Many robotic tasks, such as human-robot interactions or the handling of fragile objects, require tight control and limitation of appearing forces and moments alongside sensible motion control to achieve safe yet high-performance operation. We propose a learning-supported model predictive force and motion control scheme that provides stochastic safety guarantees while adapting to changing situations. Gaussian processes are used to learn the uncertain relations that map the robot's states to the forces and moments. The model predictive controller uses these Gaussian process models to achieve precise motion and force control under stochastic constraint satisfaction. As the uncertainty only occurs in the static model parts -- the output equations -- a computationally efficient stochastic MPC formulation is used. Analysis of recursive feasibility of the optimal control problem and convergence of the closed loop system for the static uncertainty case are given. Chance constraint formulation and back-offs are constructed based on the variance of the Gaussian process to guarantee safe operation. The approach is illustrated on a lightweight robot in simulations and experiments.
A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
department of Cognitive Robotics, Delft University of Technology（认知机器人系，代尔夫特理工大学）；Institute for Dynamic Systems and Control, ETH Zürich（动态系统与控制研究所，苏黎世联邦理工学院）
Leveraging the Template and Anchor Framework for Safe, Online Robotic Gait Design
Robotics Institute, University of Michigan（密歇根大学机器人研究所）；Department of Mechanical Engineering, University of Michigan（密歇根大学机械工程系）；Department of Naval Architecture and Marine Engineering, University of Michigan（密歇根大学 naval architecture and marine engineering系）
Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats
KU Leuven（鲁汶大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Nanyang Technological University（南洋理工大学）；SMART（新加坡制造技术研究院）；SENSEable City Laboratory, Massachusetts Institute of Technology（麻省理工学院可感知城市实验室）；Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
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）
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.
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）
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）
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
近年来，强化学习和基于学习的控制-以及对它们的安全性的研究，这对于在现实世界的机器人中部署至关重要-都获得了巨大的吸引力。然而，为了充分衡量新结果的进展和适用性，我们需要工具来公平地比较控制和强化学习社区提出的方法。在这里，我们提出了一个新的开源基准套件，称为安全控制健身房，支持基于模型和基于数据的控制技术。我们提供了三个动态系统的实现-车杆，1D和2D四旋翼-和两个控制任务-稳定和轨迹跟踪。我们建议扩展OpenAI的Gym API -强化学习研究中的事实标准-（i）指定（和查询）符号动力学的能力和（ii）约束，以及（iii）（可重复）在控制输入，状态测量和惯性属性中注入模拟干扰。为了证明我们的建议，并试图使研究社区更紧密地联系在一起，我们展示了如何使用安全控制健身房定量比较控制性能，数据效率和安全性的多种方法从传统的控制，基于学习的控制，和强化学习的领域。
Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Singapore Institute of Manufacturing Technology, A*STAR（新加坡制造技术研究院（新加坡科研局））；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电气与计算机工程系）；Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）机器人与自主系统学部）
本文提出一种基于学习的控制优化方法，通过核选择增强加性高斯过程的安全贝叶斯优化算法，高效解决高维复杂系统的多控制器优化问题，并在Safe Control Gym基准的无人机PID控制中验证了其优于现有技术的显著改进。
Systems-theoretic Safety Assessment of Robotic Telesurgical Systems
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）
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）
