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[2512.01809] Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
Summary
This paper evaluates generative control policies in robotics, revealing that their success is due to iterative computation rather than multi-modality or complex mappings.
Why It Matters
Understanding the true drivers of success in generative robotic control is crucial for advancing the design of more efficient and effective robotic systems. This research challenges existing beliefs and opens new avenues for policy development, potentially leading to better performance in robotic applications.
Key Takeaways
- Generative control policies (GCPs) excel due to iterative computation, not multi-modality.
- Intermediate supervision during training enhances performance.
- Minimum iterative policies (MIPs) can match or exceed the performance of complex GCPs.
- The distribution-fitting aspect of GCPs is less significant than previously thought.
- New design spaces should focus on control performance rather than complex behavior modeling.
Computer Science > Robotics arXiv:2512.01809 (cs) [Submitted on 1 Dec 2025 (v1), last revised 23 Feb 2026 (this version, v3)] Title:Much Ado About Noising: Dispelling the Myths of Generative Robotic Control Authors:Chaoyi Pan, Giri Anantharaman, Nai-Chieh Huang, Claire Jin, Daniel Pfrommer, Chenyang Yuan, Frank Permenter, Guannan Qu, Nicholas Boffi, Guanya Shi, Max Simchowitz View a PDF of the paper titled Much Ado About Noising: Dispelling the Myths of Generative Robotic Control, by Chaoyi Pan and 10 other authors View PDF HTML (experimental) Abstract:Generative models, like flows and diffusions, have recently emerged as popular and efficacious policy parameterizations in robotics. There has been much speculation as to the factors underlying their successes, ranging from capturing multi-modal action distribution to expressing more complex behaviors. In this work, we perform a comprehensive evaluation of popular generative control policies (GCPs) on common behavior cloning (BC) benchmarks. We find that GCPs do not owe their success to their ability to capture multi-modality or to express more complex observation-to-action mappings. Instead, we find that their advantage stems from iterative computation, as long as intermediate steps are supervised during training and this supervision is paired with a suitable level of stochasticity. As a validation of our findings, we show that a minimum iterative policy (MIP), a lightweight two-step regression-based policy, essentially mat...