Paper Citation Record · LEDGER

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

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 25 inbound Pith citation observations for arXiv:2505.11164.

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

pith.paper-citation-record.v1
2505.11164 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:59:59.291743Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:24:01.081333Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:50:11.338513Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy7
unresolved33
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External citation measurements

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Outbound references

Observation e90e4b98-5c71-4ae3-bc6a-cb7d07b4c6f3 · outbound

This paper cites In: 6th Annual Conference on Robot Learning.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: 6th Annual Conference on Robot Learning

Reference 1

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Observation 0a71758a-037b-4961-8cc9-62a98a555f88 · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Flamingo: a Visual Language Model for Few-Shot Learning

Reference 2

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Observation d5b35bf6-7611-4593-aa39-4ce1d6cd5ff3 · outbound

This paper cites Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors

Reference 3

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Observation e3dbf175-dd32-4d34-ab09-8f731ec8bafe · outbound

This paper cites doi:10.15607/RSS.2023.XIX.025.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning doi:10.15607/RSS.2023.XIX.025

Reference 4

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Observation 44f0481b-82d1-4d68-984e-e49b95fb5943 · outbound

This paper cites Language Models are Few-Shot Learners.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Language Models are Few-Shot Learners

Reference 5

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Observation f3390d06-7c4f-4573-8ed3-29f31b76441e · outbound

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Unresolved cited work

Reference 6

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Observation 0b0d8253-8e07-4a98-993c-b3ef26af1f8d · outbound

This paper cites In: 2024 IEEE International Conference on Robotics and Automation (ICRA).

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: 2024 IEEE International Conference on Robotics and Automation (ICRA)

Reference 7

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Observation 4f1f6d7f-d570-411f-b49e-960eca106d7e · outbound

This paper cites Science Robotics 8(74): eade2256.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Science Robotics 8(74): eade2256

Reference 8

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Observation 992ee3af-dc61-4170-a095-46c74ef1db3a · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 9

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Observation 1de6b9bb-d055-4315-ac97-53de759dd341 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning PaLM-E: An Embodied Multimodal Language Model

Reference 10

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Observation aababf23-f719-4feb-91b5-d445e81157b0 · outbound

This paper cites In: Mobile Service Robotics.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: Mobile Service Robotics

Reference 11

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Observation 31464aaf-2f0b-4ea5-97ed-6e1ffa6a6d7f · outbound

This paper cites IEEE Transactions on Robotics 38(5): 2908–2927.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning IEEE Transactions on Robotics 38(5): 2908–2927

Reference 12

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Unresolved cited work

Reference 13

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This paper cites Perceptive Locomotion through Nonlinear Model Predictive Control.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Perceptive Locomotion through Nonlinear Model Predictive Control

Reference 14

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Unresolved cited work

Reference 15

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Observation 7c8c4f70-fa42-496b-867c-e42a81e93107 · outbound

This paper cites Nature Machine Intelligence 6(787–798).

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Nature Machine Intelligence 6(787–798)

Reference 16

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Observation 23991f07-0383-45fb-98db-f500b29b4829 · outbound

This paper cites Neural Computation 9(8): 1735--1780.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Neural Computation 9(8): 1735--1780

Reference 17

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Unresolved cited work

Reference 18

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Observation 276a6fd9-7e46-4042-b31d-723a14e3534e · outbound

This paper cites IEEE Transactions on Robotics 38(6): 3395--3413.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning IEEE Transactions on Robotics 38(6): 3395--3413

Reference 19

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Observation 9da25300-8fba-4f61-a4b8-af18ea00613a · outbound

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Science Robotics 9(86)

Reference 20

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Observation bbfd2d7e-8172-45d3-9de8-a4bbd43855f7 · outbound

This paper cites IEEE Robotics and Automation Letters 8(10): 6619--6626.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning IEEE Robotics and Automation Letters 8(10): 6619--6626

Reference 21

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Observation 9b95e54b-96d1-43d1-9ac3-1e035d5a69c7 · outbound

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: 2020 IEEE International Conference on Robotics and Automation (ICRA)

Reference 22

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Observation f09fc795-6257-4097-b974-2a0ac6b8865d · outbound

This paper cites Science Robotics 5(47).

