BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

By Gregory Kahn, Pieter Abbeel, Sergey Levine. 2020

327 citations recorded by the source corpus.

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our reinforcement learning approach, which we 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. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://sites.google.com/view/badgr

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning, Robotics & Control

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Citations in the corpus

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020, methodology): “, 2018), and robotic navigation skills (Kahn et al., 2020).”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “Robot learning has emerged as a strong paradigm for automatically acquiring complex and skilled behaviors such as locomotion (Yang et al., 2019; Peng et al., 2020), navigation (Mirowski et al., 2017; Kahn et al., 2020), and manipulation (Gu et al., 2017; et al, 2019).”
ANYmal parkour: Learning agile navigation for quadrupedal robots (2023, background): “The locomotion module contains a catalog of locomotion skills that can overcome specific terrains.”
Model-Based Offline Planning (2020, methodology): “(2017b) show that a simple model-based controller can quickly learn to drive a vehicle on a dirt track, the BADGR robot (Kahn et al., 2020) also uses Model-Predictive Path Integral (MPPI) (Williams et al.”
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (2024, unknown): “Some methods tackled these challenges with model-based RL to learn predictive models of events or disengagements (88), or utilizing demonstration data with offline RL (89).”
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks (2021, background): “While self-supervised system [53] formulates the navigation beyond the geometric problem and allows the robot to learn from its own experiences.”