Guanhe Huang

2 accepted papers

2026

Efficient Continual Imitation Learning With Online Meta-Adapters

Ruiqi Zhu, Endong Sun, Guanhe Huang, Oya Çeliktutan

RA-L 2026

Continual adaptation is essential for general autonomous agents. For example, a household robot pretrained with a repertoire of skills must still learn and adapt to unseen tasks specific to each household. However, prior works mainly emphasize either effective pretraining of models for decision-maki

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2026

Unified Number-Free Text-to-Motion Generation Via Flow Matching

Guanhe Huang, Oya Celiktutan

CVPR 2026

Generative models excel at motion synthesis for a fixed number of agents but struggle to generalize with variable agents. Based on limited, domain-specific data, existing methods employ autoregressive models to generate motion recursively, which suffer from inefficiency and error accumulation. We pr

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