# ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models

arXiv: https://arxiv.org/abs/2506.16211 · HTML: https://mlsanity.com/paper/2506.16211/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning and post-training, one of 13 directions in Vision-language-action models.
- Builds on 58 of its 62 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Placed in

- Vision-language-action models / [Reinforcement learning and post-training](https://mlsanity.com/vla/reinforcement-learning-and-post-training/)

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [Learning Interactive Real-World Simulators](https://mlsanity.com/paper/2310.06114/) (2023)
- [Lumiere: A Space-Time Diffusion Model for Video Generation](https://mlsanity.com/paper/2401.12945/) (2024)
- [VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training](https://mlsanity.com/paper/2210.00030/) (2022)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [An Embodied Generalist Agent in 3D World](https://mlsanity.com/paper/2311.12871/) (2023)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)

## Built on by (most cited first, 30 of 43)

- [GWM: Towards Scalable Gaussian World Models for Robotic Manipulation](https://mlsanity.com/paper/2508.17600/) (2025)
- [A Survey on Efficient Vision-Language-Action Models](https://mlsanity.com/paper/2510.24795/) (2025)
- [dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought](https://mlsanity.com/paper/2509.25681/) (2025)
- [ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations](https://mlsanity.com/paper/2510.01607/) (2025)
- [Can World Simulators Reason? Gen-ViRe: A Generative Visual Reasoning Benchmark](https://mlsanity.com/paper/2511.13853/) (2025)
- [Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends](https://mlsanity.com/paper/2506.20966/) (2025)
- [MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models](https://mlsanity.com/paper/2606.13515/) (2026)
- [Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations](https://mlsanity.com/paper/2511.22697/) (2025)
- [TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation](https://mlsanity.com/paper/2601.20321/) (2026)
- [PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies](https://mlsanity.com/paper/2509.18282/) (2025)
- [SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation](https://mlsanity.com/paper/2511.06754/) (2025)
- [Mind to Hand: Purposeful Robotic Control via Embodied Reasoning](https://mlsanity.com/paper/2512.08580/) (2025)
- [OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation](https://mlsanity.com/paper/2604.17876/) (2026)
- [Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation](https://mlsanity.com/paper/2605.30282/) (2026)
- [Survey of Vision-Language-Action Models for Embodied Manipulation](https://mlsanity.com/paper/2508.15201/) (2025)
- [RoDyn: Taming Interactive Robot-Dynamic 2.5D World Model for Robotic Manipulation](https://mlsanity.com/paper/2510.09036/) (2025)
- [X-Tokenizer: A Multimodal Action Tokenizer for Vision-Language-Action Pretraining](https://mlsanity.com/paper/2606.14752/) (2026)
- [Breaking Lock-In: Preserving Steerability under Low-Data VLA Post-Training](https://mlsanity.com/paper/2604.23121/) (2026)
- [VLA^2: Empowering Vision-Language-Action Models with an Agentic Framework for Unseen Concept Manipulation](https://mlsanity.com/paper/2510.14902/) (2025)
- [PosA-VLA: Enhancing Action Generation via Pose-Conditioned Anchor Attention](https://mlsanity.com/paper/2512.03724/) (2025)
- [Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models](https://mlsanity.com/paper/2605.29074/) (2026)
- [GraSP-VLA: Graph-based Symbolic Action Representation for Long-Horizon Planning with VLA Policies](https://mlsanity.com/paper/2511.04357/) (2025)
- [ST-VLA: Enabling 4D-Aware Spatiotemporal Understanding for General Robot Manipulation](https://mlsanity.com/paper/2603.13788/) (2026)
- [TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning](https://mlsanity.com/paper/2605.12236/) (2026)
- [Towards Predictive, Aligned, and Scalable Robot Learning](https://mlsanity.com/paper/2607.11270/) (2026)
- [StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models](https://mlsanity.com/paper/2603.07307/) (2026)
- [ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation](https://mlsanity.com/paper/2605.30484/) (2026)
- [FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation](https://mlsanity.com/paper/2606.20867/) (2026)
- [SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation](https://mlsanity.com/paper/2608.05970/) (2026)
- [FT-NCFM: An Influence-Aware Data Distillation Framework for Efficient VLA Models](https://mlsanity.com/paper/2511.16233/) (2025)

## Abstract (arXiv)

Learning real-world robotic manipulation is challenging, particularly when limited demonstrations are available. Existing methods for few-shot manipulation often rely on simulation-augmented data or pre-built modules like grasping and pose estimation, which struggle with sim-to-real gaps and lack extensibility. While large-scale imitation pre-training shows promise, adapting these general-purpose policies to specific tasks in data-scarce settings remains unexplored. To achieve this, we propose ControlVLA, a novel framework that bridges pre-trained VLA models with object-centric representations via a ControlNet-style architecture for efficient fine-tuning. Specifically, to introduce object-centric conditions without overwriting prior knowledge, ControlVLA zero-initializes a set of projection layers, allowing them to gradually adapt the pre-trained manipulation policies. In real-world experiments across 6 diverse tasks, including pouring cubes and folding clothes, our method achieves a 76.7% success rate while requiring only 10-20 demonstrations -- a significant improvement over traditional approaches that require more than 100 demonstrations to achieve comparable success. Additional experiments highlight ControlVLA's extensibility to long-horizon tasks and robustness to unseen objects and backgrounds.