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Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains

IEEE robotics AND automation LETTERS 2021年第4期6卷 6250-6257页

作者： Puranic, Aniruddh Gopinath V. Deshmukh, Jyotirmoy Nikolaidis, Stefanos Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA

Learning control policies that are safe, robust and interpretable are prominent challenges in developing robotic systems. Learning-from-demonstrations with formal logic is an arising paradigm in reinforcement learning to estimate rewards and extract robot control policies that seek to overcome these challenges. In this approach, we assume that mission-level specifications for the robotic system are expressed in a suitable temporal logic such as Signal Temporal Logic (STL). The main idea is to automatically infer rewards from user demonstrations (that could be suboptimal or incomplete) by evaluating and ranking them w.r.t. the given STL specifications. In contrast to existing work that focuses on deterministic environments and discrete state spaces, in this letter, we propose significant extensions that tackle stochastic environments and continuous state spaces.

关键词： Learning from demonstration probabilistic inference formal methods in robotics and automation

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On Provably Safe and Live Multirobot Coordination With Online Goal Posting

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IEEE TRANSACTIONS ON robotics 2021年第6期37卷 1973-1991页

作者： Mannucci, Anna Pallottino, Lucia Pecora, Federico Univ Pisa Res Ctr E Piaggio Pisa Italy Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Orebro Univ Ctr Appl Autonomous Sensor Syst AASS S-70281 Orebro Sweden

A standing challenge in multirobot systems is to realize safe and efficient motion planning and coordination methods that are capable of accounting for uncertainties and contingencies. The challenge is rendered harder by the fact that robots may be heterogeneous and that their plans may be posted asynchronously. Most existing approaches require constraints on the infrastructure or unrealistic assumptions on robot models. In this article, we propose a centralized, loosely-coupled supervisory controller that overcomes these limitations. The approach responds to newly posed constraints and uncertainties during trajectory execution, ensuring at all times that planned robot trajectories remain kinodynamically feasible, that the fleet is in a safe state, and that there are no deadlocks or livelocks. This is achieved without the need for hand-coded rules, fixed robot priorities, or environment modification. We formally state all relevant properties of robot behavior in the most general terms possible, without assuming particular robot models or environments, and provide both formal and empirical proof that the proposed fleet control algorithms guarantee safety and liveness.

关键词： Robots Robot kinematics Trajectory Collision avoidance System recovery Safety Task analysis formal methods in robotics and automation intelligent and flexible manufacturing multirobot systems planning scheduling and coordination

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Autonomous Decision-Making With Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning

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IEEE robotics AND automation LETTERS 2021年第4期6卷 8357-8362页

作者： Vilchis-Medina, Jose-Luis Godary-Dejean, Karen Lesire, Charles Univ Toulouse ONERA DTIS F-31000 Toulouse France Univ Montpellier LIRMM Explore F-34000 Montpellier France

In this article we propose a decision process integrating Non-Monotonic Reasoning (NMR), embedded in a deliberative architecture. The NMR process uses Default Logic to implement goal reasoning, managing partially observable or incomplete information, allowing the design of default behaviours completed by the handling of specific situations, in order to manage the current mission objective as well as safety rules. We illustrate our approach through an application of an underwater robot performing a marine biology mission.

关键词： AI-based methods formal methods in robotics and automation cognitive control architectures marine robotics

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On Null Space-Based Inverse Kinematics Techniques for Fleet Management: Toward Time-Varying Task Activation

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IEEE TRANSACTIONS ON robotics 2021年第1期37卷 257-274页

作者： Mannucci, Anna Caporale, Danilo Pallottino, Lucia Univ Pisa Res Ctr E Piaggio I-56126 Pisa Italy Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Orebro Univ Multirobot Planning & Control Lab Ctr Appl Autonomous Sensor Syst AASS S-70281 Orebro Sweden

Multirobot fleets play an important role in industrial logistics, surveillance, and exploration applications. A wide literature exists on the topic, both resorting to reactive (i.e. collision avoidance) and to deliberative (i.e. motion planning) techniques. In this work, null space-based inverse kinematics (NSB-IK) methods are applied to the problem of fleet management. Several NSB-IK approaches existing in the literature are reviewed, and compared with a reverse priority approach, which originated in manipulator control, and is here applied for the first time to the considered problem. All NSB-IK approaches are here described in a unified formalism, which allows (i) to encode the property of each controller into a set of seven main key features, (ii) to study possible new control laws with an opportune choice of these parameters. Furthermore, motivated by the envisioned application scenario, we tackle the problem of task-switching activation. Leveraging on the iCAT TPC technique Simetti and Casalino, 2016, in this article, we propose a method to obtain continuity in the control in face of activation or deactivation of tasks, and subtasks by defining suitable damped projection operators. The proposed approaches are evaluated formally, and via simulations. Performances with respect to standard methods are compared considering a specific case study for multivehicles management.

关键词： Task analysis Kinematics Manipulators Robot kinematics Standards Trajectory formal methods in robotics and automation motion control multirobot systems path planning for multiple mobile robots or agents

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Encoding Human Driving Styles in Motion Planning for Autonomous Vehicles

Encoding Human Driving Styles in Motion Planning for Autonom...

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IEEE International Conference on robotics and automation (ICRA)

作者： Karlsson, Jesper van Waveren, Sanne Pek, Christian Torre, Ilaria Leite, Iolanda Tumova, Jana KTH Royal Inst Technol Stockholm Div Robot Percept & Learning Stockholm Sweden

ISBN: (纸本)9781728190778

Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style.

