# Mamba as a motion encoder for robotic imitation learning

arXiv: https://arxiv.org/abs/2409.02636 · HTML: https://mlsanity.com/paper/2409.02636/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Video, motion and generative Mamba models](https://mlsanity.com/ssm/video-motion-and-generative-mamba-models/)
- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

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

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [VIMA: General Robot Manipulation with Multimodal Prompts](https://mlsanity.com/paper/2210.03094/) (2022)
- [Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images](https://mlsanity.com/paper/1506.07365/) (2015)
- [UniT: Multimodal Multitask Learning with a Unified Transformer](https://mlsanity.com/paper/2102.10772/) (2021)
- [What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data](https://mlsanity.com/paper/2204.06252/) (2022)
- [RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation](https://mlsanity.com/paper/2406.04339/) (2024)
- [A Survey of Resource-efficient LLM and Multimodal Foundation Models](https://mlsanity.com/paper/2401.08092/) (2024)
- [MUTEX: Learning Unified Policies from Multimodal Task Specifications](https://mlsanity.com/paper/2309.14320/) (2023)
- [Imitation Learning by Estimating Expertise of Demonstrators](https://mlsanity.com/paper/2202.01288/) (2022)
- [Differentiable Spatial Planning using Transformers](https://mlsanity.com/paper/2112.01010/) (2021)
- [Multimodal VAE Active Inference Controller](https://mlsanity.com/paper/2103.04412/) (2021)
- [Variable impedance control and learning -- A review](https://mlsanity.com/paper/2010.06246/) (2020)
- [Multimodal representation models for prediction and control from partial information](https://mlsanity.com/paper/1910.03854/) (2019)
- [MaIL: Improving Imitation Learning with Mamba](https://mlsanity.com/paper/2406.08234/) (2024)
- [Motion Planning Transformers: A Motion Planning Framework for Mobile Robots](https://mlsanity.com/paper/2106.02791/) (2021)
- [ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer](https://mlsanity.com/paper/2401.16653/) (2024)
- [Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE](https://mlsanity.com/paper/2111.10954/) (2021)

## Built on by (most cited first, 7 of 7)

- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [MTIL: Encoding Full History with Mamba for Temporal Imitation Learning](https://mlsanity.com/paper/2505.12410/) (2025)
- [Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation](https://mlsanity.com/paper/2509.18865/) (2025)
- [AnoleVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation](https://mlsanity.com/paper/2603.15046/) (2026)
- [ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System](https://mlsanity.com/paper/2411.09942/) (2024)
- [Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics](https://mlsanity.com/paper/2509.04737/) (2025)
- [ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation](https://mlsanity.com/paper/2609.35200/) (2026)

## Abstract (arXiv)

Recent advancements in imitation learning, particularly with the integration of LLM techniques, are set to significantly improve robots' dexterity and adaptability. This paper proposes using Mamba, a state-of-the-art architecture with potential applications in LLMs, for robotic imitation learning, highlighting its ability to function as an encoder that effectively captures contextual information. By reducing the dimensionality of the state space, Mamba operates similarly to an autoencoder. It effectively compresses the sequential information into state variables while preserving the essential temporal dynamics necessary for accurate motion prediction. Experimental results in tasks such as cup placing and case loading demonstrate that despite exhibiting higher estimation errors, Mamba achieves superior success rates compared to Transformers in practical task execution. This performance is attributed to Mamba's structure, which encompasses the state space model. Additionally, the study investigates Mamba's capacity to serve as a real-time motion generator with a limited amount of training data.