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.
By Tuo An, Chuhao Zhou, Jiahua Dong, Jingliang Li, Jiaqi Bai, Jindou Jia, Jianfei Yang
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 %.
By Riccardo Andrea Izzo, Rimvydas Rubavicius, Gianluca Bardaro, Subramanian Ramamoorthy, Matteo Matteucci, Alessandro Suglia
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...
By Wuyang Luan, Junhui Li, Weiguang Zhao, Rui Ma, Tieru Wu
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.
By Zhenchen Dong, Fulin Chen, Jinna Fu, Jiaming Wu, Qingran Wu, Shengyuan Yu, Hongyu Yu, Yide Liu
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.
By Kian Hosseinkhani (Simon Fraser University), Qinhe Peng (University of Pennsylvania), George Shramko (Simon Fraser University), Mehran Aghabozorgi (Simon Fraser University), Jianing Qian (University of Pennsylvania), Tristan Engst (Simon Fraser University), Alireza Moazeni (Simon Fraser University), Dinesh Jayaraman (University of Pennsylvania), Ke Li (Simon Fraser University, Canada CIFAR AI Chair)
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.
By Chen Xu, Rishi Shah, Hadas Kress-Gazit, Haruki Nishimura, Masha Itkina
The paper proposes a method to train efficient multi‑task manipulation policies by distilling knowledge from single‑task Conditional Flow Matching (CFM) experts. Instead of training separate models for each task, the authors transfer the experts’ learned velocity fields into a shared policy, combining this distillation signal with the original CFM objective. Experiments on RLBench demonstrate that this approach improves multi‑task performance while keeping the model size fixed, avoiding the need for larger capacity or performance drops seen with naive concatenated training.
By Shreya Deshmukh, Imen Mahdi, Nick Heppert, Abhinav Valada
Task-Prototype Guided Flow Matching (TP-Flow) is a few‑shot manipulation framework that transforms support demonstrations into structured task‑prototype tokens to guide both the initial flow prior and the velocity field. It uses symmetric cross‑attention with learnable queries to extract phase‑level prototypes, parameterizes a task‑adaptive initial distribution, and injects prototype information through gated adaptive normalization. TP‑Flow is trained with an episodic support‑query objective and prototype contrastive regularization, achieving high success rates on the LEROBOT‑ARM‑SO101 platform while maintaining real‑time execution and low latency.
By Yizhao Wang, Guantao Zhang, Jingbo Wang
arXiv:2610.08183v1 Announce Type: cross
Abstract: Multi-task manipulation policies differ in architecture, scale, and pretrained priors all at once, so published comparisons cannot attribute performa...
By Nanhe Chen, Runqiu Yang, Jiawei Tang, Sichao Liu, Yuquan Wang
arXiv:2609.21216v1 Announce Type: cross
Abstract: Vision-language-action (VLA) policies based on flow matching generate action chunks through repeated evaluations of an action expert. Increasing the...
By Zhipeng Tang, Xinda Chen, Weining Rao, Xiao Li, Wenting Tan, Yuning Wang, Xiao Shi, Xiaofang Zhao
Multi-task manipulation policies differ in architecture, scale, and pretrained priors all at once, so published comparisons cannot attribute performance to any single component. We argue that most of...
The paper introduces a method for quantifying epistemic uncertainty in flow‑matching based generalist robot policies, such as vision‑language‑action models and world‑action models. By measuring velocity‑field disagreement across a small ensemble, the authors obtain better‑calibrated uncertainty estimates that can detect deployment failures and guide active fine‑tuning. Their SAVE approach reduces the need for expert demonstrations, improving real‑world task success from 39 % to 47 % while maintaining a fixed demonstration budget.
By Ralf R\"omer, Maximilian Seeliger, Saida Liu, Ben Sturgis, Marco Bagatella, Daniel Marta, Andreas Krause, Angela P. Schoellig