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FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies
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FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies
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Developing efficient Vision-Language-Action (VLA) policies is crucial for practical robotics deployment, yet current approaches face prohibitive computational costs and resource requirements. Existing diffusion-based VLA policies require multi-billion-parameter models and massive datasets to achieve strong performance. We tackle this efficiency challenge with two contributions: intermediate-modality fusion, which reallocates capacity to the diffusion head by pruning up to $50\%$ of LLM layers, and action-specific Global-AdaLN conditioning, which cuts parameters by $20\%$ through modular adaptation. We integrate these advances into a novel 950 M-parameter VLA called FLOWER. Pretrained in just 200 H100 GPU hours, FLOWER delivers competitive performance with bigger VLAs across $190$ tasks spanning ten simulation and real-world benchmarks and demonstrates robustness across diverse robotic embodiments. In addition, FLOWER achieves a new SoTA of 4.53 on the CALVIN ABC benchmark. Demos, code and pretrained weights are available at https://intuitive-robots.github.io/flower_vla/.
Forward citations
Cited by 23 Pith papers
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PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space
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QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
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Action latents supervised by both control reconstruction and environment-conditioned visual consequences outperform trajectory-centric tokenizers for robotic manipulation, especially long-horizon tasks.
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Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure
ReStruct steers robot policies at inference time by reconfiguring task structure with neural automata and synchronous products, claiming up to 25% gains over VLA models in success and preference adherence.
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ASCII Art Turns LLMs into VLA Controllers
ASCII rendering of visual states enables fine-tuned text-only LLMs to serve as VLA controllers that identify objects and generate feasible action sequences in 2D manipulation benchmarks in simulation and on hardware.
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Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory
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Flow-based Policy Adaptation without Policy Updates
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MPCoT: Reward-Guided Multi-Path Latent Reasoning for Test-Time Scalable Vision-Language-Action
MPCoT improves long-horizon VLA performance on LIBERO and CALVIN by initializing M latent hypotheses, refining them over K steps, and aggregating via a reward-trained path scorer while preserving the original 8-step a...
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Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories
ENAP extracts an emergent Mealy automaton from visuomotor trajectories to act as a high-level planner for a low-level residual policy, yielding up to 27% higher success than end-to-end VLA policies in low-data regimes.
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OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism
OxyGen unifies KV cache management in MoT VLAs to enable cross-task KV sharing and cross-frame continuous batching, delivering up to 3.7x speedup with 200+ tokens/s language and 70 Hz action on on-device platforms.
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VLANeXt: Recipes for Building Strong VLA Models
VLANeXt distills 12 design insights from a unified VLA study into a model that outperforms prior methods on LIBERO benchmarks while releasing code for further exploration.
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Robot-DIFT: Correspondence-Sensitive Diffusion Features for Contact-Rich Robot Manipulation
Distilling Stable Diffusion's decoder features into a deterministic student backbone with a multi-scale fusion network improves contact-rich manipulation success in simulation and on a real robot.
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ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
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mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
mimic-video combines internet video pretraining with a flow-matching decoder to achieve state-of-the-art robotic manipulation performance with 10x better sample efficiency than vision-language-action models.
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Semantic Anchoring for Robotic Action Representations
Anchoring a VLA's mid-layer action features to a frozen semantic encoder during fine-tuning, with shared/private decomposition, improves in-distribution and out-of-distribution robot manipulation success without chang...
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Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
An RL data generation pipeline with generalizable rewards and language annotations produces diverse synthetic datasets that improve multi-task policy generalization on three bimanual manipulation tasks.
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QuoVLA: Quotient Space for Vision-Language-Action Models
QuoVLA introduces a quotient-space framework that compresses VLM latents into action-sufficient representations via quantization and dual-branch design for better VLA generalization.
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PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
PointACT proposes a 3D-aware dual-system VLA policy using multi-scale point-action interaction with bottleneck window self-attention, achieving 10% higher success rates on RLBench-10Tasks over prior pretrained VLAs.
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Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance
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AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
AVA-VLA reformulates VLA learning as a POMDP using recurrent states and active visual attention to achieve state-of-the-art results on LIBERO, CALVIN, and real dual-arm tasks.
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Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
RL pipeline with generalizable rewards and domain randomization generates datasets that improve generalization of language-conditioned bimanual policies on three manipulation tasks.
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ERQA-Plus: A Diagnostic Benchmark for Reasoning in Embodied AI
ERQA-Plus is a new benchmark dataset with a structured taxonomy for evaluating fine-grained embodied reasoning in AI agents across perceptual, action-centric, social, navigation, and commonsense categories.
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