# Generative Artificial Intelligence in Robotic Manipulation: A Survey

arXiv: https://arxiv.org/abs/2503.03464 · HTML: https://mlsanity.com/paper/2503.03464/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2025.

## Built on by (most cited first, 16 of 16)

- [Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges](https://mlsanity.com/paper/2505.04769/) (2025)
- [Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2508.13073/) (2025)
- [Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition](https://mlsanity.com/paper/2510.01068/) (2025)
- [Growing with Your Embodied Agent: A Human-in-the-Loop Lifelong Code Generation Framework for Long-Horizon Manipulation Skills](https://mlsanity.com/paper/2509.18597/) (2025)
- [From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation](https://mlsanity.com/paper/2605.11951/) (2026)
- [ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems](https://mlsanity.com/paper/2510.07151/) (2025)
- [ALRM: Agentic LLM for Robotic Manipulation](https://mlsanity.com/paper/2601.19510/) (2026)
- [Retrieval-Augmented Robots via Retrieve-Reason-Act](https://mlsanity.com/paper/2603.02688/) (2026)
- [RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes](https://mlsanity.com/paper/2510.00154/) (2025)
- [3D Generation for Embodied AI and Robotic Simulation: A Survey](https://mlsanity.com/paper/2604.26509/) (2026)
- [RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?](https://mlsanity.com/paper/2609.05324/) (2026)
- [A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model](https://mlsanity.com/paper/2510.19128/) (2025)
- [VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis](https://mlsanity.com/paper/2606.00053/) (2026)
- [Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs](https://mlsanity.com/paper/2606.18747/) (2026)
- [Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations](https://mlsanity.com/paper/2606.30807/) (2026)
- [A Comprehensive Review of Generative Physical Artificial Intelligence](https://mlsanity.com/paper/2609.18111/) (2026)

## Abstract (arXiv)

This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulation faces critical bottlenecks, including significant challenges in insufficient data and inefficient data acquisition, long-horizon and complex task planning, and the multi-modality reasoning ability for robust policy learning performance across diverse environments. To tackle these challenges, this survey introduces several generative model paradigms, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, probabilistic flow models, and autoregressive models, highlighting their strengths and limitations. The applications of these models are categorized into three hierarchical layers: the Foundation Layer, focusing on data generation and reward generation; the Intermediate Layer, covering language, code, visual, and state generation; and the Policy Layer, emphasizing grasp generation and trajectory generation. Each layer is explored in detail, along with notable works that have advanced the state of the art. Finally, the survey outlines future research directions and challenges, emphasizing the need for improved efficiency in data utilization, better handling of long-horizon tasks, and enhanced generalization across diverse robotic scenarios. All the related resources, including research papers, open-source data, and projects, are collected for the community in https://github.com/GAI4Manipulation/AwesomeGAIManipulation