# HRP: Human Affordances for Robotic Pre-Training

arXiv: https://arxiv.org/abs/2407.18911 · HTML: https://mlsanity.com/paper/2407.18911/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 74 of its 106 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 30 of 74)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training](https://mlsanity.com/paper/2210.00030/) (2022)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction](https://mlsanity.com/paper/2203.01577/) (2022)

## Built on by (most cited first, 30 of 44)

- [Latent Action Pretraining from Videos](https://mlsanity.com/paper/2410.11758/) (2024)
- [DreamGen: Unlocking Generalization in Robot Learning through Video World Models](https://mlsanity.com/paper/2505.12705/) (2025)
- [FLARE: Robot Learning with Implicit World Modeling](https://mlsanity.com/paper/2505.15659/) (2025)
- [ExBody2: Advanced Expressive Humanoid Whole-Body Control](https://mlsanity.com/paper/2412.13196/) (2024)
- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation](https://mlsanity.com/paper/2411.02704/) (2024)
- [DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies](https://mlsanity.com/paper/2505.07813/) (2025)
- [Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations](https://mlsanity.com/paper/2507.00990/) (2025)
- [ZeroMimic: Distilling Robotic Manipulation Skills from Web Videos](https://mlsanity.com/paper/2503.23877/) (2025)
- [Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets](https://mlsanity.com/paper/2410.22325/) (2024)
- [UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation](https://mlsanity.com/paper/2506.09284/) (2025)
- [DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy](https://mlsanity.com/paper/2506.20668/) (2025)
- [ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations](https://mlsanity.com/paper/2510.01607/) (2025)
- [Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations](https://mlsanity.com/paper/2511.16661/) (2025)
- [AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons](https://mlsanity.com/paper/2503.03081/) (2025)
- [MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction](https://mlsanity.com/paper/2602.03668/) (2026)
- [MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos](https://mlsanity.com/paper/2504.06084/) (2025)
- [Learning to Transfer Human Hand Skills for Robot Manipulations](https://mlsanity.com/paper/2501.04169/) (2025)
- [Developing Vision-Language-Action Model from Egocentric Videos](https://mlsanity.com/paper/2509.21986/) (2025)
- [How Do I Do That? Synthesizing 3D Hand Motion and Contacts for Everyday Interactions](https://mlsanity.com/paper/2504.12284/) (2025)
- [2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos](https://mlsanity.com/paper/2503.09320/) (2025)
- [Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining](https://mlsanity.com/paper/2506.16475/) (2025)
- [H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos](https://mlsanity.com/paper/2512.09406/) (2025)
- [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026)
- [DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation](https://mlsanity.com/paper/2605.30350/) (2026)
- [Human Universal Grasping](https://mlsanity.com/paper/2606.17054/) (2026)
- [Scaling Single Human Demonstrations for Imitation Learning using Generative Foundational Models](https://mlsanity.com/paper/2602.12734/) (2026)
- [Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer](https://mlsanity.com/paper/2501.16389/) (2025)
- [Concept Drift Guided LayerNorm Tuning for Efficient Multimodal Metaphor Identification](https://mlsanity.com/paper/2505.11237/) (2025)
- [PAC Bench: Do Foundation Models Understand Prerequisites for Executing Manipulation Policies?](https://mlsanity.com/paper/2506.23725/) (2025)

## Abstract (arXiv)

In order to *generalize* to various tasks in the wild, robotic agents will need a suitable representation (i.e., vision network) that enables the robot to predict optimal actions given high dimensional vision inputs. However, learning such a representation requires an extreme amount of diverse training data, which is prohibitively expensive to collect on a real robot. How can we overcome this problem? Instead of collecting more robot data, this paper proposes using internet-scale, human videos to extract "affordances," both at the environment and agent level, and distill them into a pre-trained representation. We present a simple framework for pre-training representations on hand, object, and contact "affordance labels" that highlight relevant objects in images and how to interact with them. These affordances are automatically extracted from human video data (with the help of off-the-shelf computer vision modules) and used to fine-tune existing representations. Our approach can efficiently fine-tune *any* existing representation, and results in models with stronger downstream robotic performance across the board. We experimentally demonstrate (using 3000+ robot trials) that this affordance pre-training scheme boosts performance by a minimum of 15% on 5 real-world tasks, which consider three diverse robot morphologies (including a dexterous hand). Unlike prior works in the space, these representations improve performance across 3 different camera views. Quantitatively, we find that our approach leads to higher levels of generalization in out-of-distribution settings. For code, weights, and data check: https://hrp-robot.github.io