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Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2607.14439.
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66 of 66 outbound references displayed
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Observation 2717502b-b872-495a-bf8a-564eaf239691 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Learning transferable visual models from natural language supervision,
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies Language models are few-shot learners,
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies Visual instruction tuning,
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Observation 259f5ae7-0e49-42f5-960a-2ac63308eea1 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Rt-2: Vision-language-action models transfer web knowledge to robotic control,
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Observation 76fe82a9-2d60-4ffd-b2ba-8b659bc65e7e · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Openvla: An open-source vision-language-action model,
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies π0: A vision-language-action flow model for general robot control,
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies Shortcut learning in generalist robot policies: The role of dataset diversity and fragmentation,
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies Robot Learning as an Empirical Science: Best Practices for Policy Evaluation
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Observation a2da5308-31b8-4006-864b-c5ed6aa02757 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
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Observation 412d22f2-ebdb-418c-bc42-0f954c0d48b0 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Mujoco: A physics engine for model-based control,
Reference 11
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Observation 00ee71b2-11d8-41e4-a533-f346f6451421 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies A survey on vision–language–action models for embodied ai,
Reference 12
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Observation 446cd3ef-44e9-416b-96fd-ac47ac13c3d8 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Bc-z: Zero-shot task generalization with robotic imitation learning,
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Observation 91d41f49-feb2-4356-b3b6-15eed9e22f57 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies A Generalist Agent
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Observation 8d52feeb-33ea-4cba-ba5d-766d19a0f1d5 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies RT-1: Robotics Transformer for Real-World Control at Scale
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Observation ac1655d4-19fc-441c-aae0-786ce0ae8917 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Open x-embodiment: Robotic learning datasets and rt-x models: Open x- embodiment collaboration 0,
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Observation eb8ac80d-2714-44d7-8811-922e1441aa68 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
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Observation 3ee9749e-5966-4d74-9688-07f2d2104745 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies A taxonomy for evaluating generalist robot manipulation policies,
Reference 18
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Observation f62f0d8e-e3b5-42c3-84ec-84f098f9161e · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Investigating the role of instruction variety and task difficulty in robotic manipulation tasks,
Reference 19
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Observation 9a51e0d1-cf7c-4109-9122-94162d215b7d · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Libero: Benchmarking knowledge transfer for lifelong robot learn- ing,
Reference 20
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Observation 74b00c6b-1faf-4d91-8f6d-885bdeb4e64c · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,
Reference 21
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Observation 04fa341f-f101-44e9-93a1-abe267567079 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
Reference 22
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Observation 4f2dda6c-2c73-43af-b052-21f854a58096 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Decomposing the generalization gap in imitation learning for visual robotic manipulation,
Reference 23
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Observation 06b2cd9a-2943-4df9-af5f-0ba6e0d5c7cd · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Vlabench: A large-scale benchmark for language- conditioned robotics manipulation with long-horizon reasoning tasks,
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies Design and use paradigms for gazebo, an open-source multi-robot simulator,
Reference 25
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Observation 28e6d279-942e-49f7-a272-aa3fa200d1e6 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Evaluating real-world robot manipulation policies in simulation,
Reference 26
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Observation 467348d3-6b37-4c34-a64e-dc831043eab2 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
Reference 27
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies Real-to-sim robot policy evaluation with gaussian splatting simulation of soft-body interactions,
Reference 28
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Observation 7b5d5d60-e873-4ad7-9f87-a0279dafc0c3 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Realm: A real-to-sim validated benchmark for generalization in robotic ma- nipulation,
Reference 29
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Observation 20674451-f08e-47fb-8ee4-c9994c9d5877 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Polaris: Scalable real-to-sim evaluations for generalist robot policies,
Reference 30
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Observation a2cc74d6-68dc-4d30-80fa-587bdbd036ba · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
Reference 31
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Observation 90cf0fc5-aeb9-4b96-ab38-b0730084e74d · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware
Reference 32
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Observation 32e65eb9-9bf8-4753-b501-ed084d839e62 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Robotarena ∞: Scalable robot benchmarking via real-to-sim translation,
Reference 33
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Observation 0406c47a-9b9d-44e6-b110-0e28668d6de4 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Contrast sets for evaluating language-guided robot policies,
Reference 34
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Observation 9314c1a5-6324-47f9-933f-081da43ba212 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Reliable and scalable robot policy evaluation with imperfect simulators,
Reference 35
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Observation e1a14480-4852-460c-b5ae-176faa007a0e · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping
