Scaling robotic foundation models requires diverse training data and reliable evaluation environments. Simulation offers a scalable solution, yet existing generation pipelines remain constrained by predefined assets and skills, a disconnect between scene generation and task generation, and limited support for complex e...
Yi-Kai Qin, Yi-Fei Deng, Ming-Jian Liang et al.· 0 citations
This work proposes DIDO, which distills the converged dynamics of a multi-step video model into a single denoising step and achieves an average success rate of 99.0\% on LIBERO, 76.6\% on LIBERO-Plus, and 92.0\% on RoboTwin, while also demonstrating effective transfer to long-horizon and generalization tasks in real-wo...
Jing Lyu, Shuanghao Bai, Run-Ze Xiao et al.· 2 citations
GraphIR, an architecture-aware intermediate representation that supplements executable programs with a mutation-aligned candidate state, achieves the best overall search performance while maintaining comparable model size and favorable end-to-end NAS efficiency when integrated into OpenEvolve.
Zhen Liu, Wanqi Zhou, Shuanghao Bai et al.· 1 citation
HAF (Humanoid Adaptation Framework), a two-part framework consisting of HAF-VLA and HAF-Steer that transfers off-the-shelf generalist VLA foundation models to humanoid whole-body loco-manipulation, surpasses vanilla single-stage VLA baselines and improves whole-body coordination and task performance.
Lang-Zhe Gu, Cheng-Kai Hou, Meng Li et al.· 3 citations
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