Awesome-Dexterous-Manipulation

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Awesome-Dexterous-Manipulation

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A curated awesome list for dexterous robot manipulation, tactile sensing, dexterous hands, robot learning, datasets, benchmarks, and simulators.

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<h1 id="awesome-dexterous-manipulation">Awesome-Dexterous-Manipulation</h1>

<p>
  <strong>A curated map of papers, hardware, sensors, methods, datasets, benchmarks, and infrastructure for dexterous robot manipulation.</strong>
</p>

</div>

## About

**Awesome-Dexterous-Manipulation** is a curated list for the ecosystem around dexterous robot manipulation: dexterous hands, tactile sensing, hand-centric tasks, robot learning, optimal control, teleoperation, benchmarks, datasets, and simulators.

This list is intentionally **dexterity-first**. It includes strong adjacent manipulation work only when it materially helps understand, build, evaluate, or deploy dexterous manipulation systems.

<p align="center">
  <img src="assets/dexterous-manipulation-examples.png" alt="Examples of dexterous manipulation systems and tasks" width="900">
  <br>
  <sub><span style="color: #777;">Source: Fig. 1 from <a href="https://arxiv.org/abs/2504.03515">Dexterous Manipulation through Imitation Learning: A Survey</a>.</span></sub>
</p>

## Must Read

Start here if you want the shortest path through the field.

| Goal | Start with |
| :-- | :-- |
| Understand classic dexterous manipulation | [A Mathematical Introduction to Robotic Manipulation](https://www.cds.caltech.edu/~murray/mlswiki/index.php/Main_Page), [A framework for planning dexterous manipulation](https://dblp.org/rec/conf/icra/TrinkleH91) |
| Learn modern in-hand RL | [Learning Dexterous In-Hand Manipulation](https://arxiv.org/abs/1808.00177), [Learning Complex Dexterous Manipulation with Deep RL and Demonstrations](https://arxiv.org/abs/1709.10087), [HORA: In-Hand Object Rotation via Rapid Motor Adaptation](https://arxiv.org/abs/2210.04887) |
| Study dexterous hands | [LEAP Hand](https://v1.leaphand.com/), [Shadow Dexterous Hand](https://www.shadowrobot.com/dexterous-hand-series/), [Allegro Hand](https://github.com/Wonikrobotics-git/allegro_hand_ros_v5) |
| Study tactile sensing | [TacThru](https://tacthru.yuyang.li/), [GelSight](https://gelsight.com/), [DIGIT](https://digit.ml/), [ReSkin](https://reskin.dev/), [Awesome Touch](https://github.com/linchangyi1/Awesome-Touch) |
| Build visuo-tactile object-state pipelines | [Tac2Pose](https://arxiv.org/abs/2204.11701), [FoundPose](https://arxiv.org/abs/2311.18809), [FoundationPose](https://arxiv.org/abs/2312.08344), [Point2Pose](https://arxiv.org/abs/2604.10415), [AlignPose](https://arxiv.org/abs/2512.20538) |
| Find benchmarks and datasets | [DexYCB](https://dex-ycb.github.io/), [DexGraspNet](https://pku-epic.github.io/DexGraspNet/), [Adroit](https://github.com/Farama-Foundation/D4RL/wiki/Tasks#adroit), [Shadow Hand environments](https://isaac-sim.github.io/IsaacLab/main/source/overview/environments.html) |
| Build with simulators | [Isaac Lab](https://isaac-sim.github.io/IsaacLab/), [MuJoCo](https://mujoco.org/), [SAPIEN](https://sapien.ucsd.edu/), [PyBullet](https://github.com/bulletphysics/bullet3) |
| Explore data collection | [DexDirect](https://arxiv.org/abs/2607.27784), [ACE-Data-0](https://arxiv.org/abs/2607.28625), [DexCap](https://arxiv.org/abs/2403.07788), [DexUMI](https://arxiv.org/abs/2505.21864), [DexWild](https://dexwild.github.io/), [Open-TeleVision](https://arxiv.org/abs/2407.01512) |

## News

- 2026-06-24: Realigned the README navigation to the canonical five-section taxonomy and folded enabling-system material into Infrastructure.

