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We show only Monte Carlo and NB for clarity; comparisons with other methods are in Table 1

Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems

NIPS 2020, pp.6402-6416, (2020)

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Abstract

Learning-based methodologies increasingly find applications in safety-critical domains like autonomous driving and medical robotics. Due to the rare nature of dangerous events, real-world testing is prohibitively expensive and unscalable. In this work, we employ a probabilistic approach to safety evaluation in simulation, where we are con...More

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Introduction
  • Data-driven and learning-based approaches have the potential to enable robots and autonomous systems that intelligently interact with unstructured environments.
  • Currently deployed safety-critical autonomous systems are limited to structured environments that allow mechanisms such as PID control, simple verifiable protocols, or convex optimization to enable guarantees for properties like stability, consensus, or recursive feasibility.
  • The stylized settings of these problems and the limited expressivity of guaranteeable properties are barriers to solving unstructured, real-world tasks such as autonomous navigation, locomotion, and manipulation.
  • The authors assume access to a simulator to test the system’s performance.
  • Given a distribution X ∼ P0 of simulation parameters that describe typical environments for the system under test, the governing problem is to estimate the probability of an adverse event pγ := P0(f (X) ≤ γ)
Highlights
  • Data-driven and learning-based approaches have the potential to enable robots and autonomous systems that intelligently interact with unstructured environments
  • A major focus of this work is empirical, and Section 4 empirically demonstrates the superiority of neural bridge sampling over competing techniques in a variety of applications: (i) we evaluate the sensitivity of a formallyverified system to domain shift, (ii) we consider design optimization for high-precision rockets, and (iii) we perform model comparisons for two learning-based approaches to autonomous navigation
  • We describe an Markov-chain Monte Carlo (MCMC) method that combines exploration, exploitation, and optimization to draw samples Xik ∼ Pk
  • We consider two examples of using neural bridge sampling as a tool for engineering design in high-dimensional settings: (a) comparing thruster sizes to safely land a rocket [12] in the presence of wind, and (b) comparing two algorithms on the OpenAI Gym CarRacing environment [54]
  • We show only Monte Carlo (MC) and NB for clarity; comparisons with other methods are in Table 1
  • We intend to investigate how efficiently sampling rare failures—like we propose here for evaluation—could enable the automated repair of safety-critical reinforcement-learning agents
Methods
  • The authors evaluate the approach on a variety of scenarios showcasing its use in efficiently evaluating the safety of autonomous systems.
  • All methods are given the same computational budget as measured by evaluations of the simulator.
  • This varies from 50,000-100,000 queries to run Algorithm 1 as determined by pγ.
  • Despite running Algorithm 1 with a given γ, the authors evaluate estimates pγtest for all γtest ≥ γ.
  • The authors calculate the ground-truth values pγtest for non-synthetic problems using a fixed, very large number of MC queries
Conclusion
  • There is a growing need for rigorous evaluation of safety-critical systems which contain components without formal guarantees.
  • Evaluating the safety of such systems in the presence of rare, catastrophic events is a necessary component in enabling the development of trustworthy high-performance systems.
  • Neural bridge sampling, employs three concepts—exploration, exploitation, and optimization—in order to evaluate system safety with provable statistical and computational efficiency.
  • The authors intend to investigate how efficiently sampling rare failures—like the authors propose here for evaluation—could enable the automated repair of safety-critical reinforcement-learning agents.
Tables
  • Table1: Relative mean-square error E[(pγ/pγ − 1)2] over 10 trials
Download tables as Excel
Related work
  • Safety evaluation Several communities [25] have attempted to evaluate the closed-loop performance of cyber-physical, robotic, and embodied agents both with and without learning-based components. Existing solutions are predicated on the definition of the evaluation problem: verification, falsification, or estimation. In this paper we consider a method that utilizes interactions with a gradient oracle in order to solve the estimation problem (1). In contrast to our approach, the verification community has developed tools (e.g. [55, 22, 3]) to investigate whether any adverse or unsafe executions of the system exist. Such methods can certify that failures are impossible, but they require that the model is written in a formal language (a barrier for realistic systems) and they require whitebox access to this formal model. Falsification approaches (e.g. [38, 29, 4, 104, 32, 79]) attempt to find any failure cases for the system (but not the overall probability of failure). Similar to our approach, some falsification approaches (e.g. [1, 103]) utilize gradient information, but their goal is to simply minimize f (x) rather than solve problem (1). Adversarial machine learning is closely related to falsification; the key difference is the domain over which the search for falsifying evidence is conducted. Adversarial examples (e.g. [59, 91, 52, 95]) are typically restricted to an pnorm ball around a point from a dataset, whereas falsification considers all possible in-distribution examples. Both verification and falsification methods provide less information about the system under test than estimation-based methods: they return only whether or not the system satisfies a specification. When the system operates in an unstructured environment (e.g. driving in an urban setting), the mere existence of failures is trivial to demonstrate [89]. Several authors (e.g. [74, 100]) have proposed that it is more important in such settings to understand the overall frequency of failures as well as the relative likelihoods of different failure modes, motivating our approach.
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