Problem
Research questions and friction points this paper is trying to address.
generative policies
real-time control
constraint satisfaction
inference-time steering
Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies
🤖 AI Summary
为解决预训练生成策略在实时控制中适应部署约束的问题，提出通过输入噪声空间的轨迹优化方法INSPO，实现实时策略引导。
📝 Abstract
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 constraints on the induced state trajectory, INSPO searches the policy-induced behavior space without directly modifying generated actions. The optimization includes a regularization term that encourages solutions to remain consistent with the policy's input distribution and is solved online using population-based particle optimization. We evaluate INSPO on state- and image-based task-specific policies and generalist vision-language-action policies across Push-T, Can pick-and-place, and LIBERO-Spatial. INSPO improves task success and constraint satisfaction over best-of-N sampling and action projection, while comparing favorably with gradient-guided generation at lower runtime.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Noise-Space Trajectory Optimization
Real-Time Policy Steering
Population-based Particle Optimization
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