Author

Negar Mehr

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Preprint Sep 2026

Residual Denoising Enables Sample-Efficient Multi-Agent Coordination on Demand

Pretrained robot policies offer strong manipulation skills but are typically limited to single-agent settings, where a robot acts in isolation. In this work, we study how to adapt pretrained single-agent diffusion policies to multi-agent settings using minimal collaborative data, co-optimizing for two key objectives: h...

Da-Yi Dong, Maulik Bhatt, Aayushi Shrivastava et al. · 0 citations

Preprint Sep 2026

HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation

This work proposes a hierarchical approach to humanoid loco-manipulation that eliminates per-task requirements, and substantially outperforms a high-level reinforcement learning baseline and approaches the performance of a demonstration-based baseline.

Seoyeon Choi, Shi-Zhao Ye, N. Bui et al. · 0 citations