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Paper Citation Record · LEDGER

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control

As of 17 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2607.20110.

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

pith.paper-citation-record.v1
2607.20110 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T10:49:04.512869Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

61 of 61 outbound references displayed

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Outbound references

Observation b89bb417-ea50-4334-97b0-5b5d507a32e0 · outbound

This paper cites The role of delib- erate practice in the acquisition of expert performance,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control The role of delib- erate practice in the acquisition of expert performance,

Reference 1

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Observation eb3a03d3-f20a-4212-b7d3-c935b3d43064 · outbound

This paper cites How do you learn to walk? Thousands of steps and dozens of falls per day,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control How do you learn to walk? Thousands of steps and dozens of falls per day,

Reference 2

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source=pdf_text observed=2026-08-01T10:48:58.447292Z digest=sha256:ce335a8e54eda520f28299967dc4e14eba842fc396f2b963e011b02293b8d838

Observation adc36edb-49ba-4d97-83f9-15e38500d133 · outbound

This paper cites Consolidation in human motor memory,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Consolidation in human motor memory,

Reference 3

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source=pdf_text observed=2026-08-01T10:48:58.563322Z digest=sha256:2fa5e021e4ad0cd704eb85fddc646dbd962eb56b679b81dc070d828d5ce79f54

Observation 2225e3a3-f467-47af-bb2e-a3890d638e53 · outbound

This paper cites SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

Reference 4

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source=pdf_text observed=2026-08-01T10:48:58.733550Z digest=sha256:c1901ea581ad8e9bfae7f6f51dd0a007d2bb5670c3d9f13e38785fc2de737174

Observation 30b1ed6a-8a41-437c-bf2c-89644ffc7247 · outbound

This paper cites Robust and generalized humanoid motion tracking,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Robust and generalized humanoid motion tracking,

Reference 5

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source=pdf_text observed=2026-08-01T10:48:58.899312Z digest=sha256:633af88457d7eaaad10812a42c887484b9bce96d5cff8dec664e0e3f53192625

Observation b52181f2-a450-48ba-9571-1695e0d6d4e6 · outbound

This paper cites GMT: General Motion Tracking for Humanoid Whole-Body Control.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control GMT: General Motion Tracking for Humanoid Whole-Body Control

Reference 6

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source=pdf_text observed=2026-08-01T10:48:58.999431Z digest=sha256:bd9b721e7389df25ce4a8a17161ea3d93b3d6712654991315bea5aed10036908

Observation 8cc8eeb0-a3dd-49bb-b6df-649c5f84dd9e · outbound

This paper cites UniTracker: Learning universal whole-body motion tracker for humanoid robots,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control UniTracker: Learning universal whole-body motion tracker for humanoid robots,

Reference 7

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source=pdf_text observed=2026-08-01T10:48:59.060482Z digest=sha256:66409fcc3ad11a0a08d069223ad105e981814c0f91c5db3b25c3f1ad928479af

Observation e8a09a74-87f2-4fd5-91f0-46b7fbbfe706 · outbound

This paper cites KungfuBot2: Learning versatile motion skills for humanoid whole-body control,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control KungfuBot2: Learning versatile motion skills for humanoid whole-body control,

Reference 8

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source=pdf_text observed=2026-08-01T10:48:59.173637Z digest=sha256:8f0f630579471b29b991dd8176c30cda36d968aefeba4ec0a353a152f6391edf

Observation e1abb882-efa4-47e8-9f1a-f28aa9bb75a5 · outbound

This paper cites BFM-Zero: A promptable behavioral foundation model for humanoid control using unsupervised reinforcement learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control BFM-Zero: A promptable behavioral foundation model for humanoid control using unsupervised reinforcement learning,

Reference 9

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source=pdf_text observed=2026-08-01T10:48:59.258190Z digest=sha256:5c05b98eae1d9ab566922b0127a5962b4da89bf6aa45216a1136e05283e08e01

Observation 1f33c2cb-ff1f-41ae-9c0b-932d1b9b4ef7 · outbound

This paper cites CLONE: Closed-loop whole-body humanoid teleoperation for long- horizon tasks,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control CLONE: Closed-loop whole-body humanoid teleoperation for long- horizon tasks,

