The paper introduces an inductive generalization evaluation protocol for manipulation policies and shows that SOTA vision-language-action models fail on progressively harder task variants.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
NoFA-BC proposes a non-forgetting allocator using recursive least-squares and bi-level competition for improved knowledge allocation in class-incremental learning.
citing papers explorer
-
Inductive Generalization for Robotic Manipulation
The paper introduces an inductive generalization evaluation protocol for manipulation policies and shows that SOTA vision-language-action models fail on progressively harder task variants.
-
Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning
NoFA-BC proposes a non-forgetting allocator using recursive least-squares and bi-level competition for improved knowledge allocation in class-incremental learning.