DASIP: Dynamic Test-Time Compute Scaling for Robot Control with Stochastic Interpolant Policies

Researchers introduce DA-SIP, a dynamic inference framework for robotic control that adaptively adjusts computational resources based on task difficulty. The approach reduces inference time by 2.6-4.4x while maintaining performance, addressing the computational inefficiency of fixed-budget diffusion and flow-based policies in robotics.

DA-SIP represents a meaningful advancement in making generative models practical for real-world robotic deployment. Traditional diffusion and flow-based policies allocate identical computational budgets across all control steps, wasting resources on simple decisions while potentially underfitting complex scenarios. This research tackles resource allocation at inference time through a difficulty classifier that determines appropriate step budgets and integration strategies dynamically.

The approach builds on stochastic interpolant frameworks, which unify diffusion and flow models under a common mathematical foundation. This generality enables researchers to mix training and inference configurations flexibly—switching between deterministic ODE and stochastic SDE solvers, or adjusting sampling steps—without retraining. The difficulty classifier operates on raw observations, making deployment straightforward in existing robotic systems.

The empirical results demonstrate substantial efficiency gains across manipulation benchmarks. Achieving 2.6-4.4x computation reduction while preserving task success rates directly addresses a critical bottleneck for deploying learned policies on resource-constrained robotic hardware. This matters for both industrial deployment and academic research, where computational constraints limit model capacity and real-time control frequency.

Looking ahead, the framework's success depends on how well difficulty classification generalizes to novel tasks and environments. Future work likely involves exploring different classifier architectures, understanding which task features drive difficulty predictions, and extending adaptive computation to multi-agent or hierarchical control scenarios. The underlying principle—matching computational investment to task demands—has broader applications in embodied AI beyond manipulation tasks.

• →DA-SIP dynamically adjusts inference computation based on real-time task difficulty, reducing total compute by 2.6-4.4x compared to fixed-budget approaches.
• →The framework unifies diffusion and flow-based policies under stochastic interpolants, enabling flexible training and inference configuration combinations.
• →A difficulty classifier analyzes observations to select optimal solver variants and integration strategies at each control step without retraining.
• →Task success rates remain comparable to fixed maximum-computation baselines despite substantial inference time reduction.
• →The approach makes resource-aware inference practical for deploying generative robot controllers on hardware-constrained platforms.