Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs 事件

Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs arXiv:2606.01072v1 Announce Type: cross Abstract: Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due to their large spatial scales. In addition, many tasks involve executing a series of subtasks requiring autonomous robots to reason over extended time horizons. To address these chal