## Contents

- [Awesome-Dexterous-Manipulation](#awesome-dexterous-manipulation)
  - [About](#about)
  - [Must Read](#must-read)
  - [News](#news)
  - [Hardware & Sensor Systems](#-1-hardware--sensor-systems)
    - [Dexterous Hands](#11-dexterous-hands)
    - [Tactile Sensors](#12-tactile-sensors)
    - [Multi-Modal Fusion Setups](#13-multi-modal-fusion-setups)
  - [Dexterity Capabilities & Tasks](#-2-dexterity-capabilities--tasks)
    - [Prehensile Manipulation](#21-prehensile-manipulation)
    - [Non-Prehensile Manipulation](#22-non-prehensile-manipulation)
    - [Dynamic & Agile Manipulation](#23-dynamic--agile-manipulation)
    - [Complex & Specialized Tasks](#24-complex--specialized-tasks)
  - [Methodology](#-3-methodology)
    - [Reinforcement Learning](#31-reinforcement-learning)
    - [Imitation Learning](#32-imitation-learning)
    - [Vision-Language-Action & Foundation Models](#33-vision-language-action-vla--foundation-models)
    - [Trajectory Optimization & Optimal Control](#34-trajectory-optimization--optimal-control)
    - [Data Collection Paradigms](#35-data-collection-paradigms)
  - [Infrastructure](#-4-infrastructure)
    - [Simulators](#41-simulators)
    - [Benchmarks & Datasets](#42-benchmarks--datasets)
    - [Enabling Methods & System Utilities](#43-enabling-methods--system-utilities)
      - [Grasp & Initialization](#431-grasp--initialization)
      - [Retargeting](#432-retargeting)
      - [Perception & Tracking](#433-perception--tracking)
      - [Calibration & Modeling](#434-calibration--modeling)
      - [Evaluation & Robustness](#435-evaluation--robustness)
  - [Surveys & Reviews](#-5-surveys--reviews)
  - [Acknowledgement](#acknowledgement)
  - [Citation](#citation)

<h2 id="-1-hardware--sensor-systems">🦾 1. Hardware & Sensor Systems</h2>

Physical systems and sensing layers for dexterous manipulation.

### 1.1 Dexterous Hands

#### Anthropomorphic Hands

#### Non-Anthropomorphic & Multi-Finger Grippers

### 1.2 Tactile Sensors

#### Vision-Based Tactile

#### Capacitive & Piezoresistive

### 1.3 Multi-Modal Fusion Setups

#### Visuo-Tactile Integration

#### Proprioception & Force-Torque

<h2 id="-2-dexterity-capabilities--tasks">🖐️ 2. Dexterity Capabilities & Tasks</h2>

Hand-centric capabilities, organized by manipulation taxonomy rather than by only individual benchmark names.

### 2.1 Prehensile Manipulation

#### In-Hand Reorientation / Rotation

#### Finger Gaiting / Regrasping

#### Tool Use & Extrinsic Dexterity

### 2.2 Non-Prehensile Manipulation

#### Pushing, Sliding & Flipping

| Date | Keywords | Institute (first) | Paper / Resource | Publication | Others |
| :--: | :------: | :---------------: | :--------------- | :---------: | :----: |
| 2016-09-28 | Pushing, Planar Manipulation, Learning | MIT | [Learning to Push by Grasping: Using Multiple Tasks for Effective Learning](https://arxiv.org/abs/1609.09025) | ICRA 2017 | [paper](https://arxiv.org/abs/1609.09025) |

#### Rolling & Pivoting

### 2.3 Dynamic & Agile Manipulation

#### Catching, Throwing & Juggling

#### Pen Spinning / Twirling / Fine Motor Skills

### 2.4 Complex & Specialized Tasks

#### Bimanual Dexterous Manipulation

#### Deformable Object Manipulation

<h2 id="-3-methodology">🧠 3. Methodology</h2>

Learning, planning, control, and data-pipeline methods for dexterous manipulation.