关键词： Autonomous Vehicle Navigation formal methods in robotics and automation Human Factors and Human-in-the-Loop

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A Framework for formal Verification of Behavior Trees With Linear Temporal Logic

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2341-2348页

作者： Biggar, Oliver Zamani, Mohammad Def Sci & Technol Grp Land Div Canberra NSW 3124 Australia

Despite the current increasing popularity of Behavior Trees (BTs) in the robotics community, there does not currently exist a method to formally verify their correctness without compromising their most valuable traits: modularity, flexibility and reusability. In this letter we present a new mathematical framework in which we formally express Behavior Trees in Linear Temporal Logic (LTL). We show how this framework equivalently represents classical BTs. Then we utilize the proposed framework to construct an algorithm to verify that a given BT satisfies a given LTL specification. We prove that this algorithm is sound. Importantly, we prove that this method does not compromise the flexible design process of BTs, i.e. changes to subtrees can be verified separately and their combination can be assured to be correct. We present an example of the proposed algorithm in use.

关键词： formal methods in robotics and automation control architectures and programming

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Point-Based Policy Synthesis for POMDPs With Boolean and Quantitative Objectives

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1860-1867页

作者： Wang, Yue Chaudhuri, Swarat Kavraki, Lydia E. Rice Univ Dept Comp Sci Houston TX 77005 USA

Effectively planning robust executions under uncertainty is critical for building autonomous robots. Partially observable Markov decision processes (POMDPs) provide a standard framework for modeling many robot applications under uncertainty. We study POMDPs with two kinds of objectives: (1) Boolean objectives for a correctness guarantee of accomplishing tasks and (2) quantitative objectives for optimal behaviors. For robotic domains that require both correctness and optimality, POMDPs with Boolean and quantitative objectives are natural formulations. We present a practical policy synthesis approach for POMDPs with Boolean and quantitative objectives by combining policy iteration and policy synthesis for POMDPs with only Boolean objectives. To improve efficiency, our approach produces approximate policies by performing the point-based backup on a small set of representative beliefs. Despite being approximate, our approach maintains validity (satisfying Boolean objectives) and guarantees improved policies at each iteration before termination. Moreover, the error due to approximation is bounded. We evaluate our approach in several robotic domains. The results show that our approach produces good approximate policies that guarantee task completion.

关键词： formal methods in robotics and automation Task Planning Motion and Path Planning

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Provably Safe Multi-Robot Coordination With Unreliable Communication

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3232-3239页

作者： Mannucci, Anna Pallottino, Lucia Pecora, Federico Univ Pisa Res Ctr E Piaggio I-56122 Pisa Italy Orebro Univ Ctr Appl Autonomous Sensor Syst SE-70182 Orebro Sweden

Coordination is a core problem in multi-robot systems, since it is a key to ensure safety and efficiency. Both centralized and decentralized solutions have been proposed, however, most assume perfect communication. This letter proposes a centralized method that removes this assumption, and is suitable for fleets of robots driven by generic second-order dynamics. We formally prove that: first, safety is guaranteed if communication errors are limited to delays;and second, the probability of unsafety is bounded by a function of the channel model in networks with packet loss. The approach exploits knowledge of the network's non-idealities to ensure the best possible performance of the fleet. The method is validated via several experiments with simulated robots.

关键词： Multi-robot systems planning scheduling and coordination formal methods in robotics and automation

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A formal Safety Net for Waypoint-Following in Ground Robots

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IEEE robotics AND automation LETTERS 2019年第3期4卷 2910-2917页

作者： Bohrer, Brandon Tan, Yong Kiam Mitsch, Stefan Sogokon, Andrew Platzer, Andre Tech Univ Munich Fak Informat D-85748 Garching Germany Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA

We present a reusable formally verified safety net that provides end-to-end safety and liveness guarantees for two-dimensional way point-following of Dubins-type ground robots with tolerances and acceleration. First, we model a robot in differential dynamic logic and specify assumptions on the controller and robot kinematics. Second, we prove formal safety and liveness properties for waypoint-following with speed limits. Third, we synthesize a monitor, which is automatically proven to enforce model compliance at runtime. Fourth, our use of the VeriPhy toolchain makes these guarantees carry over down to the level of machine code with untrusted controllers, environments, and plans. The guarantees for the safety net apply to any robot as long as the waypoints are chosen safely and the physical assumptions in its model hold. Experiments show that these assumptions hold in practice, with an inherent tradeoff between compliance and performance.

关键词： formal methods in robotics and automation robot safety hybrid logical/dynamical planning and verification motion control kinematics

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Guiding Autonomous Exploration With Signal Temporal Logic

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3332-3339页

作者： Barbosa, Fernando S. Duberg, Daniel Jensfelt, Patric Tumova, Jana KTH Royal Inst Technol Div Robot Percept & Learning S-11428 Stockholm Sweden

Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a method to influence the manner the robot moves in the environment by taking into consideration a user-defined spatial preference formulated in a fragment of signal temporal logic (STL). We propose to guide the motion planning toward minimizing the violation of such preference through a cost function that integrates the quantitative semantics, i.e., robustness of STL. To demonstrate the effectiveness of the proposed approach, we integrate it into the autonomous exploration planner (AEP). Results from simulations and real-world experiments are presented, highlighting the benefits of our approach.

关键词： Mapping motion and path planning formal methods in robotics and automation search and rescue robots

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