Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation

Key points

• When training supervises future prediction alone, terminal goal accuracy is not an explicit learning objective, even when geometric recovery is available.1
• We present Entity-Level Goal Readout, a learned prediction-to-execution interface that makes the executable terminal goal an explicit output of a 3D trace world model.1
• A shared Pose-Native Executor consumes this fixed goal with online object-pose feedback for closed-loop control without rerunning the world model.1
• These results support treating the prediction-to-execution interface as an explicit learned component of world-model planning rather than incidental post-processing in the control pipeline itself.1

Sources (1)

• [1]Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:07 AM

  Generative world models provide rich predictions of how manipulation scenes may evolve toward task objectives, yet those futures do not directly expose the compact task variables required by control.

  When training supervises future prediction alone, terminal goal accuracy is not an explicit learning objective, even when geometric recovery is available.

Extractive summary: sentences quoted from the sources.