MOSS: Mode-Oriented Source Shaping for Robot Flow Matching Policies
We propose Mode-Oriented Source Shaping (MOSS), which turns mode selection into an explicit choice of where generation starts.
ProofPaper ↗
Key points
- In robotic manipulation, the same observation often admits multiple feasible actions.
- Generative policies such as diffusion and flow matching naturally capture such multimodal behaviors, yet which mode is executed is left to random sampling and cannot be actively controlled.
- We first reveal, both empirically and theoretically, that deterministic flow transport induces mode-dependent structure in the source space.
- Across 6 simulated and 3 real-world manipulation tasks, MOSS steers to the target mode up to 100% of the time with high task success, recovers minority modes at over 90% guidance rate under a 10:90 imbalance dataset, and surpasses mode-conditioned flow policy by 43.3 points of guidance rate on real robots.
Sources (1)
- [1]MOSS: Mode-Oriented Source Shaping for Robot Flow Matching PoliciesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 04:44 PM
We propose Mode-Oriented Source Shaping (MOSS), which turns mode selection into an explicit choice of where generation starts.
In robotic manipulation, the same observation often admits multiple feasible actions.
Extractive summary: sentences quoted from the sources.
Before this
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