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 precise foot placements on stepping stones, all while subject to model uncertainty. We conduct preliminary experimental validation of the proposed controller on a rectilinear spring-cart system under different types of model uncertainty and perturbations.
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…
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）
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…
University of Wollongong（伍伦贡大学）；Lebanese American University（黎巴嫩美国大学）
This paper proposes a new variable stiffness soft gripper that enables high-performance grasping tasks in industrial applications. The design of the proposed monolithic soft gripper includes a middle bellow and two side bellows (i.e., fingers). The positions of the fingers are regulated by adjusting the negative pressure in the middle bellow actuator via an on-off controller. The stiffness of the soft gripper is modulated by controlling the positive pressure in the fingers through the use of a proportional air-pressure regulator. It is experimentally shown that the proposed soft gripper can mo…
Over the decades, kinematic controllers have proven to be practically useful for applications like set-point and trajectory tracking in robotic systems. To this end, we formulate a novel safety-critical paradigm for kinematic control in this paper. In particular, we extend the methodology of control barrier functions (CBFs) to kinematic equations governing robotic systems. We demonstrate a purely kinematic implementation of a velocity-based CBF, and subsequently introduce a formulation that guarantees safety at the level of dynamics. This is achieved through a new form CBFs that incorporate ki…
A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
Department of Mechanical and Process Engineering, ETH Zurich（瑞士苏黎世联邦理工学院机械与过程工程系）；Clearpath Robotics（Clearpath机器人公司）；Electronic Frontier Foundation（电子前沿基金会）
Robust Adaptive Safe Robotic Grasping with Tactile Sensing
Maersk Mc-Kinney Moller Institute, University of Southern Denmark（马士基麦金尼默勒研究所，丹麦南方大学）；Departments of Mechanical and Civil Engineering and Computing and Mathematical Sciences , California Institute of Technology（机械与土木工程系和计算与数学科学系，加州理工学院）
Safety-Ensured Robotic Control Framework for Cutting Task Automation in Endoscopic Submucosal Dissection
Division of Mechanical System Engineering, Myongji University, Yongin-si, Gyeonggi-do, Republic of Korea（机械系统工程系，明州大学，韩国京畿道 Yongin市）；SDU Robotics, the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark（SDU机器人，马士克麦金尼莫勒研究所，丹麦南部大学，Odense）；Department of Mechanical Engineering, Myongji University, Yongin-si, Gyeonggi-do, Republic of Korea（机械工程系，明州大学，韩国京畿道 Yongin市）
Shanghai Jiao Tong University（上海交通大学）；University of Oxford（牛津大学）；Tencent Robotics X Lab（腾讯机器人X实验室）
Designing safety-critical control for robotic manipulators is challenging, especially in a cluttered environment. First, the actual trajectory of a manipulator might deviate from the planned one due to the complex collision environments and non-trivial dynamics, leading to collision; Second, the feasible space for the manipulator is hard to obtain since the explicit distance functions between collision meshes are unknown. By analyzing the relationship between the safe set and the controlled invariant set, this paper proposes a data-driven control barrier function (CBF) construction method, whi…
On the Robotic Uncertainty of Fully Autonomous Traffic: From Stochastic Car-Following to Mobility-Safety Trade-Offs
organization= Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science；organization= Department of Civil \& Environmental Engineering, University of Wisconsin-Madison , city= Madison , postcode= 53706 , state= Wisconsin , country= United States
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.
Control Barrier Functions (CBF) are widely used to enforce the safety-critical constraints on nonlinear systems. Recently, these functions are being incorporated into a path planning framework to design safety-critical path planners. However, these methods fall short of providing a realistic path considering both the algorithm's run-time complexity and enforcement of the safety-critical constraints. This paper proposes a novel motion planning approach using the well-known Rapidly Exploring Random Trees (RRT) algorithm that enforces both CBF and the robot Kinodynamic constraints to generate a s…
A Verification Methodology for Safety Assurance of Robotic Autonomous Systems
Faculty of Science and Technology, Norwegian University Of Life Sciences (NMBU)（科学与技术学院，挪威生命科学大学）；Department of Mechatronics, University of Agder (UiA)（机械电子系，阿格德大学）；Department of Computer Science, University Of York, UK（计算机科学系，约克大学（英国））
California Institute of Technology（加州理工学院）；Miso Robotics（味噌机器人公司）
Recent advances allow for the automation of food preparation in high-throughput environments, yet the successful deployment of these robots requires the planning and execution of quick, robust, and ultimately collision-free behaviors. In this work, we showcase a novel framework for modifying previously generated trajectories of robotic manipulators in highly detailed and dynamic collision environments using Control Barrier Functions (CBFs). This method dynamically re-plans previously validated behaviors in the presence of changing environments -- and does so in a computationally efficient mann…
Institute for Aerospace Studies, University of Toronto（多伦多大学航空航天研究所）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Vector Institute for Artificial Intelligence（向量人工智能研究院）
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. Our review includes: learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforce…
Digital Modeling of Massage Techniques and Reproduction by Robotic Arms
Shanghai Jiao Tong University（上海交通大学）；Meta Robotics Institute, Shanghai Jiao Tong University（上海交通大学元机器人研究所）；University of Shanghai for Science and Technology（上海理工大学）；Gongli Hospital Medical Technology（Gongli医院医疗技术学院）
Birla Institute of Technology and Science (BITS)（比尔拉理工学院）；Georgia Institute of Technology（佐治亚理工学院）
Control barrier functions are mathematical constructs used to guarantee safety for robotic systems. When integrated as constraints in a quadratic programming optimization problem, instantaneous control synthesis with real-time performance demands can be achieved for robotics applications. Prevailing use has assumed full knowledge of the safety barrier functions, however there are cases where the safe regions must be estimated online from sensor measurements. In these cases, the corresponding barrier function must be synthesized online. This paper describes a learning framework for estimating c…
Emerging applications in robotics and autonomous systems, such as autonomous driving and robotic surgery, often involve critical safety constraints that must be satisfied even when information about system models is limited. In this regard, we propose a model-free safety specification method that learns the maximal probability of safe operation by carefully combining probabilistic reachability analysis and safe reinforcement learning (RL). Our approach constructs a Lyapunov function with respect to a safe policy to restrain each policy improvement stage. As a result, it yields a sequence of sa…
Networked robotic systems, such as connected vehicle platoons, can improve the safety and efficiency of transportation networks by allowing for high-speed coordination. To enable such coordination, these systems rely on networked communications. This can make them susceptible to cyber attacks. Though security methods such as encryption or specially designed network topologies can increase the difficulty of successfully executing such an attack, these techniques are unable to guarantee secure communication against an attacker. More troublingly, these security methods are unable to ensure that i…
