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MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Research

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arxiv 1908.08031 v3 pith:BHWSWTKQ submitted 2019-08-21 cs.RO

Siddhartha S. Srinivasa , Patrick Lancaster , Johan Michalove , Matt Schmittle , Colin Summers , Matthew Rockett , Rosario Scalise , Joshua R. Smith
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Sanjiban Choudhury Christoforos Mavrogiannis Fereshteh Sadeghi
This is my paper
classification cs.RO
keywords mushrroboticslow-costplatformresearchdevelopededucationopen-source
verification ladder T0 review T1 audit T2 compute T3 formal
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We present MuSHR, the Multi-agent System for non-Holonomic Racing. MuSHR is a low-cost, open-source robotic racecar platform for education and research, developed by the Personal Robotics Lab in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. MuSHR aspires to contribute towards democratizing the field of robotics as a low-cost platform that can be built and deployed by following detailed, open documentation and do-it-yourself tutorials. A set of demos and lab assignments developed for the Mobile Robots course at the University of Washington provide guided, hands-on experience with the platform, and milestones for further development. MuSHR is a valuable asset for academic research labs, robotics instructors, and robotics enthusiasts.

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Cited by 3 Pith papers

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  1. Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation

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    Sym2Real learns a symbolic dynamics model in low-fidelity simulation, then adds a residual neural network trained on a few real-world trajectories to achieve sample-efficient adaptive control.

  3. NeoRacer: An Open, Standardized 1:12 Scale Autonomous Race Car for Benchmarking and Education

    cs.RO 2026-07 conditional novelty 4.5 of 10

    NeoRacer packages 67-TOPS Jetson compute, LiDAR, 120 fps camera, and ROS2 into a ~$2,700 pre-assembled 1:12 open racing platform for education and cross-lab benchmarks.

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