One of the key reasons for the success of deep learning in robot control is the ability of neural networks to elegantly process rich visual information and output useful information for control. Unfortunately, vision-based controllers can also be very brittle when faced with out-of-distribution inputs, potentially leading to catastrophic system failures. In this work, we propose an approach for stress testing these controllers using photorealistic simulators. We formulate the closed-loop failure discovery as an optimal control problem, which allows us to do a targeted and efficient search for visual inputs that might trigger system failures. Our findings reveal intriguing and unexpected situations that could compromise state-of-the-art visual controllers, such as pedestrian markings confusing an autonomous aircraft or light-colored walls misleading indoor navigation robots.

Human-robot interactions are an unavoidable aspect of many modern robotic applications and ensuring safety for these interactions is critical. In such scenarios, the robot often leverages a human motion predictor to predict their future states and plan safe and efficient trajectories, includinh high-capacity neural networks that can leverage the scene semantics to predict complex human behaviors. However, no model is ever perfect -- when the observed human behavior deviates from the model predictions, the robot might plan unsafe maneuvers. In this work, we propose a HJ reachability-based approach that maintains a confidence in the human model and automatically adjust the safety assurances online as this confidence changes based on the observed human behavior. Overall, our approach allows us to ensure a safe human-robot interaction even when the human model is incorrect. We demonstrate our approach in several safety-critical autonomous driving scenarios, involving a state-of-the-art deep learning-based prediction model.

Real-world autonomous vehicles often operate in a priori unknown environments. Since most of these systems are safety-critical, it is important to ensure they operate safely in the face of environment uncertainty, such as unseen obstacles. Current safety analysis tools do not scale well to scenarios where the environment is being sensed in real time, such as during navigation tasks. In this work, we propose a novel, real-time safety analysis method based on Hamilton-Jacobi reachability that provides strong safety guarantees despite environment uncertainty. Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors. We demonstrate our approach in simulation and in hardware to provide safety guarantees around a state-of-the-art vision-based, learning-based planner.

Real-world autonomous vehicles often need to navigate in a priori unknown environments. In this work, we propose a modular approach that enables autonomous navigation in unknown enviornments using only the monocular RGB images from an onboard camera. Our approach uses machine learning for high-level planning based on perceptual information; this high-level plan is then used for low-level planning and control via leveraging classical model-based, control-theoretic approaches. This modular policy leads to a significantly better performance in new, unseen buildings compared to pure learning- based approaches. In addition, we demonstrate that a navigation policy encapsulated in this fashion can be transferred from simulation to reality directly with no retraining or finetuning, demonstrating the robust generalization capabilities of the learned policy. Finally, a side advantage of our integrated policy is its significant data efficiency compared to pure learning-based approaches (a 10x improvement in sample complexity!).

Autonomous robots often navigate in unfamiliar, dynamic environments, where they need to share the space with humans. Navigating around humans is especially difficult because it requires predicting their future motion, which can be quite challenging. We propose a novel framework for navigation around humans which combines learning-based perception with model-based optimal control. The proposed framework learns to anticipate and react to peoples' motion based only on a monocular RGB image, without explicitly predicting the future human motion. Our method generalizes well to unseen buildings and humans in both simulation and real world environments. Furthermore, our experiments demonstrate that combining model-based control and learning leads to better and more data-efficient navigational behaviors as compared to a purely learning based approach.