pith:BF4S6654
Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
Reinforcement learning via GRPO with a custom affordance reward function produces zero-shot generalization and emergent test-time reasoning in multimodal models for robot affordance grounding.
arxiv:2508.06206 v5 · 2025-08-08 · cs.RO · cs.CV
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Claims
Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Affordance-R1 achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities.
The custom affordance function containing format, perception, and cognition rewards will steer the GRPO optimization toward generalizable cognitive reasoning rather than overfitting to training distributions or reward specifics, as implied by the claim of emergent capabilities from RL-only training.
Affordance-R1 applies GRPO-based reinforcement learning to multimodal LLMs for affordance grounding, using format-perception-cognition rewards and the ReasonAff dataset to achieve zero-shot generalization and emergent reasoning.
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| First computed | 2026-05-21T01:04:15.766254Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
09792f7bbc05f0d846600cdc41efafff6834f3c836aa21e98cfff82244b8febf
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BF4S6654AXYNQRTABTOED35P75 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 09792f7bbc05f0d846600cdc41efafff6834f3c836aa21e98cfff82244b8febf
Canonical record JSON
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