Reference 10

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source=pdf_text observed=2026-08-01T10:48:59.361510Z digest=sha256:22a1e0d3b479518ea1c51e14bc39a53f5fbdfa1414f86db6af36ae055a84e21c

Observation f07d3766-bee8-4582-b974-ace04b615e98 · outbound

This paper cites Agility meets stability: Versatile humanoid control with heterogeneous data,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Agility meets stability: Versatile humanoid control with heterogeneous data,

Reference 11

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source=pdf_text observed=2026-08-01T10:48:59.430296Z digest=sha256:e15248945e5d24abbea6be475d252d557d40fa628d7877710825ceb9211fc034

Observation 4fb85a25-bb13-4a34-a48a-c46d149db540 · outbound

This paper cites VMP: Ver- satile motion priors for robustly tracking motion on physical characters,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control VMP: Ver- satile motion priors for robustly tracking motion on physical characters,

Reference 12

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source=pdf_text observed=2026-08-01T10:48:59.525251Z digest=sha256:64d58c627c240b592a58f01ef93d2ab679ef69cbb57ffd976d87c5323f28aebe

Observation 7708e664-ddef-41d0-8ff5-79e2564202d4 · outbound

This paper cites TWIST2: Scalable, portable, and holistic humanoid data collection system,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control TWIST2: Scalable, portable, and holistic humanoid data collection system,

Reference 13

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source=pdf_text observed=2026-08-01T10:48:59.594048Z digest=sha256:c1ac6a68b01373e616457642f85b59603e66fc9fc8e29035f2f25eedbcf4bda1

Observation 2f4ca8d2-794c-44b3-92b3-0a38ad7fe6ff · outbound

This paper cites HOMIE: Humanoid loco-manipulation with isomorphic exoskeleton cockpit,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control HOMIE: Humanoid loco-manipulation with isomorphic exoskeleton cockpit,

Reference 14

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source=pdf_text observed=2026-08-01T10:48:59.673312Z digest=sha256:279332131f06a6bd45036ce33d01a71a639de43126e3039bf5c0836cb025f2d7

Observation 1bfa5cd6-9513-448a-bea9-dc2d05df9a29 · outbound

This paper cites Expressive whole-body control for humanoid robots,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Expressive whole-body control for humanoid robots,

Reference 15

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source=pdf_text observed=2026-08-01T10:48:59.761882Z digest=sha256:d293ea5f494ea1f654f4024715e641e717d6ef4568c3344377b644164dae58d7

Observation f0ff9709-19de-45c0-8260-ecfa245cd93a · outbound

This paper cites BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion

Reference 16

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source=pdf_text observed=2026-08-01T10:48:59.843045Z digest=sha256:b5200aa8e1b1ced564cc35af9eb2bf7ca70ddbe3010ef69b0a2671a022734dab

Observation 2a8c1690-7c40-4498-b85c-3c33ebd50675 · outbound

This paper cites KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills

Reference 17

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source=pdf_text observed=2026-08-01T10:48:59.940665Z digest=sha256:f8f79a1909e13bc03ca048fb31b86146e0726836de757cdb29c55424cf7f9d60

Observation ab13efcd-2d62-4789-88c3-c2cc079e197f · outbound

This paper cites ZEST: Zero- shot embodied skill transfer for athletic robot control,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control ZEST: Zero- shot embodied skill transfer for athletic robot control,

Reference 18

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source=pdf_text observed=2026-08-01T10:48:59.989939Z digest=sha256:e480e71d916917d64689a495953e5c8ebaf7a788673ee4af22f92445af882479

Observation 54e668cf-ae4e-4a53-a00e-ae0de6e8ebd9 · outbound

This paper cites Track any motions under any disturbances,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Track any motions under any disturbances,

Reference 19

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source=pdf_text observed=2026-08-01T10:49:00.082129Z digest=sha256:799ff58cd409c86646b01a1db47916f0ca378976e34c1870831b63a03b468b76

Observation 665617cd-1fb4-4935-a7dc-575f132c7894 · outbound

This paper cites Finite scalar quantization: VQ-V AE made simple,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Finite scalar quantization: VQ-V AE made simple,

Reference 20

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source=pdf_text observed=2026-08-01T10:49:00.143359Z digest=sha256:668cd161de0ebae0a63e940709fe3bfeec351dcbb9b1672c37f791de3d1093d8

