STAR（新加坡科技研究局）
Digital Twin technology is playing a pivotal role in the modern industrial evolution. Especially, with the technological progress in the Internet-of-Things (IoT) and the increasing trend in autonomy, multi-sensor equipped robotics can create practical digital twin, which is particularly useful in the industrial applications for operations, maintenance and safety. Herein, we demonstrate a real-world digital twin of a safety-critical robotics applications with a Franka-Emika-Panda robotic arm. We develop and showcase an edge-assisted collaborative digital twin for dynamic obstacle avoidance whic…
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）；Avian Technologies Ltd（Avian Technologies有限公司）
The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this paper we discuss the state of the art in safety assurance, existing as well as emerging standards in this area, and the need for new approaches to safety assurance in the context of learning machines. We then focus on robotic learning from demonstration, the challenges these tech…
University of Southern California（南加州大学）；University of California, Berkeley（加州大学伯克利分校）
We present a novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the presence of model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on dynamic walking of a five-link planar bipedal robot subject to contact force constraints as well as safety-critical pr…
Department of Mechanical and Civil Engineering, Caltech（加州理工学院机械与土木工程系）；Department of Computing and Mathematical Sciences, Caltech（加州理工学院计算与数学科学系）；Department of Mechanical Engineering, Tufts University（塔夫茨大学机械工程系）
Robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions. This work aims to develop the mathematical underpinnings of such a representation -- specifically, the goal is to develop safety filters that are risk-aware. To this end, we take a two step approach: encoding an understanding of the environment via Poisson's equation, and associated risk via Laplace guidance fields. That is, we first solve a Dirichlet problem for Poisson's equation to generate a safety function that encodes system safety as its 0-superleve…
Predictive Safety Shield for Dyna-Q Reinforcement Learning
Departement U2IS, ENSTA Paris, Institut Polytechnique de Paris（ENSTA巴黎大学U2IS部门，巴黎理工学院）；Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA（加州大学伯克利分校电气工程与计算机科学系）
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California（南加州大学明希德电气与计算机工程系）；Department of Aeronautics and Astronautics, Stanford University（斯坦福大学航空与航天系）；Electrical and Computer Engineering Department, New York University（纽约大学电气与计算机工程系）
This paper presents a framework for the safety-critical control of robotic systems, when safety is defined on safe regions in the configuration space. To maintain safety, we synthesize a safe velocity based on control barrier function theory without relying on a -- potentially complicated -- high-fidelity dynamical model of the robot. Then, we track the safe velocity with a tracking controller. This culminates in model-free safety critical control. We prove theoretical safety guarantees for the proposed method. Finally, we demonstrate that this approach is application-agnostic. We execute an o…
Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters
Computing and Mathematical Sciences, California Institute of Technology（加州理工学院计算与数学科学系）；Mechanical and Civil Engineering, California Institute of Technology（加州理工学院机械与土木工程系）
Safety Aware Task Planning via Large Language Models in Robotics
University of Minnesota – Twin Cities（明尼苏达大学双城分校）；Institute for Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；Pontificia Universidad Católica de Chile（智利天主教大学）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Applied Dexterity（Applied Dexterity公司）；Rush University Medical Center（拉什大学医学中心）；Massachusetts Institute of Technology（麻省理工学院）
Robotic telesurgical systems are one of the most complex medical cyber-physical systems on the market, and have been used in over 1.75 million procedures during the last decade. Despite significant improvements in design of robotic surgical systems through the years, there have been ongoing occurrences of safety incidents during procedures that negatively impact patients. This paper presents an approach for systems-theoretic safety assessment of robotic telesurgical systems using software-implemented fault-injection. We used a systemstheoretic hazard analysis technique (STPA) to identify the p…
