Safe Visual Navigation via Deep Learning and Novelty Detection

@inproceedings{Richter2017SafeVN,
  title={Safe Visual Navigation via Deep Learning and Novelty Detection},
  author={Charles Richter and Nicholas Roy},
  booktitle={Robotics: Science and Systems},
  year={2017}
}
Robots that use learned perceptual models in the real world must be able to safely handle cases where they are forced to make decisions in scenarios that are unlike any of their training examples. However, state-of-the-art deep learning methods are known to produce erratic or unsafe predictions when faced with novel inputs. Furthermore, recent ensemble, bootstrap and dropout methods for quantifying neural network uncertainty may not efficiently provide accurate uncertainty estimates when… 
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141 Citations
Highly Influential Citations
16
Background Citations
72
Methods Citations
44
Results Citations
4

141 Citations

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Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
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This paper proposes a method for uncertainty-aware robotic perception that combines generative modeling and model uncertainty, and estimates an uncertainty measure about the model’s prediction, taking into account an explicit generative model of the observation distribution to handle out-of-distribution inputs.
Safe Reinforcement Learning With Model Uncertainty Estimates
TLDR
MC-Dropout and Bootstrapping are used to give computationally tractable and parallelizable uncertainty estimates and are embedded in a Safe Reinforcement Learning framework to form uncertainty-aware navigation around pedestrians, resulting in a collision avoidance policy that knows what it does not know and cautiously avoids pedestrians that exhibit unseen behavior.
Task-Aware Novelty Detection for Visual-based Deep Learning in Autonomous Systems
TLDR
This paper proposes a learning framework that leverages information learned by the prediction model in a task-aware manner to detect novel scenarios and finds that the method is able to systematically detect novel inputs and quantify the deviation from the target prediction through this task- aware approach.
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
TLDR
This work shows that a simulated robotic car and a real-world RC car can gather data and train fully autonomously without any human-provided labels beyond those needed to train the detectors, and then at test-time be able to accomplish a variety of different tasks.
Bayesian Optimization Meets Laplace Approximation for Robotic Introspection
TLDR
This paper introduces a scalable Laplace Approximation technique to make Deep Neural Networks (DNNs) more introspective, i.e. to enable them to provide accurate assessments of their failure probability for unseen test data.
Novelty Detection via Network Saliency in Visual-Based Deep Learning
TLDR
This paper proposes a multi-step framework for the detection of novel scenarios in vision-based autonomous systems by leveraging information learned by the trained prediction model and a new image similarity metric.
Learning to be Safe: Deep RL with a Safety Critic
TLDR
This work proposes to learn how to be safe in one set of tasks and environments, and then use that learned intuition to constrain future behaviors when learning new, modified tasks, and empirically studies this form of safety-constrained transfer learning in three challenging domains.
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
TLDR
The reinforcement learning approach, which the authors call BADGR, is an end-to-end learning-based mobile robot navigation system that can be trained with autonomously-labeled off-policy data gathered in real-world environments, without any simulation or human supervision.
Learning for Robot Decision Making under Distribution Shift: A Survey
TLDR
A taxonomy of existing literature to aid or improve decision making under distribution shift for robotic systems is presented and a survey of existing approaches in the area based on this taxonomy is presented.
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