Pith Number

pith:U4MTKKD6

pith:2019:U4MTKKD6J2K4NXHPRO6GIPLQBZ

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RoboNet: Large-Scale Multi-Robot Learning

Bernadette Bucher, Chelsea Finn, Frederik Ebert, Karl Schmeckpeper, Sergey Levine, Siddharth Singh, Stephen Tian, Sudeep Dasari, Suraj Nair

Pre-training on a shared dataset from seven robots lets new arms learn tasks with far less data than training from scratch on the target platform alone.

arxiv:1910.11215 v2 · 2019-10-24 · cs.RO · cs.CV · cs.LG

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4 Citations open

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Claims

C1strongest claim

by pre-training on RoboNet and fine-tuning on data from a held-out Franka or Kuka robot, we can exceed the performance of a robot-specific training approach that uses 4x-20x more data.

C2weakest assumption

That visual features and dynamics learned across the seven source robots transfer meaningfully to a held-out robot without large unmodeled domain gaps in gripper mechanics, camera calibration, or task distribution.

C3one line summary

RoboNet is a multi-robot video dataset that enables pre-training of vision-based manipulation models which, after fine-tuning on a new robot, outperform robot-specific training that uses 4-20 times more data.

References

60 extracted · 60 resolved · 25 Pith anchors

[1] arXiv preprint arXiv:1611.04201 , year= 2016 · arXiv:1611.04201

[2] Pachocki, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba 2018 · arXiv:1808.00177

[3] M. Deisenroth and C. E. Rasmussen. Pilco: A model-based and data-efﬁcient approach to policy search. In International Conference on machine learning (ICML), 2011 2011

[4] M. P. Deisenroth, D. Fox, and C. E. Rasmussen. Gaussian processes for data-efﬁcient learning in robotics and control. IEEE transactions on pattern analysis and machine intelligence, 37(2):408–423, 201 2013

[5] C. Finn, I. Goodfellow, and S. Levine. Unsupervised learning for physical interaction through video prediction. In Advances in neural information processing systems, pages 64–72, 2016 2016

Formal links

2 machine-checked theorem links

Cited by

20 papers in Pith

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

RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation

Scaling Robot Learning with Semantically Imagined Experience

MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

GR-3 Technical Report

Receipt and verification

First computed	2026-05-17T23:38:13.330998Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a71935287e4e95c6dcef8bbc643d700e44e4f5aa25789e185d58177d83487e30

Aliases

arxiv: 1910.11215 · arxiv_version: 1910.11215v2 · doi: 10.48550/arxiv.1910.11215 · pith_short_12: U4MTKKD6J2K4 · pith_short_16: U4MTKKD6J2K4NXHP · pith_short_8: U4MTKKD6

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/U4MTKKD6J2K4NXHPRO6GIPLQBZ \
  | 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: a71935287e4e95c6dcef8bbc643d700e44e4f5aa25789e185d58177d83487e30

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "cd43cb4b88979d65a940dfe632b1973e590df89eebd501f3fd0f5f50b0c58635",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2019-10-24T15:20:03Z",
    "title_canon_sha256": "1a783d605025f6949105edd8faff2929dc00f7f34cd0c8fb48fc60e3c6c94264"
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  "source": {
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    "kind": "arxiv",
    "version": 2
  }
}