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Science Robotics 5(47)

Reference 23

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: The Twelfth International Conference on Learning Representations

Reference 24

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Observation b029ee33-d20c-4bca-b2d9-cdb93a4f75f1 · outbound

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Science Robotics 7(62)

Reference 25

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Elevation Mapping for Locomotion and Navigation using GPU

Reference 26

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Symmetry Considerations for Learning Task Symmetric Robot Policies

Reference 27

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Observation afe3d279-f35d-4bfd-8a41-7ebe59272f9e · outbound

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH '02

Reference 28

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Unresolved cited work

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Unresolved cited work

Reference 30

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Unresolved cited work

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This paper cites In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S and Garnett R (eds.) Advances in Neural Information Processing Systems, volume 30.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S and Garnett R (eds.) Advances in Neural Information Processing Systems, volume 30

Reference 32

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Observation c1491cf3-18c7-461b-9d29-644a538c4ae7 · outbound

This paper cites In: LaValle SM, O'Kane JM, Otte M, Sadigh D and Tokekar P (eds.) Algorithmic Foundations of Robotics XV.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: LaValle SM, O'Kane JM, Otte M, Sadigh D and Tokekar P (eds.) Algorithmic Foundations of Robotics XV

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This paper cites In: Faust A, Hsu D and Neumann G (eds.) Proceedings of the 5th Conference on Robot Learning, Proceedings of Machine Learning Research, volume 164.

Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: Faust A, Hsu D and Neumann G (eds.) Proceedings of the 5th Conference on Robot Learning, Proceedings of Machine Learning Research, volume 164

Reference 35

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning Learning Agile Locomotion on Risky Terrains

Reference 36

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

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning In: 7th Annual Conference on Robot Learning

Reference 38

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning , " * write output.state after.block = add.period write newline

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning write newline

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning , " * write output.state after.block = add.period write newline

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Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning write newline

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Pith citing papers

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Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots cites this paper.

Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

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Observation 8831231a-5f3c-4738-aa4d-181af8f07c10 · inbound

HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation cites this paper.

HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

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Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots cites this paper.

Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

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DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction cites this paper.

DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

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Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning cites this paper.

Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 86

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Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching cites this paper.

Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 33

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Observation c0bc330c-107c-4f26-92dd-c8264272a6a5 · inbound

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation cites this paper.

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T20:06:57.642863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40bac118-f639-4527-83f8-ee3365c37184 · inbound

Quadruped Parkour Learning: Sparsely Gated Mixture of Experts with Visual Input cites this paper.

Quadruped Parkour Learning: Sparsely Gated Mixture of Experts with Visual Input Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:01:04.835554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 073be399-a54a-4193-84f2-707a254cd39a · inbound

Evaluation of an Actuated Spine in Agile Quadruped Locomotion cites this paper.

Evaluation of an Actuated Spine in Agile Quadruped Locomotion Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:55:53.087240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba4a9c46-eae0-4771-8e14-c1e697e94233 · inbound

roto 2.0: The Robot Tactile Olympiad cites this paper.

roto 2.0: The Robot Tactile Olympiad Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:33:56.537378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b82c0896-b318-4a2a-91b7-e5a47d825542 · inbound

Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient cites this paper.

Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:03:48.653533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6656f8c3-d883-4c4b-b463-f1b1681dfcff · inbound

SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World cites this paper.

SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:42:47.067007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3b1a3024-d6f0-4fc5-9c3d-5182eabd64ca · inbound

CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation cites this paper.

CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:56:47.408984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1717206-431a-44b7-a2c7-ca7143b694c5 · inbound

LadderMan: Learning Humanoid Perceptive Ladder Climbing cites this paper.

LadderMan: Learning Humanoid Perceptive Ladder Climbing Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:26:58.720310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92e407e8-b3c3-43eb-ab27-756945063146 · inbound

TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion cites this paper.

TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:26:59.100084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f35f84b9-26ef-4246-9578-0f770cbc4af8 · inbound

HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning cites this paper.

HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:38:55.569322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a7264f61-11a6-4701-941f-49c7584d8060 · inbound

SWAP: Symmetric Equivariant World-Model for Agile Robot Parkour cites this paper.

SWAP: Symmetric Equivariant World-Model for Agile Robot Parkour Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:19:34.949012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f6953792-0d08-4b74-b3e7-913cc1d6654f · inbound

StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots cites this paper.

StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:50:11.339967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6217da8-8f69-4f40-a4d9-147f9d21fb97 · inbound

StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots cites this paper.

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T10:12:45.478787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75f6c521-c955-4199-ac94-e701a2213616 · inbound

Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing cites this paper.

Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:55:42.174814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67f65685-3310-4d7b-8310-92fe15bc72c0 · inbound

Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing cites this paper.

Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-03T22:08:58.732234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9fa7bc2d-d012-44f7-b8da-d86be24b4552 · inbound

EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal cites this paper.

EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T05:10:48.554751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control cites this paper.

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

Resolution: unresolved; no resolver link, observed 2026-08-01T10:49:00.294878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbe3a2df-ca82-4311-955f-a1be726f7fa6 · inbound

Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform cites this paper.

Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T18:18:00.209464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75a53b56-9e12-4c5f-ae9a-46a2fb5b9d1b · inbound

Light-Loco-Parkour: Versatile Perceptive Whole-Body Locomotion via Multi-Skill Distillation cites this paper.

Light-Loco-Parkour: Versatile Perceptive Whole-Body Locomotion via Multi-Skill Distillation Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T00:59:55.340577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.