Observation cbcae288-c723-4598-8211-e46690eaa9c7 · outbound

This paper cites Attention-based map encoding for learning generalized legged locomo- tion,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Attention-based map encoding for learning generalized legged locomo- tion,

Reference 21

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Observation e0e83cf6-b8e0-4787-a9f0-55eec97589d0 · outbound

This paper cites Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 22

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source=pdf_text observed=2026-08-01T10:49:00.294878Z digest=sha256:9e8def59372348493981bf4562d57d31d95b9e8a03a698f537f4fa9840eaa10d

Observation 55006973-4917-46fe-9f4f-9f43fa3b5bae · outbound

This paper cites VPIES: Varia- tional privileged information encoder as scaffold for legged locomotion learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control VPIES: Varia- tional privileged information encoder as scaffold for legged locomotion learning,

Reference 23

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source=pdf_text observed=2026-08-01T10:49:00.389371Z digest=sha256:88ef5ae656d17cd3b5e78d64bd54f61ff8f135cb2f56449fa484b05d9f64f5a7

Observation b46d4852-f1ad-445b-9a6a-c895f961a78b · outbound

This paper cites TerAdapt: Proprioceptive terrain-adaptive locomotion via codebook aligned representation learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control TerAdapt: Proprioceptive terrain-adaptive locomotion via codebook aligned representation learning,

Reference 24

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source=pdf_text observed=2026-08-01T10:49:00.471106Z digest=sha256:63ba0492a63eea7666832d299fb1e8f1433d8103fef23c914b3a11f809437c2d

Observation db30a53f-d2d4-4237-8343-a22712339af3 · outbound

This paper cites MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains

Reference 25

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source=pdf_text observed=2026-08-01T10:49:00.565067Z digest=sha256:f4693ded76fd1246375e8f19d9ab654f603ce45b53b58d4f0fda8f7442c7d832

Observation 9e7fa9e9-6611-4d2d-ba6c-50131566eefb · outbound

This paper cites BeamDojo: Learning agile humanoid locomotion on sparse footholds,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control BeamDojo: Learning agile humanoid locomotion on sparse footholds,

Reference 26

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source=pdf_text observed=2026-08-01T10:49:00.663690Z digest=sha256:d81a1fae48dfb4943f8df31631312bf0821c44f49c41a481f23acf12621f8783

Observation e2dc9273-fb39-4090-a4b1-657d6942466a · outbound

This paper cites APEX: Learning adaptive high-platform traversal for humanoid robots,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control APEX: Learning adaptive high-platform traversal for humanoid robots,

Reference 27

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source=pdf_text observed=2026-08-01T10:49:00.738803Z digest=sha256:df737e5142a1493f014fc4368c7cb1498d1238cf601d3d1a487da733ac813345

Observation 9e4bfb2b-e64b-4661-b754-e19409a55cd8 · outbound

This paper cites Learning humanoid standing-up control across diverse postures,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Learning humanoid standing-up control across diverse postures,

Reference 28

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source=pdf_text observed=2026-08-01T10:49:00.815497Z digest=sha256:a79e9ab8f6d24e89bb209a964d70a5d745ad1d9575bba97c26a5de6ec7d7ca2a

Observation 56894f8b-6487-4660-b904-ff9071d7c88b · outbound

This paper cites Learning getting-up policies for real-world humanoid robots,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Learning getting-up policies for real-world humanoid robots,

Reference 29

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source=pdf_text observed=2026-08-01T10:49:00.912699Z digest=sha256:c97b60f169d85ffbe290f7b9babb6b43909109e6520cba4f9656b1d0a9b3da12

Observation e5090ad8-4720-453d-9599-20c28153f40b · outbound

This paper cites OmniXtreme: Breaking the generality barrier in high- dynamic humanoid control,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control OmniXtreme: Breaking the generality barrier in high- dynamic humanoid control,

Reference 30

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source=pdf_text observed=2026-08-01T10:49:00.981339Z digest=sha256:2de40bec4dbf5396dc53bbd3b1e9aa424b8c2b8fb79c1ce5aced1a5e4fafd327

