Pith Number

pith:N6PYG5VG

pith:2022:N6PYG5VGWTBM4IFTG5R72R6MHQ

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VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training

Amy Zhang, Dinesh Jayaraman, Osbert Bastani, Shagun Sodhani, Vikash Kumar, Yecheng Jason Ma

VIP pre-trains a visual representation on unlabeled human videos that supplies dense rewards for many robot tasks without any fine-tuning.

arxiv:2210.00030 v2 · 2022-09-30 · cs.RO · cs.AI · cs.CV · cs.LG

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Claims

C1strongest claim

Trained on large-scale Ego4D human videos and without any fine-tuning on in-domain, task-specific data, VIP's frozen representation can provide dense visual reward for an extensive set of simulated and real-robot tasks, enabling diverse reward-based visual control methods and significantly outperforming all prior pre-trained representations.

C2weakest assumption

That a value function learned solely from unlabeled human videos (via an action-free dual goal-conditioned objective) will produce rewards that remain effective when transferred to robotic embodiments and dynamics without further adaptation.

C3one line summary

VIP learns a visual embedding from human videos whose distance defines dense, smooth rewards for arbitrary goal-image robot tasks without task-specific fine-tuning.

References

50 extracted · 50 resolved · 12 Pith anchors

[1] Human-to-robot imitation in the wild

[2] arXiv preprint arXiv:2104.07749 , year=

[3] in-the- wild

[4] Imagenet: A large-scale hierarchical image database 2009

[5] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · arXiv:1810.04805

Formal links

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Cited by

28 papers in Pith

Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own

TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance

SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization

When a Robot is More Capable than a Human: Learning from Constrained Demonstrators

Receipt and verification

First computed	2026-05-17T23:38:53.500659Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6f9f8376a6b4c2ce20b33763fd47cc3c21aff47ef3529e6361252bbae0bff9ff

Aliases

arxiv: 2210.00030 · arxiv_version: 2210.00030v2 · doi: 10.48550/arxiv.2210.00030 · pith_short_12: N6PYG5VGWTBM · pith_short_16: N6PYG5VGWTBM4IFT · pith_short_8: N6PYG5VG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/N6PYG5VGWTBM4IFTG5R72R6MHQ \
  | 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: 6f9f8376a6b4c2ce20b33763fd47cc3c21aff47ef3529e6361252bbae0bff9ff

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2022-09-30T18:14:07Z",
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