# Towards Generalist Robot Learning from Internet Video: A Survey

arXiv: https://arxiv.org/abs/2404.19664 · HTML: https://mlsanity.com/paper/2404.19664/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 223 of its 307 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 30 of 223)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)

## Built on by (most cited first, 30 of 40)

- [Latent Action Pretraining from Videos](https://mlsanity.com/paper/2410.11758/) (2024)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [DreamGen: Unlocking Generalization in Robot Learning through Video World Models](https://mlsanity.com/paper/2505.12705/) (2025)
- [Large Video Planner Enables Generalizable Robot Control](https://mlsanity.com/paper/2512.15840/) (2025)
- [Latent Action Learning Requires Supervision in the Presence of Distractors](https://mlsanity.com/paper/2502.00379/) (2025)
- [CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning](https://mlsanity.com/paper/2505.17006/) (2025)
- [AVID: Adapting Video Diffusion Models to World Models](https://mlsanity.com/paper/2410.12822/) (2024)
- [What Do Latent Action Models Actually Learn?](https://mlsanity.com/paper/2506.15691/) (2025)
- [RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization](https://mlsanity.com/paper/2602.03310/) (2026)
- [Grounding Video Models to Actions through Goal Conditioned Exploration](https://mlsanity.com/paper/2411.07223/) (2024)
- [Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow](https://mlsanity.com/paper/2512.24766/) (2025)
- [TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2510.00739/) (2025)
- [RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation](https://mlsanity.com/paper/2604.19092/) (2026)
- [AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons](https://mlsanity.com/paper/2503.03081/) (2025)
- [AMPLIFY: Actionless Motion Priors for Robot Learning from Videos](https://mlsanity.com/paper/2506.14198/) (2025)
- [Solving New Tasks by Adapting Internet Video Knowledge](https://mlsanity.com/paper/2504.15369/) (2025)
- [OpenEgo: A Large-Scale Multimodal Egocentric Dataset for Dexterous Manipulation](https://mlsanity.com/paper/2509.05513/) (2025)
- [StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation](https://mlsanity.com/paper/2510.05057/) (2025)
- [6D Object Pose Tracking in Internet Videos for Robotic Manipulation](https://mlsanity.com/paper/2503.10307/) (2025)
- [Object-Centric Latent Action Learning](https://mlsanity.com/paper/2502.09680/) (2025)
- [Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals](https://mlsanity.com/paper/2601.05848/) (2026)
- [Self-Improving Loops for Visual Robotic Planning](https://mlsanity.com/paper/2506.06658/) (2025)
- [Large-scale Pre-training for Grounded Video Caption Generation](https://mlsanity.com/paper/2503.10781/) (2025)
- [EgoDTM: Towards 3D-Aware Egocentric Video-Language Pretraining](https://mlsanity.com/paper/2503.15470/) (2025)
- [D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI](https://mlsanity.com/paper/2510.05684/) (2025)
- [Learning Skills from Action-Free Videos](https://mlsanity.com/paper/2512.20052/) (2025)
- [CARE: Multi-Task Pretraining for Latent Continuous Action Representation in Robot Control](https://mlsanity.com/paper/2601.22467/) (2026)
- [Vision-Language Models Unlock Task-Centric Latent Actions](https://mlsanity.com/paper/2601.22714/) (2026)
- [Information-Theoretic Policy Pre-Training with Empowerment](https://mlsanity.com/paper/2510.05996/) (2025)
- [Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation](https://mlsanity.com/paper/2601.02778/) (2026)

## Abstract (arXiv)

Scaling deep learning to massive and diverse internet data has driven remarkable breakthroughs in domains such as video generation and natural language processing. Robot learning, however, has thus far failed to replicate this success and remains constrained by a scarcity of available data. Learning from videos (LfV) methods aim to address this data bottleneck by augmenting traditional robot data with large-scale internet video. This video data provides foundational information regarding physical dynamics, behaviours, and tasks, and can be highly informative for general-purpose robots. This survey systematically examines the emerging field of LfV. We first outline essential concepts, including detailing fundamental LfV challenges such as distribution shift and missing action labels in video data. Next, we comprehensively review current methods for extracting knowledge from large-scale internet video, overcoming LfV challenges, and improving robot learning through video-informed training. The survey concludes with a critical discussion of future opportunities. Here, we emphasize the need for scalable foundation model approaches that can leverage the full range of available internet video and enhance the learning of robot policies and dynamics models. Overall, the survey aims to inform and catalyse future LfV research, driving progress towards general-purpose robots.