SOURCE-LINKED INTELLIGENCE

Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Generative robot policies can represent diverse, multimodal behaviors, but adapting pretrained policies to deployment-time constraints such as collision avoidance and orientation maintenance remains challenging. Existing inference-time steering methods typically apply gradient guidance through iterative diffusion or flow processes, which can be computationally expensive for real-time control. We propose INSPO, which formulates inference-time steering of one-step generative policies as trajectory optimization in the policy's input noise space. By optimizing the input noise while evaluating cons

Read original source ↗ Open in workspace

recordType

paper

region

Global