### 3.1 Reinforcement Learning

#### Sim-to-Real Transfer

#### Reward Design & State Estimation

### 3.2 Imitation Learning

#### Behavior Cloning & Action Chunking

#### Diffusion Policies

| Date | Keywords | Institute (first) | Paper / Resource | Publication | Others |
| :--: | :------: | :---------------: | :--------------- | :---------: | :----: |
| 2026-07-30 | Frequency-Adaptive, Visual-Force, Reactive Diffusion, Contact-Rich | Shanghai Jiao Tong University | [FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation](https://arxiv.org/abs/2607.28596) | arXiv | [project](https://fa-rdp.github.io/) |
| 2026-07-28 | Reactive, Flow Matching, Proprioception, XHand, Real-Time | Carnegie Mellon University | [$\pi\mathbf{R}^2$: Reactive Real-time Flow Policies](https://arxiv.org/abs/2607.26055) | arXiv | [project](https://pi-r2-flow.github.io/) |
| 2026-07-19 | Asynchronous Fusion, Vision+Force, Reactive Diffusion Policy | Shanghai Jiao Tong | [Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion](https://arxiv.org/abs/2607.17257) | arXiv | [project](https://lag-fusion.github.io/) |
| 2025-08-24 | Long-Horizon, Synthetic Data, Skill Routing | HKU | [LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations](https://arxiv.org/abs/2508.17547) | arXiv | [paper](https://arxiv.org/abs/2508.17547) |
| 2025-08-20 | Force Closure, Differentiable Optimization, Grasp Synthesis | ETH Zurich | [GraspQP: Differentiable Optimization of Force Closure for Diverse and Robust Dexterous Grasping](https://arxiv.org/abs/2508.15002) | IROS 2025 | [paper](https://arxiv.org/abs/2508.15002) |
| 2025-06-17 | Sim-to-Real, Clutter, Diffusion Policy | Tsinghua | [ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes](https://arxiv.org/abs/2506.14317) | CoRL 2025 | [project](https://clutterdexgrasp.github.io/) |
| 2025-03-03 | Soft Hands, Diffusion Policy, Proprioceptive | University of Michigan | [KineSoft: Learning Proprioceptive Manipulation Policies with Soft Robot Hands](https://arxiv.org/abs/2503.01078) | ICRA 2025 | [paper](https://arxiv.org/abs/2503.01078) |
| 2024-12-19 | Dexterous Fine-Tuning, Real-World Hands | UC Berkeley | [DEFT: Dexterous Fine-Tuning for Real-World Hand Policies](https://dexterous-finetuning.github.io/) | Website | [project](https://dexterous-finetuning.github.io/) |
| 2024-10-25 | Diffusion Policy, Compliant Manipulation, Contact-Rich, Stiffness Control | University of Tokyo | [DIPCOM: Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation](https://arxiv.org/abs/2410.19235) | arXiv | [paper](https://arxiv.org/abs/2410.19235) |
| 2024-10-10 | Bimanual, Diffusion Foundation, Manipulation | Tsinghua | [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://arxiv.org/abs/2410.07864) | arXiv | [project](https://rdt-robotics.github.io/rdt-robotics/) / [github](https://github.com/thu-ml/RoboticsDiffusionTransformer) |
| 2024-07-21 | Flow Matching, Uncertainty-Aware, Dexterous Grasping | TUM | [FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference](https://arxiv.org/abs/2407.15161) | ICRA 2025 | [paper](https://arxiv.org/abs/2407.15161) |
| 2024-03-06 | 3D Diffusion, Point Cloud, Dexterous | Zhejiang University | [DP3: 3D Diffusion Policy for Generalizable Visuomotor Policy Learning](https://arxiv.org/abs/2403.03954) | ICRA 2025 | [project](https://3d-diffusion-policy.github.io/) |
| 2023-03-07 | Diffusion Policy, Visuomotor, Robot Learning | Columbia | [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://arxiv.org/abs/2303.04137) | RSS 2023 | [project](https://diffusion-policy.cs.columbia.edu/) / [github](https://github.com/real-stanford/diffusion_policy) |

### 3.3 Vision-Language-Action (VLA) & Foundation Models

#### Dexterous VLA Models

#### Zero-shot / Few-shot Dexterity

### 3.4 Trajectory Optimization & Optimal Control

#### Trajectory Optimization

#### Kinematics & Motion Planning

### 3.5 Data Collection Paradigms

<p align="center">
  <img src="assets/dexterous-teleoperation-frameworks.png" alt="Teleoperation frameworks and commonly used devices for dexterous manipulation" width="900">
  <br>
  <sub><span style="color: #777;">Source: teleoperation framework figure from <a href="https://arxiv.org/abs/2504.03515">Dexterous Manipulation through Imitation Learning: A Survey</a>.</span></sub>
</p>

#### Vision-based Teleoperation

#### Exoskeleton & Glove-based

#### Passive Video & Internet Data Extraction

<h2 id="-4-infrastructure">🛠️ 4. Infrastructure</h2>

Reusable simulators, benchmark suites, datasets, robot descriptions, and training resources.

### 4.1 Simulators

#### GPU-Accelerated

#### CPU-based & High-Fidelity Physics

### 4.2 Benchmarks & Datasets

#### Dexterous Grasping Datasets

#### Hand-Object & Bimanual Datasets

#### Teleoperation & Task Benchmarks

### 4.3 Enabling Methods & System Utilities

Adjacent tasks that often determine whether a dexterous manipulation system can be initialized, transferred, sensed, calibrated, and evaluated reliably.

#### Grasp & Initialization

#### Retargeting

#### Perception & Tracking

#### Calibration & Modeling

#### Evaluation & Robustness

<a id="-6-surveys--reviews"></a>
<h2 id="-5-surveys--reviews">📚 5. Surveys & Reviews</h2>

## Acknowledgement

This list follows the single-file, compact-table style of [Awesome-UMI](https://github.com/chang-xinhai/Awesome-UMI) and cross-checks scope against related public resources:

- [Dexterous-Manipulation](https://github.com/kingchou007/Dexterous-Manipulation)
- [awesome-humanoid-manipulation](https://github.com/Tsunami-kun/awesome-humanoid-manipulation)
- [Awesome-Robotics-Manipulation](https://github.com/BaiShuanghao/Awesome-Robotics-Manipulation)
- [Awesome-Touch](https://github.com/linchangyi1/Awesome-Touch)
- [awesome-robot-descriptions](https://github.com/robot-descriptions/awesome-robot-descriptions)

## Citation

If this repository helps your work, please cite or link it as:

```bibtex
@misc{awesome_dexterous_manipulation,
  title        = {Awesome-Dexterous-Manipulation},
  author       = {Xinhai Chang},
  year         = {2026},
  howpublished = {\url{https://github.com/chang-xinhai/Awesome-Dexterous-Manipulation}},
}
```

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