RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment
Understanding object affordances is essential for enabling robots to perform purposeful and fine-grained interactions in diverse and unstructured environments. However, existing approaches either rely on retrieval, which is fragile due to sparsity and coverage gaps, or on large-scale models, which frequently mislocalize contact points and mispredict post-contact actions when applied to unseen categories, thereby hindering robust generalization. We introduce Retrieval-Augmented Affordance Predict
Record details
Published: 31 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (31 March 2026), “RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment,” evidence record 6557, https://ethics.ai/record/6557 (originally published by arXiv).
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