Research

Humanoid Network builds on established research in imitation learning, egocentric vision, and decentralized data markets.

Foundational Work

The platform draws from several key research areas:

Imitation Learning

Learning robot policies from human demonstrations. Key approaches include behavioral cloning (BC), inverse reinforcement learning (IRL), and diffusion policies.

Egocentric Vision

First-person video understanding for action recognition, hand-object interaction detection, and spatial reasoning. Projects like Ego4D and EgoExo4D have demonstrated the value of egocentric datasets.

Sim-to-Real Transfer

Using simulation environments to pre-train policies, then fine-tuning with real-world demonstration data. Humanoid Network provides the real-world data layer that complements synthetic training.

How HAN Data Is Used

Robotics teams consume HAN data for:

1. Behavioral cloning: Directly learning action policies from demonstrations
2. Reward learning: Inferring reward functions from human task completions
3. Data augmentation: Supplementing lab-collected datasets with diverse real-world examples
4. Benchmark evaluation: Testing generalization across environments and body types

Open Research Questions

Humanoid Network is actively exploring: - Optimal quest design for maximizing downstream policy performance - Quality metrics that predict training value (not just recording quality) - Privacy-preserving data sharing (federated learning, differential privacy) - Cross-embodiment transfer from human demonstrations to diverse robot morphologies

Publications

Research publications and technical reports will be listed here as they are released.