# CoFreeVLA: Short-Horizon Collision-Free Dual-Arm Manipulation via Vision-Language-Action Model and Risk Estimation

arXiv: https://arxiv.org/abs/2601.21712 · HTML: https://mlsanity.com/paper/2601.21712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Human-video and latent-action pretraining, one of 13 directions in Vision-language-action models.
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Placed in

- Vision-language-action models / [Human-video and latent-action pretraining](https://mlsanity.com/vla/human-video-and-latent-action-pretraining/)

## Builds on (most cited first, 26 of 26)

- [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)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [UniVLA: Learning to Act Anywhere with Task-centric Latent Actions](https://mlsanity.com/paper/2505.06111/) (2025)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2409.12514/) (2024)
- [X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model](https://mlsanity.com/paper/2510.10274/) (2025)
- [π*0.6: a VLA That Learns From Experience](https://mlsanity.com/paper/2511.14759/) (2025)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding](https://mlsanity.com/paper/2503.02310/) (2025)
- [VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback](https://mlsanity.com/paper/2507.17294/) (2025)
- [PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks](https://mlsanity.com/paper/2407.00278/) (2024)
- [Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2503.10743/) (2025)
- [A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation](https://mlsanity.com/paper/2202.12385/) (2022)
- [VFP: Variational Flow-Matching Policy for Multi-Modal Robot Manipulation](https://mlsanity.com/paper/2508.01622/) (2025)
- [ManiPose: A Comprehensive Benchmark for Pose-aware Object Manipulation in Robotics](https://mlsanity.com/paper/2403.13365/) (2024)
- [APEX: Ambidextrous Dual-Arm Robotic Manipulation Using Collision-Free Generative Diffusion Models](https://mlsanity.com/paper/2404.02284/) (2024)
- [ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation](https://mlsanity.com/paper/2510.23016/) (2025)
- [Efficient Collision Detection Framework for Enhancing Collision-Free Robot Motion](https://mlsanity.com/paper/2409.14955/) (2024)
- [VLM-SFD: VLM-Assisted Siamese Flow Diffusion Framework for Dual-Arm Cooperative Manipulation](https://mlsanity.com/paper/2506.13428/) (2025)

## Abstract (arXiv)

Vision Language Action (VLA) models enable instruction-following manipulation, yet their deployment on coordinated dual-arm platforms remains severely constrained by under-modeled self-collisions between manipulators and grasped objects. To address this critical safety gap, we propose CoFreeVLA, a novel framework that augments end-to-end VLA policies with a lightweight, short-horizon self-collision risk estimator. The estimator predicts collision likelihoods directly from proprioceptive states, visual embeddings, and candidate action sequences. Deeply integrated into the closed-loop control system, this estimator proactively gates risky commands, autonomously synthesizes recovery trajectories to safe states via risk-guided adjustments, and biases policy refinement for safer rollouts. To ensure robust calibration, the estimator utilizes a two-stage training pipeline, pre-training with model-based synthetic collision labels, followed by post-training on real-robot rollouts. Across five bimanual tasks, six VLA backbones, and 30 trials per variant, the task-averaged collision rate decreases from 0.54 to 0.23, while the task-averaged success rate increases from 0.45 to 0.61. Compared to representative baselines, CoFreeVLA substantially reduces self-collision frequencies and improves overall task success rates, providing a crucial step toward the safe deployment of foundational models in multi-arm continuous control.