AI RESEARCH
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
arXiv CS.CV
•
ArXi:2606.01072v1 Announce Type: cross 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 challenges, we propose using scene graphs as an explicit and structured memory mechanism in imitation learning.