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Found 17 papers, 4 papers with code
SAFE-GIL: SAFEty Guided Imitation Learning
The algorithm abstracts the imitation error as an adversarial disturbance in the system dynamics, injects it during data collection to expose the expert to safety critical states, and collects corrective actions.
Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction
In this work, we propose two verification methods, based on robust scenario optimization and conformal prediction, to provide probabilistic safety guarantees for neural reachable tubes.
Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers
Our results show the efficacy of the proposed approach in identifying and handling system-level anomalies, outperforming methods such as prediction error-based detection, and ensembling, thereby enhancing the overall safety and robustness of autonomous systems.
Discovering Closed-Loop Failures of Vision-Based Controllers via Reachability Analysis
Our approach blends simulation-based analysis with HJ reachability methods to compute an approximation of the backward reachable tube (BRT) of the system, i. e., the set of unsafe states for the system under vision-based controllers.
Generating Formal Safety Assurances for High-Dimensional Reachability
A recently proposed method called DeepReach overcomes this challenge by leveraging a sinusoidal neural PDE solver for high-dimensional reachability problems, whose computational requirements scale with the complexity of the underlying reachable tube rather than the state space dimension.
DeepReach: A Deep Learning Approach to High-Dimensional Reachability
Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the ability to deal with state and input constraints.
Visual Navigation Among Humans with Optimal Control as a Supervisor
Videos describing our approach and experiments, as well as a demo of HumANav are available on the project website.
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability
In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed.
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning
We construct a new continuous-time dynamical system, where the inputs are the observations of human behavior, and the dynamics include how the belief over the model parameters change.
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
In this work, we present a framework to optimize the kernel and hyperparameters of a kernel-based model directly with respect to the closed-loop performance of the model.
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors.
Robotics
Combining Optimal Control and Learning for Visual Navigation in Novel Environments
Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories.
Context-Specific Validation of Data-Driven Models
We propose a context-specific validation framework to quantify the quality of a learned model based on a distance measure between the closed-loop actual system and the learned model.
Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances
Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical systems; it has been applied to many small-scale systems in the past decade.
Systems and Control Dynamical Systems Optimization and Control
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Reinforcement Learning is divided in two main paradigms: model-free and model-based.
Goal-Driven Dynamics Learning via Bayesian Optimization
Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics.
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
We propose a new algorithm FaSTrack: Fast and Safe Tracking for High Dimensional systems.
Robotics
