Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:08:58.007269Z
Paper Citation Record · LEDGER
As of 16 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 28 inbound Pith citation observations for arXiv:2507.00917.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:08:58.007269Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T04:17:35.717037Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T13:14:53.255674Z
100 of 299 outbound references displayed
External citation measurements
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Observation 4108ae77-c57d-497c-b9c4-3f319949c8a5 · outbound
A Survey: Learning Embodied Intelligence from Physical Simulators and World Models GPT-4 Technical Report
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models Diffusion policy: Visuomotor policy learning via action diffusion,
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models 13 482, 2014
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models Mujoco: A physics engine for model-based control,
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models (n.d.) World models
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models Siciliano and O
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Observation 534eb0ca-c735-4dbd-929b-d7856b6d7d80 · outbound
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Reference 93
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Observation 8bd10830-826d-4709-bb79-59df07354674 · outbound
A Survey: Learning Embodied Intelligence from Physical Simulators and World Models Adversarial motion priors make good substitutes for complex reward functions,
Reference 94
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models Expres- sive Whole-Body Control for Humanoid Robots,
Reference 95
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Reference 97
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models The first human-size humanoid that can fall over safely and stand-up again,
Reference 98
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models A falling motion control of humanoid robots based on biomechanical evaluation of falling down of humans,
Reference 99
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models Resistant compliance control for biped robot inspired by humanlike behavior,
Reference 100
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models Learning Humanoid Standing-up Control across Diverse Postures
Reference 101
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Reference 152
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Reference 64
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Reference 22
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Reference 23
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Reference 41
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Reference 30
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Reference 91
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Reference 91
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Reference 12
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Reference 23
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Reference 113
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Reference 24
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Reference 35
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Reference 36
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Reference 37
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Reference 105
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