Agile Autonomous Driving using End-to-End Deep Imitation Learning

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201

HOUND: An Open-Source, Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving

Off-road vehicles are susceptible to rollovers in terrains with large elevation features, such as steep hills, ditches, and berms. One way to protect them against rollovers is ruggedization through the use of industrial-grade parts and physical modifications. However, this solution can be prohibitively expensive for academic research labs. Our key insight is that a software-based rollover-prevention system (RPS) enables the use of commercial-off-the-shelf hardware parts that are cheaper than their industrial counterparts, thus reducing overall cost. In this paper, we present HOUND, a small-scale, inexpensive, off-road autonomy platform that can handle challenging outdoor terrains at high speeds through the integration of an RPS. HOUND is integrated with a complete stack for perception and control, geared towards aggressive offroad driving. We deploy HOUND in the real world, at high speeds, on four different terrains covering 50 km of driving and highlight its utility in preventing rollovers and traversing difficult terrain. Additionally, through integration with BeamNG, a state-of-the-art driving simulator, we demonstrate a significant reduction in rollovers without compromising turning ability across a series of simulated experiments. Supplementary material can be found on our website, where we will also release all design documents for the platform: https://sites.google.com/view/prl-hound .Comment: 6 Pages, 8 Figure

Survey of Agile navigation algorithms for robot ground vehicles

En aquest treball, diversos mètodes orientats a la navegació àgil de vehicles robòtics terrestres son comparats. Primerament, es realitza un estudi de publicacions per a identificar els mètodes pertanyents a l'estat de la tècnica més adequats per a ser comparats amb un mètode de navegació àgil (''CarPlanner'') desenvolupat al Autonomous Robotics and Perception Group (ARPG). Diferents mètodes són examinats i implementats en un ambient simulat. Aquests mètodes són evaluats basant-se en la seva eficàcia navegant el vehicle robòtic terrestre en una pista que té salts, sotracs i bermes. L'ambient simulat conté un vehicle terrestre de quatre rodes amortiguades amb geometria d'Ackermann, el qual ha de conduïr per un terreny amb dinàmica de fricció no linear. Els criteris per a evaluar els mètodes inclouen l'habilitat per a utilitzar les dinàmiques del vehicle per a recórrer la pista de manera ràpida i segura. Finalment, el mètode més apropiat i amb millor resultats és implementat al cotxe NinjaCar d'escala 1:8 del laboratori ARPG i comparat amb l'algoritme CarPlanner mitjançant experimentació física.In this work, several state-of-the-art methods for agile navigation of robot ground vehicles are compared. First, a survey of the literature is performed to identify the state-of-the-art and most appropriate methods for comparing to an agile navigation method (''CarPlanner'') developed in the Autonomous Robotics and Perception Group (ARPG). Several methods are reviewed and implemented in a dynamic vehicle simulation environment. These methods are evaluated on their efficacy of navigating a robot ground vehicle around a race track featuring jumps, bumps, and berms. The simulation environment features a four-wheeled, Ackermann-style ground vehicle with suspension and austere terrain with nonlinear friction dynamics. Criteria for evaluating the methods includes the ability of the method at utilizing the vehicle dynamics to quickly and safely traverse the track. Finally, the most appropriate and best-performing method is implemented on ARPG's 1/8th-scale NinjaCar vehicle platform and compared in physical experimentation to ARPG's CarPlanner algorithm