Toyota Motor Engineering & Manufacturing North America, Inc.

University of Southern California

University of California

Arizona State University

Université Joseph Fourier

Jyotirmoy Vinay Deshmukh

James P Kapinski

Xiaoqing Jin

Luan V Nguyen

Danil V Prokhorov

Shigeki Hasegawa

Jared Michael Farnsworth

Georgios Fainekos

Hani Ben Amor

Aniruddh Gopinath Puranic

Stefanos Nikolaidis

Ayca Balkan

Alexandre Donze

Sanjit A Seshia

Tommaso Dreossi

Adel Dokhanchi

Thao Dang

Systems and methods are provided for learning control policies that are safe, robust, and interpretable in developing robotic systems. In one example, a signal temporal logic is used to evaluate and rank the quality of demonstrations. The temporal logic based specifications allow generation of non-Markovian rewards, and also define causal dependencies between tasks such as sequential task specifications.

System and method for robot learning from human demonstrations with formal logic

Various embodiments for systems and methods of evaluating perception systems for autonomous vehicles using a quality temporal logic are disclosed herein.

Systems and methods for evaluating perception systems for autonomous vehicles using quality temporal logic

Systems and methods are provided for implementing safety-aware artificial intelligence (AI) that can be used for autonomously controlling systems, such as an autonomous vehicle, in a manner that is proven to satisfy given safety constraints. Additionally, a safety-aware training technique can be applied to learned AI-based models, such as neural networks. The safety-aware training techniques can apply automated reasoning tools (ART) while the AI model is trained, in order to produce a model that is provable safe with respect to the safety constraints. The ART can integrate verification into the training process, and thereby dynamically re-train the model based on the safety verification in a feedback loop approach. The ART can be configured to either verify that the AI model is provably safety, or to provide updates to the training parameters used during to re-train the AI model in instances when the safety verification has failed.

Systems and methods for safety-aware training of AI-based control systems

Systems and methods are provided for monitoring time-series data relative to a temporal logic specification regarding expected behavior of a system, such as a vehicle. The time-series data and a threshold value(s) specified in the temporal logic specification may be encrypted and analyzed without decrypting the time-series data to maintain the privacy of a user(s) of the vehicle. Encryption of the time-series data and the threshold value(s) may be accomplished using an order preserving encryption scheme. Analysis of the time-series data may be accomplished utilizing a batch processing-type architecture or a continuous processing-type architecture. When utilizing the continuous processing-type architecture, historical time-series data may be stored and utilized to determine whether currently-monitored time-series data satisfies the temporal logic specification.

Privacy-aware signal monitoring systems and methods

A system for determining desired control paths for controlling operation of a fuel cell circuit includes a memory to store a model of the fuel cell circuit and an input device to receive system requirements. The system also includes a model processor designed to select sets of time-series actuator states corresponding to time-series control of an actuator of the fuel cell circuit and to perform simulations of the model using the multiple sets of time-series actuator states as controls for the actuator. The model processor is also performs an analysis of results of the simulations to determine whether the results for each of the multiple sets of time-series actuator states satisfy the system requirements and how far the results are from the system requirements, and selects a final set of time-series actuator states that satisfy the system requirements based on the analysis.

STL actuation-path planning

Systems and methods for evaluating closed-loop control systems are disclosed. In one embodiment, a method of evaluating a control system includes determining, using a processing device, one or more convergence classifier functions from a closed-loop model, wherein the one or more convergence classifier functions convey convergent behavior of the closed-loop model over a pre-determined period of time. The method further includes generating, using the processing device, a plurality of test cases of an input space of the closed-loop model under evaluation, and determining, using the processing device, whether one or more individual test cases of the plurality of test cases do not satisfy the one or more convergence classifier functions.

Systems and methods for testing convergence of closed-loop control systems

A computer-implemented method for automatically identifying a faulty behavior of a control system. The method includes receiving, at a test processor, a description of the faulty behavior. The method also includes selecting, using the test processor, a goal state based on a heuristic decision. The method also includes selecting, using the test processor, a selected system state. The method also includes selecting, using the test processor, a selected variable to the control system based on the goal state. The method also includes loading, from a memory, a control model of the control system. The method also includes performing, using the test processor, a simulation of the control model using the selected variable and the selected system state as parameters of the simulation. The method also includes determining, using the test processor, whether the faulty behavior was observed based on the simulation.

Coverage guided technique for bug finding in control systems and software

A computer system may seek to identify at least one contraction metric that satisfies contraction conditions for a design of a dynamical system. The computer system may do so by formulating a search for a candidate contraction metric that is suspected of satisfying the contraction conditions for the design of the dynamical system from a set of simulation traces that describe the behavior of the dynamical system for a specific set of operating conditions. The search for the candidate contraction metric may then be performed. The computer system may seek to verify that a candidate contraction metric for a design of a dynamical system satisfies contraction conditions by performing a process that uses solvers based on decision procedures.

Simulation-guided incremental stability analysis

In one embodiment, a method of evaluating stability of software code for a control system includes receiving a set of initial trajectories by a semidefinite programming solver module, and determining one or more candidate Lyapunov functions based on the set of initial trajectories. The method further includes performing a plurality of simulations using a model of the control system to create a set of discovered trajectories, and evaluating the set of discovered trajectories to determine one or more counterexample trajectories that violate one or more Lyapunov conditions. If one or more counterexample trajectories are discovered, then the method includes inputting the set of discovered trajectories including the one or more counterexample trajectories into the semidefinite programming solver module, and determining, by the semidefinite programming solver module, one or more additional candidate Lyapunov functions from the set of initial trajectories and the set of discovered trajectories.

Systems and methods for evaluating stability of software code for control systems

Systems and methods for mining a temporal requirement from a block diagram model of a closed loop control system are disclosed. One embodiment of a method includes simulating the closed loop control system of a vehicle to obtain simulation traces and determining a candidate requirement by instantiating a template requirement with values of the simulation traces to locate parameter values that suggest that the template requirement is fulfilled. Some embodiments of the method include determining whether a counterexample to the candidate requirement exists; and in response to determining that the counterexample to the candidate requirement exists, obtaining the counterexample to the candidate requirement and adding the counterexample to the simulation traces for inspection.

Growing community of inventors

Torrance, CA, United States of America

Jyotirmoy Vinay Deshmukh