Pith Number

pith:2GGKXCCP

pith:2023:2GGKXCCPWRQOGGJMT2E2IZRBM3

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

Ajay Mandlekar, Bowen Wen, Dieter Fox, Iretiayo Akinola, Linxi Fan, Soroush Nasiriany, Yashraj Narang, Yuke Zhu

MimicGen adapts a few hundred human demonstrations into over 50,000 varied examples that train robots for long-horizon tasks.

arxiv:2310.17596 v1 · 2023-10-26 · cs.RO · cs.AI · cs.CV · cs.LG

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Claims

C1strongest claim

We show that robot agents can be effectively trained on this generated dataset by imitation learning to achieve strong performance in long-horizon and high-precision tasks, such as multi-part assembly and coffee preparation, across broad initial state distributions.

C2weakest assumption

Adapting human demonstrations to new contexts produces data that is as effective for training as real human demonstrations collected in those contexts.

C3one line summary

MimicGen creates over 50K robot demonstrations from roughly 200 human ones, allowing imitation learning to achieve strong performance on complex long-horizon tasks like assembly and coffee preparation.

References

128 extracted · 128 resolved · 13 Pith anchors

[1] Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation 2017 · arXiv:1710.04615

[2] RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation, 2018

[3] Bc- z: Zero-shot task generalization with robotic imitation learning, 2022

[4] Do As I Can, Not As I Say: Grounding Language in Robotic Affordances 2022 · arXiv:2204.01691

[5] RT-1: Robotics Transformer for Real-World Control at Scale 2022 · arXiv:2212.06817

Formal links

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Cited by

23 papers in Pith

EXPO: Stable Reinforcement Learning with Expressive Policies

R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data

IGen: Scalable Data Generation for Robot Learning from Open-World Images

Receipt and verification

First computed	2026-05-17T23:38:14.463497Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d18cab884fb460e3192c9e89a4662166da4e59e7623943866e11aa458afe9cbe

Aliases

arxiv: 2310.17596 · arxiv_version: 2310.17596v1 · doi: 10.48550/arxiv.2310.17596 · pith_short_12: 2GGKXCCPWRQO · pith_short_16: 2GGKXCCPWRQOGGJM · pith_short_8: 2GGKXCCP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/2GGKXCCPWRQOGGJMT2E2IZRBM3 \
  | 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: d18cab884fb460e3192c9e89a4662166da4e59e7623943866e11aa458afe9cbe

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2023-10-26T17:17:31Z",
    "title_canon_sha256": "d6fea5c706970428a6d8f92ede1082cd04b4b97d4b827f9d10cd54bf0974c18c"
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