pith:6HXPIGK7
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
By matching semantic keypoints across 3D meshes, AffordGen generates varied manipulation trajectories that let trained policies succeed on objects never seen in the original data.
arxiv:2604.10579 v2 · 2026-04-12 · cs.RO · cs.AI
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\pithnumber{6HXPIGK7KW5HIUIKZNVDNQDNVX}
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Record completeness
Claims
Experiments in simulation and the real world show that policies trained with AffordGen achieve high success rates and enable zero-shot generalization to truly unseen objects, significantly improving data efficiency in robot learning.
That semantic correspondence of meaningful keypoints across large-scale 3D meshes can reliably generate new, valid, and useful robot manipulation trajectories that transfer to real-world closed-loop control.
AffordGen generates affordance-aware manipulation demonstrations from 3D mesh correspondences to train policies with zero-shot generalization to novel objects.
Receipt and verification
| First computed | 2026-06-02T01:03:46.920492Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f1eef4195f55ba74510acb6a36c06dadc93071b02a1d55f53b7a7e8924ffe8cb
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6HXPIGK7KW5HIUIKZNVDNQDNVX \
| 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: f1eef4195f55ba74510acb6a36c06dadc93071b02a1d55f53b7a7e8924ffe8cb
Canonical record JSON
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