# Efficient Bimanual Manipulation Using Learned Task Schemas

arXiv: https://arxiv.org/abs/1909.13874 · HTML: https://mlsanity.com/paper/1909.13874/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 30 of 45)

- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models](https://mlsanity.com/paper/2401.03360/) (2023)
- [Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks](https://mlsanity.com/paper/2110.03655/) (2021)
- [Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives](https://mlsanity.com/paper/2110.15360/) (2021)
- [FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy](https://mlsanity.com/paper/2111.05623/) (2021)
- [Deep Imitation Learning for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2010.05134/) (2020)
- [Stabilize to Act: Learning to Coordinate for Bimanual Manipulation](https://mlsanity.com/paper/2309.01087/) (2023)
- [Dynamic Handover: Throw and Catch with Bimanual Hands](https://mlsanity.com/paper/2309.05655/) (2023)
- [LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation](https://mlsanity.com/paper/2210.12631/) (2022)
- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)
- [InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation](https://mlsanity.com/paper/2409.07914/) (2024)
- [VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation](https://mlsanity.com/paper/2407.04152/) (2024)
- [Learning Bimanual Scooping Policies for Food Acquisition](https://mlsanity.com/paper/2211.14652/) (2022)
- [ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection](https://mlsanity.com/paper/2405.03666/) (2024)
- [DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation](https://mlsanity.com/paper/2207.01971/) (2022)
- [STAP: Sequencing Task-Agnostic Policies](https://mlsanity.com/paper/2210.12250/) (2022)
- [Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration](https://mlsanity.com/paper/2108.06038/) (2021)
- [AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation](https://mlsanity.com/paper/2412.06779/) (2024)
- [AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots](https://mlsanity.com/paper/2603.07648/) (2026)
- [Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models](https://mlsanity.com/paper/2407.02666/) (2024)
- [DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning](https://mlsanity.com/paper/2406.09953/) (2024)
- [Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation](https://mlsanity.com/paper/2504.00420/) (2025)
- [Large Language Models for Orchestrating Bimanual Robots](https://mlsanity.com/paper/2404.02018/) (2024)
- [Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment](https://mlsanity.com/paper/2311.01059/) (2023)
- [PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning](https://mlsanity.com/paper/2403.00929/) (2024)
- [One-Shot Dual-Arm Imitation Learning](https://mlsanity.com/paper/2503.06831/) (2025)
- [Meta-Learning Parameterized Skills](https://mlsanity.com/paper/2206.03597/) (2022)
- [Lemmanaid: Neuro-Symbolic Lemma Conjecturing](https://mlsanity.com/paper/2504.04942/) (2025)

## Abstract (arXiv)

We address the problem of effectively composing skills to solve sparse-reward tasks in the real world. Given a set of parameterized skills (such as exerting a force or doing a top grasp at a location), our goal is to learn policies that invoke these skills to efficiently solve such tasks. Our insight is that for many tasks, the learning process can be decomposed into learning a state-independent task schema (a sequence of skills to execute) and a policy to choose the parameterizations of the skills in a state-dependent manner. For such tasks, we show that explicitly modeling the schema's state-independence can yield significant improvements in sample efficiency for model-free reinforcement learning algorithms. Furthermore, these schemas can be transferred to solve related tasks, by simply re-learning the parameterizations with which the skills are invoked. We find that doing so enables learning to solve sparse-reward tasks on real-world robotic systems very efficiently. We validate our approach experimentally over a suite of robotic bimanual manipulation tasks, both in simulation and on real hardware. See videos at http://tinyurl.com/chitnis-schema.