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Creators/Authors contains: "Wong, Lawson LS"

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  1. ; ; ; ; ; ; (, Artificial Intelligence and Statistics (AISTATS))
    Free, publicly-accessible full text available May 6, 2026
  2. ; ; ; ; ; ; (, Workshop on the Algorithmic Foundations of Robotics (WAFR))
    Accepted Manuscript Full Text Available
  3. ; ; (, IEEE)
    Full Text Available 
  4. ; ; ; ; (, IEEE Robotics and Automation Letters)
    Full Text Available 
  5. ; ; ; ; ; ; (, Workshop on the Algorithmic Foundations of Robotics)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, International Conference on Learning Representations)
    To achieve this, we draw inspiration from equivariant convolution networks and model the path planning problem as a set of signals over grids. We demonstrate that value iteration can be treated as a linear equivariant operator, which is effectively a steerable convolution. Building upon Value Iteration Networks (VIN), we propose a new Symmetric Planning (SymPlan) framework that incorporates rotation and reflection symmetry using steerable convolution networks. We evaluate our approach on four tasks: 2D navigation, visual navigation, 2 degrees of freedom (2-DOF) configuration space manipulation, and 2-DOF workspace manipulation. Our experimental results show that our symmetric planning algorithms significantly improve training efficiency and generalization performance compared to non-equivariant baselines, including VINs and GPPN. 
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    Accepted Manuscript Full Text Available