# CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation arXiv: https://arxiv.org/abs/2609.05832 · HTML: https://mlsanity.com/paper/2609.05832/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Tactile, force-aware and humanoid VLA, one of 13 directions in Vision-language-action models. - Builds on 27 of its 36 references that are in the map. - First posted on arXiv 5 Sep 2026. ## Placed in - Vision-language-action models / [Tactile, force-aware and humanoid VLA](https://mlsanity.com/vla/tactile-force-aware-and-humanoid-vla/) - Offline RL and imitation learning from data / [Language-conditioned policies and VLAs](https://mlsanity.com/offlinerl/language-conditioned-policies-and-vlas/) ## Builds on (most cited first, 27 of 27) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [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) - [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025) - [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023) - [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022) - [Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts](https://mlsanity.com/paper/2206.02770/) (2022) - [MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2508.19236/) (2025) - [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025) - [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025) - [Unpaired Multi-modal Segmentation via Knowledge Distillation](https://mlsanity.com/paper/2001.03111/) (2020) - [Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control](https://mlsanity.com/paper/2410.09309/) (2024) - [ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation](https://mlsanity.com/paper/2410.07554/) (2024) - [FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2411.15753/) (2024) - [A Survey of Robot Manipulation in Contact](https://mlsanity.com/paper/2112.01942/) (2021) - [TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation](https://mlsanity.com/paper/2409.11047/) (2024) - [SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich Robotic Tasks](https://mlsanity.com/paper/2406.13744/) (2024) - [CAGE: Causal Attention Enables Data-Efficient Generalizable Robotic Manipulation](https://mlsanity.com/paper/2410.14974/) (2024) - [In-the-Wild Compliant Manipulation with UMI-FT](https://mlsanity.com/paper/2601.09988/) (2026) - [Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Tasks](https://mlsanity.com/paper/2102.06838/) (2021) - [History-Aware Visuomotor Policy Learning via Point Tracking](https://mlsanity.com/paper/2509.17141/) (2025) - [ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning](https://mlsanity.com/paper/2512.10946/) (2025) - [FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.17053/) (2025) - [A Hybrid Learning and Optimization Framework to Achieve Physically Interactive Tasks with Mobile Manipulators](https://mlsanity.com/paper/2203.14613/) (2022) - [Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.19696/) (2025) - [A Passive Variable Impedance Control Strategy with Viscoelastic Parameters Estimation of Soft Tissues for Safe Ultrasonography](https://mlsanity.com/paper/2309.14893/) (2023) - [Admittance Control Parameter Adaptation for Physical Human-Robot Interaction](https://mlsanity.com/paper/1702.08376/) (2017) ## Built on by (most cited first, 1 of 1) - [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026) ## Abstract (arXiv) Integrating visuomotor policies or Vision-Language-Action (VLA) models with force/torque (F/T) perception has demonstrated significant progress in imitation learning for robotic manipulation. However, existing force-aware VLA models frequently exhibit limited capability in precise force tracking and rapid successive adjustments. This deficiency stems from the limitations of action-chunk execution strategies and the substantial latency between perception and real-time control. Such limitations can lead to task failures and safety risks, particularly when the execution of an action chunk exerts excessive interaction forces without timely adjustment. To overcome this challenge, we propose the Control-aware Compliance VLA (CC-VLA) framework for reactive control. The CC-VLA model employs a multimodal mixture-of-experts (MoE) to encode force signal sequences and vision-language fused feature. Furthermore, it utilizes a multi-stage training strategy to ensure robust perception within the visual-semantic space and effective force perception under sparse sampling conditions. Additionally, a VLA-guided adaptive compliance controller is designed to facilitate precise position tracking during contact-free motion and optimal force-position tracking for contact-rich tasks. To facilitate high-precision F/T data acquisition, we also implement an adversaria shared teleoperation strategy for contact-rich demonstrations that bolsters system safety and interactivity. Extensive real-world experiments demonstrate that CC-VLA significantly improves success rates in challenging force-perception tasks and enhances force-control precision, while providing multi-level safety and robustness under the tested partial-OOD pose-shift settings.