GRACE: Gradient-Free Robot Action Generation via Combined Diffusion-MPPI Posterior Mean Estimation
GRACE is a novel method that guides pretrained diffusion policies with Model Predictive Path Integral (MPPI) control using only forward cost evaluations, enabling the handling of nondifferentiable constraints such as binary collision checks and joint limits. It achieves higher success rates in simulation and successfully avoids obstacles on a real 7-DoF manipulator.
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[Submitted on 22 Jul 2026]
Title:GRACE: Gradient-Free Robot Action Generation via Combined Diffusion-MPPI Posterior Mean Estimation
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Abstract:Diffusion policies generate multimodal robot action sequences from demonstrations, but steering them toward deployment-time constraints typically relies on differentiable guidance costs. This excludes many practical safety constraints, such as binary collision checks, joint limits, and black-box rollout costs that are nondifferentiable. We propose Gradient-free Robot Action generation via Combined diffusion-MPPI posterior mean Estimation (GRACE), which guides a pretrained diffusion policy with Model Predictive Path Integral (MPPI) control using only forward cost evaluations. Building on the common score-ascent structure of diffusion and MPPI, GRACE constructs a cost-conditioned guidance posterior at each reverse step and estimates its mean with a single MPPI update centered at the diffusion reverse mean. For differentiable costs, GRACE recovers conventional gradient guidance under a first-order, matched-covariance approximation. GRACE attains higher success rates than diffusion-based and sampling-based baselines in simulation. On a real 7-DoF manipulator, GRACE avoids a deployment-time obstacle that the unguided prior collides with in every trial. Code and experiment videos are available at this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.21661 [cs.RO]
(or arXiv:2607.21661v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.21661
arXiv-issued DOI via DataCite
Submission history
From: Sanghyun Kim [view email] [v1] Wed, 22 Jul 2026 23:20:03 UTC (1,183 KB)
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