AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation
- Published
- Source
- arXiv
- Paper number
- 552
- Field
- Robotics
- arXiv ID
- 2607.01051
Key points
- The first framework to infer stage-adaptive motion speeds implicitly through end-to-end learning, without speed or stage annotations.
- DCT-based frequency-domain scaling enables acceleration and deceleration by non-integer factors while preserving high-frequency action details.
- Multi-target selective optimization uses the composite cost J = E/w + log(h) to manage the trade-off between prediction error and horizon length.
- The model-agnostic design applies to both non-generative policies based on MLPs and generative policies based on diffusion or flow.
- Across four real-robot tasks, the method cut execution time by a factor of 1.78 on average. On the Place the Toy task, it improved the success rate by 10 percentage points.
- The inferred speed ratios showed a strong correspondence with task stages such as reaching, assembly, and precision insertion.
Paper links
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