Reference 36
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Observation 2baaccd3-b951-4fb0-8fe4-1ba33baea9b3 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies 2205–2219
Reference 37
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Observation 01960db0-43c2-4bc0-b564-92c958763188 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Gaussian pro- cess regression: Active data selection and test point rejection,
Reference 38
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Observation 61feb7af-757a-497d-91d8-79358f615c2c · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Active risk estimation,
Reference 39
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Observation 182a8da6-482d-4d49-b549-f75598eeb811 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Bayesian experimental design: A review,
Reference 40
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Observation 89a25a70-70a3-4fb6-8f83-1bf2a05c4687 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Active surrogate estimators: An active learning approach to label-efficient model evaluation,
Reference 41
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Observation 6dfc427a-1499-40f2-b3e5-5313e51c4b25 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Active testing: An unbiased evaluation method for distantly supervised relation extraction,
Reference 42
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Observation 4b4276f6-357e-4d6d-af58-e28d9bda4dad · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Efficient evaluation of multi-task robot policies with active experiment selection,
Reference 43
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Observation ca2c65b4-f6c3-4a0b-975e-81aab916e725 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Active testing: Sample-efficient model evaluation,
Reference 44
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Observation 5f32f806-9ce4-4690-8dc4-fab518ef3b9e · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Dropout as a bayesian approximation: Representing model uncertainty in deep learning,
Reference 45
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Observation 410612e9-70da-4277-8715-bfc2ff382428 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Gaussian process regression: active data selection and test point rejection,
Reference 46
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Observation 340856fd-cc50-4a0f-a1c5-c66842f8a93a · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Bayesian Active Learning for Classification and Preference Learning
Reference 47
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Observation f8d779e2-87fe-42b5-a5ad-84d7fd1cb7ff · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Unresolved cited work
Reference 48
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Observation 09fbc945-354d-400b-b6e8-f3ca83e24b81 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies What matters in learning from large-scale datasets for robot manipulation,
Reference 49
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Observation ab6cd159-211c-47c1-8017-29e20ffe913b · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Efficient data collection for robotic manipulation via compositional generalization,
Reference 50
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Observation f7b6633b-4884-4916-aea6-827baad2e194 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies CUPID: Curating data your robot loves with influence functions,
Reference 51
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Observation b21ab75e-7c5c-4f6f-9783-f72b65777127 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Prediction-oriented bayesian active learning,
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Observation f058e837-5671-4044-a2c7-6f0147f4f44b · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Curating Demonstrations using Online Experience
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Observation fa2e06c0-e935-49f5-9ed9-a8a6c3a8e808 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Is diversity all you need for scalable robotic manipulation?
Reference 54
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Observation 0c2a9575-c9bd-4463-bd11-9b331e228d15 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Guiding Data Collection via Factored Scaling Curves
Reference 55
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Observation 23199e93-2382-4d8c-9aef-b8e736ba8646 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Robot Data Curation with Mutual Information Estimators
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Observation d2643c9a-6c43-4856-921b-a971175f3e4f · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Actively testing your model while it learns: Realizing label-efficient learning in practice,
Reference 57
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies DataMIL: Selecting data for robot imitation learning with datamodels,
Reference 62
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Observation 88174170-3a79-4f11-b48d-7144fc395fc7 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Here, we show ablations of either only acquisition functions or only surrogate models to highlight the best combination for sample-efficient evaluation
Reference 63
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Observation c892e487-dd7a-4eb9-ac59-d3fad7fb6090 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Figure 8 shows the ablation of several model design choices: 1) no noise floor,
Reference 64
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Observation bf867754-8223-4f10-bcf0-0c55a05ef0c5 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Using a noise floor of 10−1 converges the fastest
Reference 65
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Observation 74771b66-3c02-4c32-b166-4f701f8eace1 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies However, XWED is inappropriate for our problem for two reasons: –The output of the generalist model (actions) differs from the output of the surrogate model (outcomes)
Reference 66
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Observation d5b2ba6a-1538-414e-923b-930198241967 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies 3705–3728
Reference 270
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Active Real-World Factor-Based Evaluation for Generalist Robot Policies Available: https://api.semanticscholar.org/CorpusID: 16144243
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Observation 2ec2dc9b-aecc-4e25-9b42-8519e0b9d16d · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies Efficient Data Collection for Robotic Manipulation via Compositional Generalization
Reference 2024
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Observation 2f8dc7e8-215a-400d-af15-4327f62a8ec2 · outbound
Active Real-World Factor-Based Evaluation for Generalist Robot Policies $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
Reference 2026
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