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Creators/Authors contains: "Platt, Robert"

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  1. ; ; ; ; ( , Conference on Robot Learning)
    Free, publicly-accessible full text available November 6, 2025
  2. ; ; ; ; ; ; ; ; ( , Proceedings of the Conference on Robot Learning)
    Free, publicly-accessible full text available October 1, 2025
  3. ; ; ; ( , IEEE)
    Free, publicly-accessible full text available May 13, 2025
  4. ; ; ; ( , IEEE Int'l Conf. on Robotics and Automation)
    Free, publicly-accessible full text available May 13, 2025
  5. ; ; ; ( , Conference on Robot Learning)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ( , International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ( , Conference on Robot Learning)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ; ; ( , Conference on Robot Learning)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ( , Conference on Robot Learning)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ( , 2023 Conference on Robot Learning (CoRL-23))
    Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient learning. Specifically, by encoding the equivariance regarding specific group symmetries into the neural networks, our actor-critic reinforcement learning agents can reuse solutions in the past for related scenarios. Consequently, our equivariant agents outperform non-equivariant approaches significantly in terms of sample efficiency and final performance, demonstrated through experiments on a range of robotic tasks in simulation and real hardware. 
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    Accepted Manuscript Full Text Available