Observation 5d005fed-7d4e-42c8-b263-1c0afe45858e · outbound

This paper cites HumanPlus: Humanoid shadowing and imitation from humans,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control HumanPlus: Humanoid shadowing and imitation from humans,

Reference 31

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source=pdf_text observed=2026-08-01T10:49:01.090884Z digest=sha256:8c5466ea004f31255ee996291775901f9c364dcf9382d460983e54bc38509760

Observation a6dd4c14-2f1b-4fee-a80c-5fe832077a45 · outbound

This paper cites iCub3 avatar system: Enabling remote fully immersive embodiment of humanoid robots,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control iCub3 avatar system: Enabling remote fully immersive embodiment of humanoid robots,

Reference 32

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source=pdf_text observed=2026-08-01T10:49:01.150537Z digest=sha256:b5eea16f1bbb5cab3ab5e41f2e64d33b7130da2f9e2a1a75a911f0bb19d166db

Observation 02ba3055-915e-4441-a328-0ed63fc71133 · outbound

This paper cites EGM: Efficiently learning general motion tracking policy for high dynamic humanoid whole-body control,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control EGM: Efficiently learning general motion tracking policy for high dynamic humanoid whole-body control,

Reference 33

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source=pdf_text observed=2026-08-01T10:49:01.222141Z digest=sha256:78e39ed3082d04427e0a32aec7f408a234bceb393b0e3367ac28cca8d2f4429a

Observation 8a22751b-bc14-4c0a-b667-c0b0fbbe8596 · outbound

This paper cites Visual Imitation Enables Contextual Humanoid Control.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Visual Imitation Enables Contextual Humanoid Control

Reference 34

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source=pdf_text observed=2026-08-01T10:49:01.309464Z digest=sha256:7aa479cff0d466c8677d139d8bd409a22af9499e82dc60651f04740daaaab353

Observation 9b956693-73f2-49be-b78a-844742975344 · outbound

This paper cites AMASS: Archive of motion capture as surface shapes,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control AMASS: Archive of motion capture as surface shapes,

Reference 35

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source=pdf_text observed=2026-08-01T10:49:01.377179Z digest=sha256:d502cc8b59469a53951eb4fe0978520f3a83e2757d03c518fba0410a470a4a17

Observation 2dc8dec8-ec40-45d9-bba7-44cfe922a3bf · outbound

This paper cites Robust motion in-betweening,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Robust motion in-betweening,

Reference 36

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Observation d9be3c32-a426-4a95-a1bd-c5ff0929fcae · outbound

This paper cites Retargeting matters: General motion retargeting for humanoid motion tracking,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Retargeting matters: General motion retargeting for humanoid motion tracking,

Reference 37

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Observation d47f067e-a590-4671-9484-052bcbc2675a · outbound

This paper cites OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction

Reference 38

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source=pdf_text observed=2026-08-01T10:49:01.599354Z digest=sha256:3127994f0cc52e2411dadee9bafdf5692fbb34706bbbf17396cd83e4abf55fdb

Observation 1ede3598-d790-44f5-984f-035d96d5baeb · outbound

This paper cites Experience replay for continual learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Experience replay for continual learning,

Reference 39

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source=pdf_text observed=2026-08-01T10:49:01.678728Z digest=sha256:6c48580c98fcf209d54ce3dc6cbc3cbf316d5b436ff5ff0a8574f9b27758bfbb

Observation 9a6b5faa-e6c3-4536-8012-52ebc44a2637 · outbound

This paper cites Continual learning with global alignment,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Continual learning with global alignment,

Reference 40

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source=pdf_text observed=2026-08-01T10:49:01.748275Z digest=sha256:c96d4c2c4d1add35e4c08ce3c2076d46e77ff99c1300002512c443a3fa698a14

Observation 0d0e08ce-0433-480d-a3f9-8010a15b4df1 · outbound

This paper cites SAFE: Slow and fast parameter-efficient tuning for continual learning with pre- trained models,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control SAFE: Slow and fast parameter-efficient tuning for continual learning with pre- trained models,

Reference 41

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source=pdf_text observed=2026-08-01T10:49:01.811087Z digest=sha256:36c81b62ba7f11a39663b662b274b53bb5e2657546eb64adf49b5c34c16a425e

