Inductive Generalization for Robotic Manipulation
Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models learn structures that transfer across domains. However, in practice, visuomotor policies test performance by interpolation on known distributions using unstructured domain shifts (e.g. lighting, clutter, diverse objects). We argue that to measure generalization capabilities we must instead test the inductive capacity of policies on progressively harder, out-of-distribution task variants. We call this inductive generalization, drawing directly on how axis-based evaluation has revealed inherent generalization limitations in language models (e.g...
| Authors | Annabella Macaluso, Haochen Zhang, Ishaan Masilamony et al. |
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| Primary category | cs.RO |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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