Paper Citation Record · LEDGER

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 39 inbound Pith citation observations for arXiv:1910.10897.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1910.10897 v2

Coverage vector

measured 0 of 0 reference resolution

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measured 39 of 39 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 39 of 39 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:29:56.960598Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T14:09:52.691576Z

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Outbound references

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Pith citing papers

Observation 846be998-4b5d-4b84-88b3-3d0b3627e9a5 · inbound

TD-MPC2: Scalable, Robust World Models for Continuous Control cites this paper.

TD-MPC2: Scalable, Robust World Models for Continuous Control Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 140

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:27:35.853102Z

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Observation dcf5e911-491b-4013-b9ad-76fa0aa02644 · inbound

Gymnasium: A Standard Interface for Reinforcement Learning Environments cites this paper.

Gymnasium: A Standard Interface for Reinforcement Learning Environments Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:29:49.533695Z

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Observation 032392b3-b7e5-4f61-b24a-1614214eeac3 · inbound

AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation cites this paper.

AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 14

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Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving cites this paper.

Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 42

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Observation a2e551cf-2fb8-40fc-a387-d64f945924d8 · inbound

An Open-Source Software Toolkit & Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models cites this paper.

An Open-Source Software Toolkit & Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 32

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Observation 81a6273c-6ca9-480b-9cf9-1d8e263842e3 · inbound

M3PO: Massively Multi-Task Model-Based Policy Optimization cites this paper.

M3PO: Massively Multi-Task Model-Based Policy Optimization Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 11

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Observation 8c392d02-622c-4d53-87e5-5a936ab95710 · inbound

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation cites this paper.

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 57

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Observation 914a5390-5546-4116-a704-24b43d785c39 · inbound

VLMgineer: Vision Language Models as Robotic Toolsmiths cites this paper.

VLMgineer: Vision Language Models as Robotic Toolsmiths Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 75

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning cites this paper.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 38

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Observation 38595e7e-4b50-4c39-9c3b-64849ca100ca · inbound

Equivariant Goal Conditioned Contrastive Reinforcement Learning cites this paper.

Equivariant Goal Conditioned Contrastive Reinforcement Learning Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 41

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Observation 54e0e1a9-2db9-4aba-94b0-ea133a08d417 · inbound

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks cites this paper.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 71

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Model-Based Reinforcement Learning under Random Observation Delays cites this paper.

Model-Based Reinforcement Learning under Random Observation Delays Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:36:28.692263Z

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Observation dbb18c19-446a-4473-9b6f-530e1c27b171 · inbound

DIPOLE: Fusing Vision and Geometry for Robust Visuomotor Generalization cites this paper.

DIPOLE: Fusing Vision and Geometry for Robust Visuomotor Generalization Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 45

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Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions cites this paper.

Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:31:14.973742Z

Source-reported events for the cited work

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Observation b060b4c2-c937-4879-af64-61f8eb6bad38 · inbound

Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot cites this paper.

Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 24

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Observation e5b699d9-b8bd-45e2-b267-8f2aad3b133e · inbound

Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot cites this paper.

Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 24

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Observation 1eb96b86-9466-4797-9884-693295768816 · inbound

Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation cites this paper.

Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 54

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Observation 1a8bc444-43a8-4f04-963c-e6bc63f7afc4 · inbound

Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models cites this paper.

Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 77

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OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration cites this paper.

OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:40:20.975501Z

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Observation 0caa017d-3bf2-4430-8796-f0be4dfd4438 · inbound

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies cites this paper.

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:11:05.047902Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2f0cae3d-c263-4016-9386-ec76e126e983 · inbound

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies cites this paper.

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:29:49.656170Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f2537333-ad9f-4c1c-9e01-a3072b159154 · inbound

OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation cites this paper.

OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 59

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:30:20.092660Z

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Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines cites this paper.

Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 27

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Observation 8fd0347b-5701-4638-ba50-a720e98b9bf6 · inbound

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning cites this paper.

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 23

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Observation ca892969-ab63-47de-8e28-6d7787f02cd4 · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 42

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When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 40

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Observation d1523e7b-96be-4edc-bded-f9f91f037422 · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 40

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Observation 819ffc5c-76a4-492c-a821-6c4d6cf21501 · inbound

World Action Models: The Next Frontier in Embodied AI cites this paper.

World Action Models: The Next Frontier in Embodied AI Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 221

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:18.022270Z

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Observation 21a976ec-06e3-434e-b6c2-6983734eeb25 · inbound

DSSP: Diffusion State Space Policy with Full-History Encoding cites this paper.

DSSP: Diffusion State Space Policy with Full-History Encoding Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 61

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Observation 34ebb7b3-8e34-4a12-89b7-929353053f8f · inbound

ManiSoft: Towards Vision-Language Manipulation for Soft Continuum Robotics cites this paper.

ManiSoft: Towards Vision-Language Manipulation for Soft Continuum Robotics Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 7

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AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation cites this paper.

AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:55.399084Z

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Observation c2b63716-6a45-4e35-b8c7-7ce7def21f27 · inbound

MagicSim: A Unified Infrastructure for Executable Embodied Interaction cites this paper.

MagicSim: A Unified Infrastructure for Executable Embodied Interaction Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 108

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UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning cites this paper.

UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:09:14.984897Z

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Observation 29cae255-e016-4900-bc62-da97fc6e54b2 · inbound

Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications cites this paper.

Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:29:35.490985Z

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Observation b2ef1e5d-3fa9-459f-8a35-289d1acbdf8f · inbound

World Action Models: A Survey cites this paper.

World Action Models: A Survey Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 190

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:09:35.502417Z

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Observation 8868467a-30b5-499b-b8d0-70de6622717a · inbound

Bridging Performance and Generalization in Reinforcement Learning for Agile Flight cites this paper.

Bridging Performance and Generalization in Reinforcement Learning for Agile Flight Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:09:52.693618Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 77e6409c-2337-4703-aad7-5bfe76c1ab26 · inbound

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems cites this paper.

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 33

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Observation 475cc60e-e3f6-4151-b798-6b1f392b64a8 · inbound

Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks cites this paper.

Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 54

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Observation 16198af1-af7c-42f5-9203-57d531e0a003 · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 292

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