EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
cs.RO, cs.AI
Submitted: 2026-10-06
Updated: 2026-10-06
Project page: https://ego-lap.github.io
Sources
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- OpenVLA: An Open-Source Vision-Language-Action Model
- World Action Models are Zero-shot Policies
- Grounded Human-Object Interaction Hotspots from Video
- HRP: Human Affordances for Robotic Pre-Training
- Zero-Shot Robot Manipulation from Passive Human Videos
- Any-point Trajectory Modeling for Policy Learning
- Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers
- Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
- Human-to-Robot Imitation in the Wild
- Phantom: Training Robots Without Robots Using Only Human Videos
- Masquerade: Learning from In-the-wild Human Videos using Data-Editing
- In-N-On: Scaling Egocentric Manipulation with in-the-wild and on-task Data
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- Ordered Action Tokens for Visuomotor Policy Learning
- LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer
- Actions as Language: Fine-Tuning VLMs into VLAs Without Catastrophic Forgetting
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