# Stabilize to Act: Learning to Coordinate for Bimanual Manipulation

arXiv: https://arxiv.org/abs/2309.01087 · HTML: https://mlsanity.com/paper/2309.01087/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 3 Sep 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning](https://mlsanity.com/paper/2206.08686/) (2022)
- [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021)
- [SpeedFolding: Learning Efficient Bimanual Folding of Garments](https://mlsanity.com/paper/2208.10552/) (2022)
- [Deep Imitation Learning for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2010.05134/) (2020)
- [Efficient Bimanual Manipulation Using Learned Task Schemas](https://mlsanity.com/paper/1909.13874/) (2019)
- [A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies](https://mlsanity.com/paper/2208.00596/) (2022)
- [Influencing Towards Stable Multi-Agent Interactions](https://mlsanity.com/paper/2110.08229/) (2021)
- [Untangling Dense Knots by Learning Task-Relevant Keypoints](https://mlsanity.com/paper/2011.04999/) (2020)
- [Learning Bimanual Scooping Policies for Food Acquisition](https://mlsanity.com/paper/2211.14652/) (2022)
- [Interactive Imitation Learning of Bimanual Movement Primitives](https://mlsanity.com/paper/2210.16220/) (2022)
- [Leveraging Multimodal Haptic Sensory Data for Robust Cutting](https://mlsanity.com/paper/1909.12460/) (2019)
- [Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2203.08277/) (2022)
- [Learning to Scaffold the Development of Robotic Manipulation Skills](https://mlsanity.com/paper/1911.00969/) (2019)
- [Intrinsic Motivation for Encouraging Synergistic Behavior](https://mlsanity.com/paper/2002.05189/) (2020)
- [It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying](https://mlsanity.com/paper/2209.12890/) (2022)
- [Learning from My Partner's Actions: Roles in Decentralized Robot Teams](https://mlsanity.com/paper/1910.07613/) (2019)
- [A Certified-Complete Bimanual Manipulation Planner](https://mlsanity.com/paper/1705.02573/) (2017)
- [Compliant Movement Primitives in a Bimanual Setting](https://mlsanity.com/paper/1707.04629/) (2017)
- [Robust Planning for Multi-stage Forceful Manipulation](https://mlsanity.com/paper/2208.00319/) (2022)
- [Manipulation Planning under Changing External Forces](https://mlsanity.com/paper/1710.11190/) (2017)

## Built on by (most cited first, 30 of 39)

- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024)
- [Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning](https://mlsanity.com/paper/2407.03162/) (2024)
- [Learning Visuotactile Skills with Two Multifingered Hands](https://mlsanity.com/paper/2404.16823/) (2024)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback](https://mlsanity.com/paper/2410.08464/) (2024)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop](https://mlsanity.com/paper/2506.10968/) (2025)
- [InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation](https://mlsanity.com/paper/2409.07914/) (2024)
- [Bi-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Dexterous Manipulations](https://mlsanity.com/paper/2405.06039/) (2024)
- [VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation](https://mlsanity.com/paper/2407.04152/) (2024)
- [Dexterous Manipulation through Imitation Learning: A Survey](https://mlsanity.com/paper/2504.03515/) (2025)
- [3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation](https://mlsanity.com/paper/2508.11002/) (2025)
- [ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection](https://mlsanity.com/paper/2405.03666/) (2024)
- [AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation](https://mlsanity.com/paper/2412.06779/) (2024)
- [Learning Diverse Bimanual Dexterous Manipulation Skills from Human Demonstrations](https://mlsanity.com/paper/2410.02477/) (2024)
- [ManiGaussian++: General Robotic Bimanual Manipulation with Hierarchical Gaussian World Model](https://mlsanity.com/paper/2506.19842/) (2025)
- [SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation](https://mlsanity.com/paper/2508.18268/) (2025)
- [Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation](https://mlsanity.com/paper/2412.02676/) (2024)
- [One-Shot Dual-Arm Imitation Learning](https://mlsanity.com/paper/2503.06831/) (2025)
- [HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation](https://mlsanity.com/paper/2406.14655/) (2024)
- [LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language](https://mlsanity.com/paper/2503.17309/) (2025)
- [Lemmanaid: Neuro-Symbolic Lemma Conjecturing](https://mlsanity.com/paper/2504.04942/) (2025)
- [BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2406.10093/) (2024)
- [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (2025)
- [BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly](https://mlsanity.com/paper/2506.06221/) (2025)
- [ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning](https://mlsanity.com/paper/2412.00396/) (2024)
- [DA-VIL: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control](https://mlsanity.com/paper/2410.19712/) (2024)
- [ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation](https://mlsanity.com/paper/2509.19454/) (2025)
- [FLAM: Foundation Model-Based Body Stabilization for Humanoid Locomotion and Manipulation](https://mlsanity.com/paper/2503.22249/) (2025)

## Abstract (arXiv)

Key to rich, dexterous manipulation in the real world is the ability to coordinate control across two hands. However, while the promise afforded by bimanual robotic systems is immense, constructing control policies for dual arm autonomous systems brings inherent difficulties. One such difficulty is the high-dimensionality of the bimanual action space, which adds complexity to both model-based and data-driven methods. We counteract this challenge by drawing inspiration from humans to propose a novel role assignment framework: a stabilizing arm holds an object in place to simplify the environment while an acting arm executes the task. We instantiate this framework with BimanUal Dexterity from Stabilization (BUDS), which uses a learned restabilizing classifier to alternate between updating a learned stabilization position to keep the environment unchanged, and accomplishing the task with an acting policy learned from demonstrations. We evaluate BUDS on four bimanual tasks of varying complexities on real-world robots, such as zipping jackets and cutting vegetables. Given only 20 demonstrations, BUDS achieves 76.9% task success across our task suite, and generalizes to out-of-distribution objects within a class with a 52.7% success rate. BUDS is 56.0% more successful than an unstructured baseline that instead learns a BC stabilizing policy due to the precision required of these complex tasks. Supplementary material and videos can be found at https://sites.google.com/view/stabilizetoact .