Paper Citation Record · LEDGER

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning

As of 19 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2510.10759.

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

pith.paper-citation-record.v1
2510.10759 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:19:08.987872Z

measured 35 of 35 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation d6d19a85-e8e8-4eb2-aaa0-43b4679f0773 · outbound

This paper cites Embodied AI beyond embodied cognition and enactivism.Philosophies, 4(3):39, 2019.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Embodied AI beyond embodied cognition and enactivism.Philosophies, 4(3):39, 2019

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:06.691845Z

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Observation 2bc32b72-2187-4f4a-95e1-d8bf99e4d856 · outbound

This paper cites MIT press, 2006.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning MIT press, 2006

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:06.735577Z

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Observation 7d66c528-ba7a-40b7-ba78-98d70205e8d0 · outbound

This paper cites Reinforcement learning.Journal of Cognitive Neuroscience, 11(1):126– 134, 1999.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Reinforcement learning.Journal of Cognitive Neuroscience, 11(1):126– 134, 1999

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:06.801655Z

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Observation a42578fb-e2ff-4fc9-a92d-87cd21fa0766 · outbound

This paper cites Anymal parkour: Learning agile navigation for quadrupedal robots.Science Robotics, 9(88):eadi7566, 2024.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Anymal parkour: Learning agile navigation for quadrupedal robots.Science Robotics, 9(88):eadi7566, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:06.855710Z

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Observation 9a1e8029-d177-4547-9ee5-72f60910a0d1 · outbound

This paper cites Rapid locomotion via reinforcement learning.The International Journal of Robotics Research, 43(4):572–587, 2024.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Rapid locomotion via reinforcement learning.The International Journal of Robotics Research, 43(4):572–587, 2024

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:06.893356Z

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Observation d3f4db10-b507-470b-9efe-7b09eba6bcd5 · outbound

This paper cites Not only rewards but also constraints: Applications on legged robot locomotion.IEEE Trans- actions on Robotics, 2024.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Not only rewards but also constraints: Applications on legged robot locomotion.IEEE Trans- actions on Robotics, 2024

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:06.965684Z

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Observation abe7357b-34d3-47bf-a7cd-941e2e9286ae · outbound

This paper cites Demon- strating a walk in the park: Learning to walk in 20 min- utes with model-free reinforcement learning.Robotics: Science and Systems (RSS) Demo, 2(3):4, 2023.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Demon- strating a walk in the park: Learning to walk in 20 min- utes with model-free reinforcement learning.Robotics: Science and Systems (RSS) Demo, 2(3):4, 2023

Reference 7

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Observation d5d8e089-8fa3-41d0-b096-a535dbae770b · outbound

This paper cites Explor- ing constrained reinforcement learning algorithms for quadrupedal locomotion.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Explor- ing constrained reinforcement learning algorithms for quadrupedal locomotion

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.074531Z

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Observation 08391986-fcf8-4bfd-afdb-e44f8b90ab12 · outbound

This paper cites A review of safe reinforcement learning: Methods, theories and applications.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning A review of safe reinforcement learning: Methods, theories and applications.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.161324Z

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Observation 6dc6c1e0-5847-4041-a25c-4e00a85c6bf0 · outbound

This paper cites Barrier function-based safe reinforce- ment learning for emergency control of power systems.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Barrier function-based safe reinforce- ment learning for emergency control of power systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.238346Z

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Observation f3c8d702-8a34-46c6-8148-4eee8be26ce0 · outbound

This paper cites Hyperparameters in reinforcement learning and how to tune them.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Hyperparameters in reinforcement learning and how to tune them

Reference 11

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Observation 49dea45d-3c52-42fa-a41b-97348226d650 · outbound

This paper cites Safe policies for reinforce- ment learning via primal-dual methods.IEEE Transac- tions on Automatic Control, 68(3):1321–1336, 2022.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Safe policies for reinforce- ment learning via primal-dual methods.IEEE Transac- tions on Automatic Control, 68(3):1321–1336, 2022

Reference 12

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Observation ce697bad-56c4-4d26-8c2e-37f0b64bfb1f · outbound

This paper cites Constrained policy optimization.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Constrained policy optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.433107Z

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Observation 69ebfb6f-7ab4-4afa-8636-c7e436aaa597 · outbound

This paper cites Trust region-based safe distributional reinforcement learning for multiple constraints.Advances in Neural Information Processing Systems, 36, 2024.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Trust region-based safe distributional reinforcement learning for multiple constraints.Advances in Neural Information Processing Systems, 36, 2024

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.514688Z

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Observation 1232b93f-135e-47e0-ac5f-5032167394ea · outbound

This paper cites Ipo: Interior- point policy optimization under constraints.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Ipo: Interior- point policy optimization under constraints

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.605441Z

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Observation bdcc797f-e60e-42e2-bfd2-a66232e29987 · outbound

This paper cites Crpo: A new approach for safe reinforcement learning with convergence guarantee.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Crpo: A new approach for safe reinforcement learning with convergence guarantee

