Pegasus用图结构把人类视频变成机器人能学的数据，不用砸钱搞硬件采集了，跨形态效果不错。
Pegasus提出基于图结构的知识转移框架，通过Task Graph从人类操作视频中提取信息，再经Affordance和Constraint Graphs转化为Robot Planning Graph，用于机器人条件视频生成。该框架在GTEA Gaze+和EPIC-KITCHENS-100等基准上评估了Task Correctness、Executability、State Consistency和Learnability，实现了可靠的跨形态翻译。Pegasus将机器人数据生成从硬件采集问题重新定义为可扩展的低资源知识转移问题。
From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence
The key bottleneck in embodied AI is not model architecture but data. Although billions of human manipulation videos exist online, robots cannot directly learn from them due to the embodiment gap between human morphology and robot hardware. We introduce Pegasus, a low-resource framework that bridges this gap by translating human demonstrations into robot-learnable data through structured knowledge transfer. Instead of relying on raw video prompts, Pegasus constructs a graph-based intermediate representation: a Task Graph extracted from human videos is transformed through Affordance and Constraint Graphs into a Robot Planning Graph for robot-conditioned video generation. A hierarchical affordance latent space models the relationship between object states, affordances, and tasks, enabling generalization beyond object identities. A closed-loop physics verifier further filters invalid generations using kinematic feasibility, collision constraints, and joint limits. We evaluate Pegasus across a range of egocentric manipulation benchmarks, including GTEA Gaze+ and EPIC-KITCHENS-100, and diverse robot embodiments, assessing Task Correctness, Executability, State Consistency, and Learnability. Results demonstrate reliable cross-embodiment translation and show that robot data generation can be reframed from a hardware collection problem into a scalable, low-resource knowledge transfer problem.