Strengthening Generative Robot Policies through Predictive World Modeling

• URL: http://arxiv.org/abs/2502.00622v2
• Date: Thu, 22 May 2025 03:40:40 GMT
• Title: Strengthening Generative Robot Policies through Predictive World Modeling
• Authors: Han Qi, Haocheng Yin, Aris Zhu, Yilun Du, Heng Yang,
• Abstract summary: generative predictive control (GPC) is a learning control framework that clones a generative diffusion-based policy from expert demonstrations.<n>GPC consistently outperforms behavior cloning in both state-based and vision-based settings.
• Score: 25.45350191178106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present generative predictive control (GPC), a learning control framework that (i) clones a generative diffusion-based policy from expert demonstrations, (ii) trains a predictive action-conditioned world model from both expert demonstrations and random explorations, and (iii) synthesizes an online planner that ranks and optimizes the action proposals from (i) by looking ahead into the future using the world model from (ii). Across a variety of robotic manipulation tasks, we demonstrate that GPC consistently outperforms behavior cloning in both state-based and vision-based settings, in simulation and in the real world.

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