Observation 4bb7805f-bb6a-4d9a-b529-596169284db4 · outbound

This paper cites Learning to continually learn with the bayesian principle,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Learning to continually learn with the bayesian principle,

Reference 42

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source=pdf_text observed=2026-08-01T10:49:01.889870Z digest=sha256:7c38a982dedf768d7d58e467311af1a3f19cecc8b8c567c7fe63bc9af1cee50c

Observation bfe266ef-123c-4147-91c1-35db8dbae040 · outbound

This paper cites CPPO: Continual learning for reinforcement learning with human feedback,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control CPPO: Continual learning for reinforcement learning with human feedback,

Reference 43

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source=pdf_text observed=2026-08-01T10:49:02.002412Z digest=sha256:397e3b8a7fab70ae0ab5ff7d31f85d18d0820f29ba3e7cb9c52aa0a080219b79

Observation 82daa81b-5998-4820-825c-2860b4375ec8 · outbound

This paper cites A study of plasticity loss in on-policy deep reinforcement learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control A study of plasticity loss in on-policy deep reinforcement learning,

Reference 44

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source=pdf_text observed=2026-08-01T10:49:02.077534Z digest=sha256:7de244630caa0164e7ff72af3b9ff082a0623543b948f0b0cd4cb2ddd18cb282

Observation 565f36e2-792b-413e-bd03-db376da608d8 · outbound

This paper cites Mitigating plasticity loss in continual reinforcement learning by re- ducing churn,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Mitigating plasticity loss in continual reinforcement learning by re- ducing churn,

Reference 45

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source=pdf_text observed=2026-08-01T10:49:02.292905Z digest=sha256:6409ff6aa705e7469b88e0c4b140019e114aecd8182b85bc20e1987f57a0dbb2

Observation 77e4c16d-fce0-4434-aa8c-1f4c3e0eec5c · outbound

This paper cites Self-composing policies for scalable continual reinforcement learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Self-composing policies for scalable continual reinforcement learning,

Reference 46

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source=pdf_text observed=2026-08-01T10:49:02.456822Z digest=sha256:fffef403800241412534ccca6563df74c050f8c14655bf235131d4f1c40eedbf

Observation 2c79ed37-3986-463d-85de-eb4a35aebca6 · outbound

This paper cites Continual reinforcement learning by planning with online world models,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Continual reinforcement learning by planning with online world models,

Reference 47

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source=pdf_text observed=2026-08-01T10:49:02.596556Z digest=sha256:ecf0e57b33fe46b01fff58a3cec9ac44b2533d6bb166527db51b3711385f247b

Observation f622b2de-c6fa-4ef3-95c2-abc3d7b20c7f · outbound

This paper cites Knowledge retention in continual model-based reinforcement learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Knowledge retention in continual model-based reinforcement learning,

Reference 48

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source=pdf_text observed=2026-08-01T10:49:02.737316Z digest=sha256:e662e2dfe4fc506bb0510af33724eb9fa8f190ea6f1692809b12ab35525c9481

Observation 978d3dd1-fb01-4393-81dd-c3f969ded176 · outbound

This paper cites Preserving and combining knowledge in robotic lifelong reinforcement learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Preserving and combining knowledge in robotic lifelong reinforcement learning,

Reference 49

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source=pdf_text observed=2026-08-01T10:49:02.868946Z digest=sha256:57d451c9d0d7f757eaa2e10362559b5f511e294c1cac320122d445e3438e2dc2

Observation c8f04e28-6fdb-4def-86b6-bf6e685dc715 · outbound

This paper cites AtomicVLA: Unlocking the poten- tial of atomic skill learning in robots,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control AtomicVLA: Unlocking the poten- tial of atomic skill learning in robots,

Reference 50

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source=pdf_text observed=2026-08-01T10:49:02.983198Z digest=sha256:b4eafc21af199ee43ae21a7b49c6623c97b120b7061c9124d67bc7bc5dfd75da

Observation 97413159-928f-4df8-b446-2c325308f6ea · outbound

This paper cites Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning

Reference 51

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source=pdf_text observed=2026-08-01T10:49:03.115497Z digest=sha256:93ef02de0baf71975994103e6ca7f3ddce2b2a758a6ee06aa141801778229a8f

