# Deep Imitation Learning for Bimanual Robotic Manipulation

arXiv: https://arxiv.org/abs/2010.05134 · HTML: https://mlsanity.com/paper/2010.05134/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 11 Oct 2020.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 22 of 22)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [Contrastive Learning of Structured World Models](https://mlsanity.com/paper/1911.12247/) (2019)
- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://mlsanity.com/paper/1707.03374/) (2017)
- [Flexible Neural Representation for Physics Prediction](https://mlsanity.com/paper/1806.08047/) (2018)
- [Hierarchical Imitation and Reinforcement Learning](https://mlsanity.com/paper/1803.00590/) (2018)
- [Deep Imitative Models for Flexible Inference, Planning, and Control](https://mlsanity.com/paper/1810.06544/) (2018)
- [Deep Reinforcement Learning in Parameterized Action Space](https://mlsanity.com/paper/1511.04143/) (2015)
- [Reinforcement Learning with Parameterized Actions](https://mlsanity.com/paper/1509.01644/) (2015)
- [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018)
- [Efficient Bimanual Manipulation Using Learned Task Schemas](https://mlsanity.com/paper/1909.13874/) (2019)
- [DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations](https://mlsanity.com/paper/1710.05421/) (2017)
- [One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks](https://mlsanity.com/paper/1810.11043/) (2018)
- [Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation](https://mlsanity.com/paper/1910.13395/) (2019)
- [Hierarchical Solution of Markov Decision Processes using Macro-actions](https://mlsanity.com/paper/1301.7381/) (2013)
- [Object-Oriented Dynamics Predictor](https://mlsanity.com/paper/1806.07371/) (2018)
- [Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space](https://mlsanity.com/paper/1810.09656/) (2018)
- [Hierarchical Variational Imitation Learning of Control Programs](https://mlsanity.com/paper/1912.12612/) (2019)

## Built on by (most cited first, 30 of 50)

- [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)
- [Multi-Stage Cable Routing through Hierarchical Imitation Learning](https://mlsanity.com/paper/2307.08927/) (2023)
- [FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy](https://mlsanity.com/paper/2111.05623/) (2021)
- [Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation](https://mlsanity.com/paper/2309.01952/) (2023)
- [Stabilize to Act: Learning to Coordinate for Bimanual Manipulation](https://mlsanity.com/paper/2309.01087/) (2023)
- [Transformer-based deep imitation learning for dual-arm robot manipulation](https://mlsanity.com/paper/2108.00385/) (2021)
- [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)
- [Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2408.01366/) (2024)
- [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)
- [Interactive Imitation Learning of Bimanual Movement Primitives](https://mlsanity.com/paper/2210.16220/) (2022)
- [Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks](https://mlsanity.com/paper/2403.03270/) (2024)
- [AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation](https://mlsanity.com/paper/2412.06779/) (2024)
- [Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation](https://mlsanity.com/paper/2504.00420/) (2025)
- [Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation](https://mlsanity.com/paper/2112.00597/) (2021)
- [Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control](https://mlsanity.com/paper/2502.12724/) (2025)
- [TwinVLA: Data-Efficient Bimanual Manipulation with Twin Single-Arm Vision-Language-Action Models](https://mlsanity.com/paper/2511.05275/) (2025)
- [Knowledge-augmented Deep Learning and Its Applications: A Survey](https://mlsanity.com/paper/2212.00017/) (2022)
- [SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation](https://mlsanity.com/paper/2508.18268/) (2025)
- [A Comparison of Imitation Learning Algorithms for Bimanual Manipulation](https://mlsanity.com/paper/2408.06536/) (2024)
- [One-Shot Dual-Arm Imitation Learning](https://mlsanity.com/paper/2503.06831/) (2025)
- [A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos](https://mlsanity.com/paper/2310.00670/) (2023)
- [APEX: Ambidextrous Dual-Arm Robotic Manipulation Using Collision-Free Generative Diffusion Models](https://mlsanity.com/paper/2404.02284/) (2024)
- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [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)

## Abstract (arXiv)

We present a deep imitation learning framework for robotic bimanual manipulation in a continuous state-action space. A core challenge is to generalize the manipulation skills to objects in different locations. We hypothesize that modeling the relational information in the environment can significantly improve generalization. To achieve this, we propose to (i) decompose the multi-modal dynamics into elemental movement primitives, (ii) parameterize each primitive using a recurrent graph neural network to capture interactions, and (iii) integrate a high-level planner that composes primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control. Our model is a deep, hierarchical, modular architecture. Compared to baselines, our model generalizes better and achieves higher success rates on several simulated bimanual robotic manipulation tasks. We open source the code for simulation, data, and models at: https://github.com/Rose-STL-Lab/HDR-IL.