# One-to-Two Acting: A Novel Framework for Single-arm Agent Action Expansion to Dual Arms

arXiv: https://arxiv.org/abs/2606.19897 · HTML: https://mlsanity.com/paper/2606.19897/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 29 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 23 of 23)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2409.12514/) (2024)
- [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023)
- [RoCo: Dialectic Multi-Robot Collaboration with Large Language Models](https://mlsanity.com/paper/2307.04738/) (2023)
- [VideoDex: Learning Dexterity from Internet Videos](https://mlsanity.com/paper/2212.04498/) (2022)
- [Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics](https://mlsanity.com/paper/2411.13587/) (2024)
- [CoPAL: Corrective Planning of Robot Actions with Large Language Models](https://mlsanity.com/paper/2310.07263/) (2023)
- [TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models](https://mlsanity.com/paper/2403.17246/) (2024)
- [CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation](https://mlsanity.com/paper/2402.14795/) (2024)
- [Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data](https://mlsanity.com/paper/2305.19075/) (2023)
- [LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation](https://mlsanity.com/paper/2310.12020/) (2023)
- [On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning](https://mlsanity.com/paper/2601.06748/) (2026)
- [Video OWL-ViT: Temporally-consistent open-world localization in video](https://mlsanity.com/paper/2308.11093/) (2023)
- [Dynamic Mobile Manipulation via Whole-Body Bilateral Teleoperation of a Wheeled Humanoid](https://mlsanity.com/paper/2307.01350/) (2023)
- [LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language](https://mlsanity.com/paper/2503.17309/) (2025)
- [Synchronized Dual-arm Rearrangement via Cooperative mTSP](https://mlsanity.com/paper/2403.08191/) (2024)
- [BestMan: A Modular Mobile Manipulator Platform for Embodied AI with Unified Simulation-Hardware APIs](https://mlsanity.com/paper/2410.13407/) (2024)
- [Learning Dual-arm Object Rearrangement for Cartesian Robots](https://mlsanity.com/paper/2402.13634/) (2024)

## Abstract (arXiv)

Dual-arm manipulation can improve throughput via parallel execution, but collecting bimanual demonstrations for training is costly and difficult. We present ExS2D, a hierarchical action expansion framework that enables dual-arm manipulation from single-arm supervision. ExS2D first generates structured subtasks from textual instructions while explicitly capturing temporal precedence. It then grounds each subtask into executable actions through subtask-guided action mapping in observation. Finally, precedence-aware action allocation and synchronized planning are performed by a multimodal large language model driven coordinator to select collision-free dual-arm executions. Simulation experiments demonstrate that ExS2D reduces the average execution steps by 54.4% while maintaining a comparable success rate to a single-arm baseline. Real-robot experiments on four tasks further demonstrate the reliability of ExS2D for dual-arm execution under few-shot single-arm samples, while using zero bimanual demonstrations.