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Paper Citation Record · LEDGER

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

As of 7 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 2026-08-07T06:34:17.273281+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.

Source: cited_works

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=sha256:8aa9092c49531dee7412e0d00a08332dd7401ccfbef53f08fad60c6368ecc7ce

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:d249f64b83c050da4792afc93898e8b60eb440059548b32a9750eb2ac7e1cb3d

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=sha256:7e29c4fe35f348c0c97674c8e458f62696369f449c7e1cad900be06851bb8c41

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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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:b0463caeaea0033b0f31a1bb01734d1e8f24a5fc1b755c89837d3bf781df3c81

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=sha256:96f926a68cd0373328f11ae4d18f465ca1af19a7ba5a5685a365538774a2d96b

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=sha256:366a5000dbf10d35278e7d27cf4dd20f59524802e807d162535e20cb73252f16

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:afe1c028d0baa24771d3028dbabb244c86cc562851fe7203208f1471b4cabf71

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:d394e2764d0c66e46903e4b55fc2c782c23feedac0e0099b30b242986f4cdecb

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=sha256:7eff4a491f529f9fb6a89669eb7b2a89eb3dab13ded04ae604352772aa22e371

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=sha256:49f0001abef7487486ec3ad3905c6b5236e85bef7fd07ab89aae890cdf15eed2

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:d4ce2761ad7793cce04290ba8f4455959c3c95b303d5b04be712d159f252e1af

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:fbcdff64615ba48e837631ebaa972f3bcb5809eabd5f3a3647df1a76bdd794a2

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=sha256:1010dd53456d1651c0fb868cafcf4c968e6f6749f89ba4ea37d6f06834ed9ca1

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:a893ab13159e9c2bfb94bf61bcf99aa7aa98354afc4307c1f30d30514a3cd9b0

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=sha256:99d1edd5ccab4dc5b2e151cf5d1ade29f8297b33d71a2df4ed0b6c8abc237d6b

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:eda2216cc31094fe6249a248c9f6952efdf7cc5f640d8c69492c48db026c6250

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:b3cca7384f7c46858bcbb16269e4e550b5dd2360eb516ff5e06671137f9d637d

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=sha256:2f26050bb8700171531395bbb9732c8c011c925746b52a906398bda952dfdd2d

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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source=pdf_text observed=2026-08-01T10:49:00.190457Z digest=sha256:8f1bea817c7763dd6f51fa357d3b9f85182f968b4e9bd632bcc1ad26d9d7af61

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=sha256:4dc91097089f9b2a709fcfd7bc984999bf93ddb0bd68434da75e92b7096c294a

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=sha256:273a4b38b0b118c44aafcdf16f31a6c715e81e3b4b6855a4a3322864eff84b14

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:cec4b89ec398934dbb5bafb5b2515cea12fefb5b69fe3f75ef0ca3b4bdcfbee6

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=sha256:8630ad7c00a9ad2f6a2a49a3e72d904dfe65297a439ca19eca6446b605b77671

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=sha256:3feb856b856899f5c0738154a5fe885d756a72181c7adee052a8d564ecd8acac

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=sha256:657ae0703c9ab9438a35fc3f8e8755095efc40b67ad8ecc2c01ce3693ec43cd3

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:e0addb65fed9720a545b2d642031c6c10eb5d70720268ba560b9ea3e15a84b0c

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=sha256:231e354e8940969bcf2aff3d5a8c979293f94b3c214a7460449f38dbfeca5a28

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:e87c93f8c6be82270848cbb3c3bf6972384ff7429f85169530d3748d7a8db0c1

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:cf47c63c59ea5b4975bae1b4df777fbbf6ffeee71743d026d7c52518ab197da1

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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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:b631fbdb19cd279fccbfbafc972e55f3dcf46729f5244e9d4c22f956fcbcbefb

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=sha256:557be149601219af346444295d29fd0e8adae2a53ab755eadb45761a2555fc08

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:b2989f91a8d0a0f73dd24f7871dfec493d24a2be18d7c7f08624f0a9d3b7fc45

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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source=pdf_text observed=2026-08-01T10:49:01.526188Z digest=sha256:c0c6a5dbec27cb05a0973508ca53b8aeb62b4292c306ece31d67a5ef2c4faa14

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=sha256:8e02156c29eeda2eefb7f30413555beb41ed1d161f4d1327d40c1dc35f763df1

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=sha256:994cc0074f10e42e39709729cd051b487eb2802aacb4907ccdcb531ff718cf27

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=sha256:78f250bd5b2ac1af414c2e9ef993e04a522c2037161de2d26fb97dff031aeca3

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=sha256:7ab1f817ee144f6f5fb0349e6c77c73ee1cad83271ba0108158d56dd912f180b

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=sha256:8ab15d37a4f5fada1690365a86745266d45a8f5110d089d0d0c502102415e919

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=sha256:95db8af275692aac10dc46889eb1554a94506b78e03de1268aa5901ffb0e1e13

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:eed9bdc6a81fff888005f7688c33c5f92facd43d179ad864a08c75a4f7d3bab8

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=sha256:7989f29f6a894f6755285bbb98196ecf4e2f90880a471c4bbd99bf5c7b303807

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:f778fa0b62b33bc2e3d56a5939bb243c964b2aeb8a4f4302c5812ce57ffc2560

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=sha256:94b02ce0a2f34900db5b446e2509349028e10e32d0787c3b91239fb8f11ed1ef

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:a88254550bc3e46374204087e33e5019c7a9a1532844e4f785ba4620e2de2a69

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=sha256:1444b61701ac231a73dd84605ba885aed8c2685eeaa1761f9b9c2be342d64de6

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=sha256:64b89da35f05ce5b260a948551a59311e6c8330cf5cbcea3a1f4e4e3dbc5cdcc

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:b74866343ec6caa715a61583a14289a5c36deaa53a4d03885b3e109708b1ca7d

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=sha256:61cb09e6163aa145cbf1e3b2bd5e9825b42516e3dcaaf3d42536d1b1c300da61

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:baa32d6684b532111e184f3df365da37c15da75f08482d1176073cec11a8157d

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=sha256:9465ba1218ad5980a3512d91cbf4c05752b701f8b868e41cce1e3081ec8394ef

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=sha256:667d14d338b9e0cd136d492ea372e32c2dafb214babc818da29ee87fad203a96

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=sha256:9d04cd73ca1249c36e6fc3521b0250d2098266d1d27ca9fb490821be095abda2

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=sha256:559509d3bfa087371eca577d418d3b1b5468631e0735c515eb508b292c59f3d9

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=sha256:056a54608ed17717104d35adff83230aad42e4f7e2dc9e8ad51e1ff1c20c4380

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=sha256:2b4a90e1ab00bf688ab89d397a242fd5b0dfb75649640ff6124df0ee1ccc08b8

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:da3a04e1f72b903ba592b40c6152b7c5fd2bf34ecc1eacac409a13fbe8bb5c69

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=sha256:3b140824cbb75ad3eb5dea2ada4c2bd897e9605fbaf6b7ecfb6dea8df03c49a2

Pith citing papers

No inbound Pith citation observations are available.