# Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation

arXiv: https://arxiv.org/abs/2509.18865 · HTML: https://mlsanity.com/paper/2509.18865/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Language-conditioned policies and VLAs, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 26 of its 32 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Placed in

- 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, 26 of 26)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [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)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [Physically Grounded Vision-Language Models for Robotic Manipulation](https://mlsanity.com/paper/2309.02561/) (2023)
- [Yell At Your Robot: Improving On-the-Fly from Language Corrections](https://mlsanity.com/paper/2403.12910/) (2024)
- [ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation](https://mlsanity.com/paper/2405.02292/) (2024)
- [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024)
- [Bridging Language and Action: A Survey of Language-Conditioned Robot Manipulation](https://mlsanity.com/paper/2312.10807/) (2023)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth](https://mlsanity.com/paper/2102.10283/) (2021)
- [An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications](https://mlsanity.com/paper/2203.08619/) (2022)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [From Cooking Recipes to Robot Task Trees -- Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network](https://mlsanity.com/paper/2309.09181/) (2023)
- [Mamba as a motion encoder for robotic imitation learning](https://mlsanity.com/paper/2409.02636/) (2024)
- [DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images](https://mlsanity.com/paper/2410.04370/) (2024)
- [Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers](https://mlsanity.com/paper/2504.01301/) (2025)
- [Hierarchical Intention Tracking for Robust Human-Robot Collaboration in Industrial Assembly Tasks](https://mlsanity.com/paper/2203.09063/) (2022)
- [Skills Made to Order: Efficient Acquisition of Robot Cooking Skills Guided by Multiple Forms of Internet Data](https://mlsanity.com/paper/2409.15172/) (2024)
- [Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning](https://mlsanity.com/paper/2412.03252/) (2024)
- [High-Speed High-Accuracy Spatial Curve Tracking Using Motion Primitives in Industrial Robots](https://mlsanity.com/paper/2301.02348/) (2023)

## Built on by (most cited first, 3 of 3)

- [TwinVLA: Data-Efficient Bimanual Manipulation with Twin Single-Arm Vision-Language-Action Models](https://mlsanity.com/paper/2511.05275/) (2025)
- [Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2603.13315/) (2026)
- [Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity](https://mlsanity.com/paper/2609.16040/) (2026)

## Abstract (arXiv)

We propose Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation (Bi-VLA), a novel framework that extends bilateral control-based imitation learning to handle more than one task within a single model. Conventional bilateral control methods exploit joint angle, velocity, torque, and vision for precise manipulation but require task-specific models, limiting their generality. Bi-VLA overcomes this limitation by utilizing robot joint angle, velocity, and torque data from leader-follower bilateral control with visual features and natural language instructions through SigLIP and FiLM-based fusion. We validated Bi-VLA on two task types: one requiring supplementary language cues and another distinguishable solely by vision. Real-robot experiments showed that Bi-VLA successfully interprets vision-language combinations and improves task success rates compared to conventional bilateral control-based imitation learning. Our Bi-VLA addresses the single-task limitation of prior bilateral approaches and provides empirical evidence that combining vision and language significantly enhances versatility. Experimental results validate the effectiveness of Bi-VLA in real-world tasks. For additional material, please visit the website: https://mertcookimg.github.io/bi-vla/