Freeform Preference Learning (FPL) for Robotic Manipulation
Introduces multi-axis preference supervision to learn dense, language-conditioned rewards across speed/precision/subtask axes without segmentation; enables compositional generalization and better long-horizon credit assignment than single-reward baselines.
The skinny
The skinny isn't ready yet — notes appear once the transcript is processed.