GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation
Classification
View four quadrants- Major category
- WAMs
- Quadrant
- Not assigned
- Architecture
- Not assigned
- Prediction paradigm
- Not assigned
- Subcategories
- Generalization & action alignment
- Source review status
- Not assigned
Category review. The paper contributes task-specific structural supervision and evaluates complete VLA and WAM variants, including a future-generating model with inverse-dynamics action extraction. A WAM methods placement is supported, while no single architecture quadrant describes every variant. Reading evidence
Contribution
GIFT trains robot-policy features to retain geometry, object–end-effector relations and instruction-relevant regions. The same auxiliary objectives improve three different action formulations while their default deployment uses no auxiliary predictions. Evidence is strongest for matched-baseline robustness gains, with substantial annotation requirements and uneven benefits across shifts.
Abstract
An abstract has not been added yet.
Affiliations
Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Tsinghua University; Fudan University; National University of Singapore