Hugging Face Trending Papers
TOPS: Reshaping Flow Source and Transport for Multi-Task Robot Manipulation
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arXiv AI
4d ago
TOPS: Reshaping Flow Source and Transport for Multi-Task Robot Manipulation
TOPS (Task‑Oriented Priors and Schedules) adapts flow‑based policies for language‑conditioned multi‑task robot manipulation by customizing the flow source prior and interpolation schedule per task without changing the network architecture. It constructs multimodal task sources from reverse‑mapped expert action sequences, allowing language instructions to select the generation start point, and adjusts the denoising schedule based on source‑target covariance mismatch to reduce velocity‑prediction risk. Experiments on LIBERO, LIBERO‑Plus, CALVIN, and real‑world tasks show significant performance gains, including a jump from 53.44 % to 69.67 % success on LIBERO‑10 with a single integration step and a five‑fold reduction in inference time.
arXiv Machine Learning
Sep 25
Decoupled Early Exits for Task-Dependent Compute Allocation in Flow-Matching VLAs
The paper introduces a framework for Flow‑Matching Vision‑Language‑Action (VLA) models that allows independent adjustment of backbone depth, action expert depth, and denoising steps. Lightweight Exit Transformers are added at intermediate layers to enable early exits, and a KV Cache synthesis mechanism manages skipped layers so the action expert can exit deeper than the backbone. Experiments on SmolVLA and π0.5 across LIBERO and Meta‑World show that joint tuning of these compute axes reduces latency by 79.2 % and FLOPs by 31.8 %, while improving mean success rate by 5.6 %.
arXiv Computer Vision
Oct 15
SnapFlow: One-Step Action Generation for Flow-Matching VLAs via Progressive Self-Distillation
arXiv:2604.05656v2 Announce Type: replace Abstract: Vision-language-action (VLA) models with flow-matching action heads, such as {\pi}0 and {\pi}0.5, have become a standard recipe for generalist robo...
arXiv AI
Sep 23
HybridFlow: A 2-NFE Generative Policy for Real-Time Robotic Manipulation
HybridFlow is a generative policy for robotic manipulation that uses a three‑stage inference procedure requiring only two network function evaluations (2‑NFE). The policy first generates a coarse action trajectory with a Global Jump based on MeanFlow, then refines the state using a parameter‑free ReNoise interpolation, and finally performs a Local Refine to query the instantaneous‑velocity limit. Experiments on RoboMimic and five real‑robot settings show that HybridFlow achieves high success rates and improves task performance over a 16‑step Diffusion Policy while reducing action‑generation latency by roughly eightfold.
arXiv Computer Vision
Sep 11
IMLE-VLA: Fast Single-Step Action Generation for Vision-Language-Action Policies
IMLE‑VLA replaces the iterative action head in vision‑language‑action policies with a single‑step conditional generator trained via conditional Implicit Maximum Likelihood Estimation (cIMLE). This eliminates multi‑step sampling, boosting inference frequency by 3.67× (55 Hz vs. 15 Hz) and achieving the highest average success rate (98.0 %) on the 40‑task LIBERO benchmark while maintaining robustness under perturbations. Real‑world tests on a Franka Emika Panda show smoother, faster motions and a 3.9×–6.6× reduction in inference time per episode.
arXiv AI
Sep 24
The Gaussian Is Enough: Flow-Matching Priors Do Not Help When Fine-Tuning Large Behavior Models
The paper investigates whether using non‑Gaussian priors improves fine‑tuning of large behavior models (LBMs) for robot imitation learning. Across more than 100,000 simulation rollouts and 1,250 hardware trials on diverse tasks, the authors find that non‑Gaussian priors do not yield better fine‑tuning performance than standard Gaussian priors, except possibly at very low data fractions. Diagnostic analyses reveal that encoder training dominates fine‑tuning outcomes, while prior choice has minimal impact.