Observation 83bd7803-2bc8-4706-86bd-2b12926d4a77 · outbound

This paper cites Pretrained vision-language- action models are surprisingly resistant to forgetting in continual learn- ing,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Pretrained vision-language- action models are surprisingly resistant to forgetting in continual learn- ing,

Reference 52

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source=pdf_text observed=2026-08-01T10:49:03.255157Z digest=sha256:73debc53310dcadc634204b9b307b00c17f67dbe9966ee4771f444ff31401609

Observation cb68113d-99bb-43d6-93d2-34e2cd7a9cb3 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Proximal Policy Optimization Algorithms

Reference 53

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source=pdf_text observed=2026-08-01T10:49:03.382599Z digest=sha256:4fad6d656ee85d30268747e0de97553df0955bfd4460333ee3373fb2f8e731d2

Observation 2d73e5ef-c494-4cd6-b01e-8807e3c5286e · outbound

This paper cites Isaac Gym: High performance GPU-based physics simulation for robot learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Isaac Gym: High performance GPU-based physics simulation for robot learning,

Reference 54

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source=pdf_text observed=2026-08-01T10:49:03.472124Z digest=sha256:4fa75645f34648a8b606ee46d608b2cf4fdeaf8f08d65ec204e6356faff08381

Observation dac72953-9d5f-48b4-be84-7955b48bdec4 · outbound

This paper cites Layer Normalization.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Layer Normalization

Reference 55

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source=pdf_text observed=2026-08-01T10:49:03.628031Z digest=sha256:49cfdf3c4906786fc8ab569051698f708d8eeaefdca6b5b7ec58534560a07b5e

Observation 6655f7c8-b1e7-4106-99c7-516ae6f71972 · outbound

This paper cites ALARM: Safe reinforcement learning with reliable mimicry for robust legged locomotion,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control ALARM: Safe reinforcement learning with reliable mimicry for robust legged locomotion,

Reference 56

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source=pdf_text observed=2026-08-01T10:49:03.732448Z digest=sha256:37c3bf25d5fad0f24dfb1fbfb67f5bb53b0dbc6dab675d3170eb9d4c05ea9e02

Observation 48fc500c-8ae9-4c81-b4f4-673166fdd008 · outbound

This paper cites Learning to walk in minutes using massively parallel deep reinforcement learning,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Learning to walk in minutes using massively parallel deep reinforcement learning,

Reference 57

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source=pdf_text observed=2026-08-01T10:49:03.883451Z digest=sha256:fd6028c0e006a736e3be5e7c0d68777b82548f8e3dcebf2a6b7d381a1528ba28

Observation 675af842-c93e-4c2e-985c-ee139d03cc4d · outbound

This paper cites Xsens MVN Animate,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control Xsens MVN Animate,

Reference 58

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source=pdf_text observed=2026-08-01T10:49:04.037249Z digest=sha256:020bf5ff4e09cae13cbac56ff70ee0b2cdba72f20d20e1c75bc7e46a126472b2

Observation 14bef8a4-9ef9-46c4-acbe-0c2fd3a0515a · outbound

This paper cites High- dimensional continuous control using generalized advantage estimation,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control High- dimensional continuous control using generalized advantage estimation,

Reference 59

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source=pdf_text observed=2026-08-01T10:49:04.203199Z digest=sha256:c9d5c363bca55e28b89a4a7f9ccd636342abe25450d7216be9f7d7ec652c5809

Observation 757646ef-dcdb-47a2-b9f2-9669ec7148ad · outbound

This paper cites ExBody2: Advanced expressive humanoid whole-body control,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control ExBody2: Advanced expressive humanoid whole-body control,

Reference 60

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source=pdf_text observed=2026-08-01T10:49:04.356650Z digest=sha256:49a0f55220c8184e9a4d28cd609f1cb4792106f45c5a9c9fff90ddb36e8ba3f7

Observation 70a4eb90-3e45-4e96-b8c2-48fc9b786be6 · outbound

This paper cites MuJoCo: A physics engine for model-based control,.

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control MuJoCo: A physics engine for model-based control,

Reference 61

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source=pdf_text observed=2026-08-01T10:49:04.512869Z digest=sha256:fb3b96085b7b1b714e37a5386b2bd9b42368dc9f61735df9f2cf56504d623eb5

Pith citing papers

No inbound Pith citation observations are available.