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Paper Citation Record · LEDGER

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies

As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2607.21867.

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

pith.paper-citation-record.v1
2607.21867 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:32:52.655581Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

17 of 17 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dd5571a2-bd30-483a-bca8-35fd6f2cc1cb · outbound

This paper cites Residual Policy Learning.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Residual Policy Learning

Reference 1

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source=pdf_text observed=2026-08-01T06:32:50.887493Z digest=sha256:c6023897aeb4efd823430f63fcee039b595f252f4bb9855b4fccc4985d459ed9

Observation 68e1e66d-4eae-4f6a-b47e-19f2bd22b0c6 · outbound

This paper cites Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

Reference 4

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source=pdf_text observed=2026-08-01T06:32:51.170921Z digest=sha256:f70793fd97f6e2c9070dbc8946ea3e079e5b5d7895610007151d13a1ebe05d71

Observation 1ceecee9-6f43-4165-b013-4e2d32e4e80d · outbound

This paper cites Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Reference 5

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source=pdf_text observed=2026-08-01T06:32:51.329299Z digest=sha256:95d15db44ace149126fdd57b82458295e79d75d192f4c27b056bf1a7957d6a31

Observation 7de8c998-94cb-4d96-98a8-5f39e1582169 · outbound

This paper cites Temporal Difference Learning for Model Predictive Control.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Temporal Difference Learning for Model Predictive Control

Reference 10

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source=pdf_text observed=2026-08-01T06:32:51.940717Z digest=sha256:0eaecd611ededc2f00fee616f542bf45064964841cee51d9f3b1f27397be4599

Observation a2b4da04-c527-460e-af13-c78f14397976 · outbound

This paper cites Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Reference 11

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source=pdf_text observed=2026-08-01T06:32:52.100841Z digest=sha256:892d91ebfd69026d2083eaa99862d18d46e20c41a43b62e60b27020ecc0c0962

Observation 6b56dd92-bc6e-4e47-858a-a29b23e322df · outbound

This paper cites Diversity is All You Need: Learning Skills without a Reward Function.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Diversity is All You Need: Learning Skills without a Reward Function

Reference 14

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source=pdf_text observed=2026-08-01T06:32:52.397160Z digest=sha256:0809da8b2fde945edba54edaf502247238d3b7a5068544ee684357dc52625c5e

Observation 534371e1-d8ba-4b70-9bdd-0db6848e22e7 · outbound

This paper cites Dynamics-Aware Unsupervised Discovery of Skills.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Dynamics-Aware Unsupervised Discovery of Skills

Reference 15

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source=pdf_text observed=2026-08-01T06:32:52.458556Z digest=sha256:2854aef688c50dbb801ee214a14527dfad8b2513b0197c318a740138ce56de98

Observation 00cb38aa-ef1b-4b01-864b-0b246d255b4c · outbound

This paper cites ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters

Reference 16

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source=pdf_text observed=2026-08-01T06:32:52.570650Z digest=sha256:041cfe3ccfe9100b181b1dc41b679dcdaccbaad9b5f63a33af01cca3fd4b42fd

Observation 20c25c21-39ef-4342-9fd5-27e017d99792 · outbound

This paper cites Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Reference 17

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source=pdf_text observed=2026-08-01T06:32:52.655581Z digest=sha256:45035fd32e4cf7f12d844398934905c5248202dc16ddbbb0c634bd05a0cee3e5

Observation a6dff5b1-97df-438d-893e-688193e00db2 · outbound

This paper cites Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Reference 2018

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source=pdf_text observed=2026-08-01T06:32:51.539244Z digest=sha256:5e43083fb78655eec790583510c9cc8c2b4d84b4692fece195838e1934b0cab4

Observation c482f795-6a45-4a5e-a87c-793b915f2edb · outbound

This paper cites When to Trust Your Model: Model-Based Policy Optimization.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies When to Trust Your Model: Model-Based Policy Optimization

Reference 2019

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source=pdf_text observed=2026-08-01T06:32:51.649648Z digest=sha256:3d0bd0be099513134ce77a64533e87fd81fedbda271760f3ac4f2d5ba808019f

Observation 3bbeb77a-ad30-4e99-a635-d965f6f44562 · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Dream to Control: Learning Behaviors by Latent Imagination

Reference 2020

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source=pdf_text observed=2026-08-01T06:32:51.832057Z digest=sha256:2655481324f37c17791325517e89e85f5c63503f484c1081fee7bee735b20f5f

Observation 59b43bd1-af11-4855-85dd-673f503c0edf · outbound

This paper cites RMA: Rapid Motor Adaptation for Legged Robots.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies RMA: Rapid Motor Adaptation for Legged Robots

Reference 2021

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source=pdf_text observed=2026-08-01T06:32:50.938941Z digest=sha256:35cb8a8fba462bbf81a808e1dd0af8a2ef1831a4a6fe9d255ad21574055091eb

Observation 0e242fb6-8977-4625-b4a6-b4f7bc8bb96c · outbound

This paper cites Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior

Reference 2022

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source=pdf_text observed=2026-08-01T06:32:51.068053Z digest=sha256:32855eabe670af1a5a4018978e0e1dd645a45e7a6ba5408a87e3c30461c73c90

Observation cfbb742c-233b-4798-bac4-8f990ee4df73 · outbound

This paper cites Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments

Reference 2023

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source=pdf_text observed=2026-08-01T06:32:51.438757Z digest=sha256:e01cad94a1d0b39797ee268a112fe46db5e5c0a6fae6c29b82a5c65dff4256de

Observation 18528b75-9185-43de-b92c-ed41a0246967 · outbound

This paper cites Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion

Reference 2024

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source=pdf_text observed=2026-08-01T06:32:52.241114Z digest=sha256:53cf4c74230b95e0e5b0ea35ce94eb29783851a07ee185d054bddd34171630a6

Observation 5602058d-dd11-402a-bb59-70839d0d8ad8 · outbound

This paper cites A Statistical Test for the Benefits of Personalizing Interventions.

When Is a Learned Command Adapter Worth It? Closed-Loop Identification and Counterfactual Auditing of Frozen Locomotion Policies A Statistical Test for the Benefits of Personalizing Interventions

Reference 2026

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source=pdf_text observed=2026-08-01T06:32:52.338091Z digest=sha256:ff76bbcadcd22be76c1c1986a4877208758c5a668f4f603d025be32abde60077

Pith citing papers

No inbound Pith citation observations are available.