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Creators/Authors contains: "Manchester, Zachary"

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  1. ; ; ; ; ; (, Arxiv (ICRA under review))
    Free, publicly-accessible full text available November 3, 2025
  2. ; ; (, IEEE International Conference on Robotics and Automation)
    We consider the problem of learning error covariance matrices for robotic state estimation. The convergence of a state estimator to the correct belief over the robot state is dependent on the proper tuning of noise models. During inference, these models are used to weigh different blocks of the Jacobian and error vector resulting from linearization and hence, additionally affect the stability and convergence of the non-linear system. We propose a gradient-based method to estimate well-conditioned covariance matrices by formulating the learning process as a constrained bilevel optimization problem over factor graphs. We evaluate our method against baselines across a range of simulated and real-world tasks and demonstrate that our technique converges to model estimates that lead to better solutions as evidenced by the improved tracking accuracy on unseen test trajectories. 
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    Free, publicly-accessible full text available May 13, 2025
  3. ; ; ; ; ; (, ICRA 2024)
    Free, publicly-accessible full text available May 13, 2025
  4. ; (, IEEE Aerospace Conference)
    Full Text Available 
  5. ; ; ; (, IEEE Aerospace Conference)
    Full Text Available 
  6. ; ; ; ; ; ; ; (, IEEE Robotics and Automation Letters)
    Full Text Available 
  7. ; (, Small Satellite Conference)
    Accepted Manuscript Full Text Available
  8. ; ; (, Autonomous Robots)
    Full Text Available 
  9. ; ; ; ; (, Physical Review Applied)
    Full Text Available 
  10. ; ; (, IEEE Robotics and Automation Letters)
    Full Text Available