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Brian Ichter

ichter _at_ stanford _dot_ edu

Brian Ichter

Alumni · now at Physical Intelligence (co-founder)

Brian Ichter is a PhD candidate in Aeronautics and Astronautics at Stanford University (expected graduation early 2018). He received a BS in Aerospace Engineering and BA in Physics from the University of Virginia in 2012 and an MS in Aeronautics and Astronautics from Stanford University in 2015.

Brian’s research focuses on sampling-based algorithms for robotic motion planning. In particular, he works on developing massively parallel algorithms (that leverage GPUs) towards real-time kinodynamic, uncertainty-aware, and perception-aware motion planning. He further works on sampling strategies for planning algorithms by showing theoretical and practical benefits of low-dispersion, deterministic sampling as well as developing methods for learning optimal sample distributions.

Awards:

  • NDSEG Fellowship
  • NSF Graduate Research Fellowship
  • NASA Space Technology Research Fellowship

Publications

8 publications · full lab bibliography

2019
  1. B. Ichter and M. Pavone, “Robot Motion Planning in Learned Latent Spaces,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2407--2414, 2019.
2018
  1. B. Ichter, “Massive Parallelism and Sampling Strategies for Robust and Real-Time Robotic Motion Planning,” Ph.D. dissertation, Stanford University, Dept. of Aeronautics and Astronautics, 2018.
  2. B. Ichter, J. Harrison, and M. Pavone, “Learning Sampling Distributions for Robot Motion Planning,” Proc. IEEE Conf. on Robotics and Automation, 2018.
  3. L. Janson, B. Ichter, and M. Pavone, “Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance,” Int. Journal of Robotics Research, vol. 37, no. 1, pp. 46--61, 2018.
2017
  1. B. Ichter, B. Landry, E. Schmerling, and M. Pavone, “Perception-Aware Motion Planning via Multiobjective Search on GPUs,” Int. Symp. on Robotics Research, 2017.
  2. B. Ichter, E. Schmerling, A. Agha-mohammadi, and M. Pavone, “Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs,” Proc. IEEE Conf. on Robotics and Automation, 2017.
  3. B. Ichter, E. Schmerling, and M. Pavone, “Group Marching Tree: Sampling-Based Approximately Optimal Motion Planning on GPUs,” IEEE Int. Conf. on Robotic Computing, 2017.
2015
  1. L. Janson, B. Ichter, and M. Pavone, “Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance,” Int. Symp. on Robotics Research, 2015.