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.695163Z

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Observation 3a053759-d171-4c10-8a5e-f5eb17a723c8 · outbound

This paper cites Risk-averse model uncertainty for distributionally robust safe rein- forcement learning.Advances in Neural Information Processing Systems, 36, 2024.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Risk-averse model uncertainty for distributionally robust safe rein- forcement learning.Advances in Neural Information Processing Systems, 36, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.738071Z

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Observation dbf013d3-9484-45d5-972c-d17b4d67a52c · outbound

This paper cites Constraints as Rewards: Reinforcement Learning for Robots without Reward Functions.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Constraints as Rewards: Reinforcement Learning for Robots without Reward Functions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.791607Z

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Observation d6102412-02ad-4729-9285-5217b0259029 · outbound

This paper cites Safe and balanced: A framework for constrained multi-objective reinforcement learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Safe and balanced: A framework for constrained multi-objective reinforcement learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.893507Z

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Observation 63585a4e-aaaa-4a6d-a1a1-bf988c8663c0 · outbound

This paper cites Safe Distributed Learning-Enhanced Predictive Control for Multiple Quadrupedal Robots.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Safe Distributed Learning-Enhanced Predictive Control for Multiple Quadrupedal Robots

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:07.963724Z

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Observation 4b911480-d93f-488e-b401-676c696e778b · outbound

This paper cites an unresolved cited work.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Unresolved cited work

Reference 21

Resolution: malformed identifier; no resolver link, observed 2026-08-04T10:19:08.002067Z

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Observation f4ed534f-1b0b-4449-b7ff-f0d82c303157 · outbound

This paper cites Hybrid reward architecture for reinforcement learning.Advances in Neural Information Processing Systems, 30, 2017.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Hybrid reward architecture for reinforcement learning.Advances in Neural Information Processing Systems, 30, 2017

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:08.065725Z

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Observation a8e23f64-ff53-4abf-a0f0-3d5c5b64aedb · outbound

This paper cites The general problem of the stability of motion.International Journal of Control, 55(3):531–534, 1992.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning The general problem of the stability of motion.International Journal of Control, 55(3):531–534, 1992

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:08.131052Z

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Observation a4fd2b0b-eff0-427f-a1df-17bb8c43a054 · outbound

This paper cites Adaptive auxiliary task weighting for reinforce- ment learning.Advances in Neural Information Process- ing Systems, 32, 2019.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Adaptive auxiliary task weighting for reinforce- ment learning.Advances in Neural Information Process- ing Systems, 32, 2019

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:08.193016Z

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Observation ba03d7c6-4de1-4571-9309-a35cd140d429 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Proximal Policy Optimization Algorithms

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:08.279170Z

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Observation a294bcf6-a5b5-46d4-afd4-d708bb09d88a · outbound

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

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Learning to walk in minutes using massively parallel deep reinforcement learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:08.341187Z

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Observation c25884f4-0e1e-46af-a48a-7cd3fab3858a · outbound

This paper cites Growable and interpretable neural control with online continual learning for autonomous lifelong locomotion learning machines.The International Journal of Robotics Research, 2025.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Growable and interpretable neural control with online continual learning for autonomous lifelong locomotion learning machines.The International Journal of Robotics Research, 2025

Reference 27

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Observation 79f9820a-1b51-45a8-acf7-c92bf9124454 · outbound

This paper cites Consider the Lyapunov candidate function (R 1t), which measures the system’s deviation from equilibrium: V(R 1t) = 0.5R2 1t.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Consider the Lyapunov candidate function (R 1t), which measures the system’s deviation from equilibrium: V(R 1t) = 0.5R2 1t

Reference 28

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Observation 42cf640c-9756-457e-8358-f31a41444f19 · outbound

This paper cites an unresolved cited work.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Unresolved cited work

Reference 29

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Observation 9ebc5362-464d-4bf4-90f6-26800ffe6edd · outbound

This paper cites However, as shown in Figure S2, the tuning process is relatively complex.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning However, as shown in Figure S2, the tuning process is relatively complex

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T10:19:08.598743Z

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Observation 1490b82c-7231-4a02-bf49-72cd80e27571 · outbound

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Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Unresolved cited work

Reference 31

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Observation cad59e71-d8c7-4cea-9636-83199aacc6d9 · outbound

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Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Unresolved cited work

Reference 32

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Observation a07c088d-c956-4814-a2d8-8a7429d12094 · outbound

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Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Unresolved cited work

Reference 33

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Observation 2bfc4e79-9dfb-452f-aca0-63c1c4694093 · outbound

This paper cites an unresolved cited work.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning Unresolved cited work

Reference 34

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Observation 385dd084-cec4-4d10-8616-02f44eadb617 · outbound

This paper cites The figure shows that ROGER is less sensitive to the hyperparameter choices than other state-of-the-art methods since none of the tested cases cause the robot to fall.

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning The figure shows that ROGER is less sensitive to the hyperparameter choices than other state-of-the-art methods since none of the tested cases cause the robot to fall

Reference 35

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