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Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments
Daniel Shin · Dhruv Shah · Ali Agha · Nicholas Rhinehart · Sergey Levine

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in Deep Reinforcement Learning »
Event URL: https://openreview.net/forum?id=b5CAA57Mozr »

Mobile robots tasked with reaching user-specified goals in open-world outdoor environments must contend with numerous challenges, including complex perception and unexpected obstacles and terrains. Prior work has addressed such problems with geometric methods that reconstruct obstacles, as well as learning-based methods. While geometric methods provide good generalization, they can be brittle in outdoor environments that violate their assumptions (e.g., tall grass). On the other hand, learning-based methods can learn to directly select collision-free paths from raw observations, but are difficult to integrate with standard geometry-based pipelines. This creates an unfortunate ``either-or" dichotomy -- either use learning and lose out on well-understood geometric navigational components, or do not use it, in favor of extensively hand-tuned geometry-based cost maps. The main idea of our approach is reject this dichotomy by designing the learning and non-learning-based components in a way such that they can be easily and effectively combined and created without labeling any data. Both components contribute to a planning criterion: the learned component contributes predicted traversability as rewards, while the geometric component contributes obstacle cost information. We instantiate and comparatively evaluate our system in a high-fidelity simulator. We show that this approach inherits complementary gains from both components: the learning-based component enables the system to quickly adapt its behavior, and the geometric component often prevents the system from making catastrophic errors.

Author Information

Daniel Shin (UC Berkeley)
Dhruv Shah (None)
Ali Agha
Nicholas Rhinehart (University of California, Berkeley)

Nick Rhinehart is a Postdoctoral Scholar in the Electrical Engineering and Computer Science Department at the University of California, Berkeley, where he works with Sergey Levine. His work focuses on fundamental and applied research in machine learning and computer vision for behavioral forecasting and control in complex environments, with an emphasis on imitation learning, reinforcement learning, and deep learning methods. Applications of his work include autonomous vehicles and first-person video. He received a Ph.D. in Robotics from Carnegie Mellon University with Kris Kitani, and B.S. and B.A. degrees in Engineering and Computer Science from Swarthmore College. Nick's work has been honored with a Best Paper Award at the ICML 2019 Workshop on AI for Autonomous Driving and a Best Paper Honorable Mention Award at ICCV 2017. His work has been published at a variety of top-tier venues in machine learning, computer vision, and robotics, including AAMAS, CoRL, CVPR, ECCV, ICCV, ICLR, ICML, ICRA, NeurIPS, and PAMI. Nick co-organized the workshop on Machine Learning in Autonomous Driving at NeurIPS 2019, the workshop on Imitation, Intent, and Interaction at ICML 2019, and the Tutorial on Inverse RL for Computer Vision at CVPR 2018.

Sergey Levine (UC Berkeley)

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