Walker Byrnes — accepted AI-conference papers

2026

Hierarchical Policy Learning via Spectral Decomposition

Shuxin Cao, Liquan Wang, Walker Byrnes, Yiye Chen, Yilun Du, Animesh Garg

ICML 2026poster

In this paper, we identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements. By analyzing actions in the spectral domain using the discrete cosine transform (DCT), we observe that low-frequency components capture global motion t…

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2025

CLIMB: Language-Guided Continual Learning for Task Planning with Iterative Model Building

Walker Byrnes, Miroslav Bogdanovic, Avi Balakirsky, Stephen Balakirsky, Animesh Garg

ICRA 2025

Intelligent and reliable task planning is a core capability for generalized robotics, which requires a descriptive domain representation that sufficiently models all object and state information for the scene. We present CLIMB, a continual learning framework for robot task planning that leverages fo

Cited by 3 Source ↗code ↗Scholar ↗