Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration | Paper Citation Record

Observation d5aa307d-e840-4430-a747-3ba80ec2af6e · outbound

This paper cites Modern Bayesian experimental design.Statistical Science, 39(1):100–114.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Modern Bayesian experimental design.Statistical Science, 39(1):100–114

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.467305Z

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Observation 045f3ce2-b5f1-41bf-98ae-e736c5e50de0 · outbound

This paper cites FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information

Reference 2

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Observation c42b94ed-6c51-47b7-87b7-c5f8a00f5687 · outbound

This paper cites GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.460254Z

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Observation bc072596-aaef-430c-ac3a-076179819ee7 · outbound

This paper cites Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.468987Z

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Observation 805465f4-cc75-4462-a5ae-931142c63c71 · outbound

This paper cites ASID: Active Explo- ration for System Identification in Robotic Manipulation.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration ASID: Active Explo- ration for System Identification in Robotic Manipulation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.451091Z

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Observation 9dd28ebc-bb56-4e10-8653-352cbe3713e4 · outbound

This paper cites Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:12:18.052708Z

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Observation 0f4da267-7f00-480e-94f5-f3bd6446707e · outbound

This paper cites Behavior Synthesis via Contact-Aware Fisher Information Max- imization.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Behavior Synthesis via Contact-Aware Fisher Information Max- imization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.452839Z

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Observation 90e1bb20-545b-48b2-a861-09c54ebfdd85 · outbound

This paper cites You’ve Got to Feel It To Believe It: Multi-Modal Bayesian Inference for Semantic and Property Prediction.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration You’ve Got to Feel It To Believe It: Multi-Modal Bayesian Inference for Semantic and Property Prediction

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.458558Z

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Observation fc75d52c-8c04-40ad-b517-bba60602ebe3 · outbound

This paper cites an unresolved cited work.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-13T10:57:39.462063Z

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Observation b22dcfb2-2cdc-4aa8-8d65-4a0c2f285098 · outbound

This paper cites Bayesian Q-learning.Aaai/iaai, 1998:761–768.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Bayesian Q-learning.Aaai/iaai, 1998:761–768

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.449336Z

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Observation 36b3d73f-0005-4097-a3c6-d7ce3b26cae9 · outbound

This paper cites Efficient exploration through bayesian deep q-networks.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Efficient exploration through bayesian deep q-networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.454664Z

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Observation 220bccc7-df05-4e20-8bd6-f0800f4d0c5d · outbound

This paper cites Gen- eralization and Exploration via Randomized Value Func- tions.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Gen- eralization and Exploration via Randomized Value Func- tions

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.465547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a99ba491-10b7-4dfc-ba8b-ab0b86ead114 · outbound

This paper cites Russo, and Zheng Wen.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Russo, and Zheng Wen

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.456625Z

Source-reported events for the cited work

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Observation 929dc835-5351-45af-b72f-ca7c9ce8edfb · outbound

This paper cites Information Di- rected Sampling and Bandits with Heteroscedastic Noise.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Information Di- rected Sampling and Bandits with Heteroscedastic Noise

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.463830Z

Source-reported events for the cited work

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Observation 52197e38-69d3-4588-912b-c65f320e655a · outbound

This paper cites Efficient exploration with Double Uncertain Value Networks.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Efficient exploration with Double Uncertain Value Networks

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T22:22:36.253842Z

Source-reported events for the cited work

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Observation fd1ce58a-eea2-4857-bde0-e5f793f9673b · outbound

This paper cites A distributional perspective on reinforcement learning.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration A distributional perspective on reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.547738Z

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Observation c0a34289-3de4-464b-b355-c9c10ed38d5a · outbound

This paper cites Distributional reinforcement learning for efficient exploration.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Distributional reinforcement learning for efficient exploration

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.566765Z

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Observation 9c89390c-9b6e-4f62-b496-a3582612956d · outbound

This paper cites Information-Directed Exploration for Deep Reinforcement Learning.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Information-Directed Exploration for Deep Reinforcement Learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.537013Z

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Observation d622ac8b-d2ed-4787-902e-af26baf6a01c · outbound

This paper cites #exploration: a study of count-based exploration for deep reinforcement learn- ing.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration #exploration: a study of count-based exploration for deep reinforcement learn- ing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.498768Z

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Observation 6bc11291-5792-419e-b1b4-934e1e991906 · outbound

This paper cites Unifying Count-Based Exploration and Intrinsic Motivation.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Unifying Count-Based Exploration and Intrinsic Motivation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.552841Z

Source-reported events for the cited work

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Observation e811dec9-9b06-429e-9eaa-578b2e8fcc74 · outbound

This paper cites DORA The Explorer: Directed Outreaching Reinforcement Action-Selection.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration DORA The Explorer: Directed Outreaching Reinforcement Action-Selection

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T22:41:31.303431Z

Source-reported events for the cited work

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Observation a18f12e1-b094-41e4-a807-4954bda079bc · outbound

This paper cites First return, then explore.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration First return, then explore

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.556234Z

Source-reported events for the cited work

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Observation fbe73b5c-29e8-428a-88fa-68e8aba9f5f2 · outbound

This paper cites Exploration by Random Network Distillation.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Exploration by Random Network Distillation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:12:18.061298Z

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Observation 965221b3-66da-4f8c-984f-49ef457b7e6f · outbound

This paper cites Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:12:18.058663Z

Source-reported events for the cited work

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Observation d11d55f5-9615-42af-b2e4-e7a3c584a167 · outbound

This paper cites an unresolved cited work.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-13T10:57:39.545902Z

Source-reported events for the cited work

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Observation bbe7fbfb-2ece-44ba-87c3-0cb463997c03 · outbound

This paper cites Efros, and Trevor Darrell.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Efros, and Trevor Darrell

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.571764Z

Source-reported events for the cited work

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Observation 7273da9f-38a0-4986-b410-b24b309d6990 · outbound

This paper cites Learning to Perform Physics Experiments via Deep Reinforcement Learning.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Learning to Perform Physics Experiments via Deep Reinforcement Learning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:12:18.072512Z

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Observation 3d49a223-3119-4ba3-964a-882a20ba8bdf · outbound

This paper cites Wilson, Jarvis A.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Wilson, Jarvis A

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.576806Z

Source-reported events for the cited work

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Observation c673d141-3e4b-4800-bcaf-f18af0c367a1 · outbound

This paper cites an unresolved cited work.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-13T10:57:39.549579Z

Source-reported events for the cited work

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Observation 7894b8f1-d423-4d63-bb42-352c9d14e558 · outbound

This paper cites Constantine.Active Subspaces.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Constantine.Active Subspaces

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.533250Z

Source-reported events for the cited work

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Observation cc6b1e70-5fc7-4c8d-89bd-d07ed80ef299 · outbound

This paper cites Sloman, Ayush Bharti, Julien Martinelli, and Samuel Kaski.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Sloman, Ayush Bharti, Julien Martinelli, and Samuel Kaski

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.535097Z

Source-reported events for the cited work

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Observation df38527a-239f-4366-b48b-65c2c698648b · outbound

This paper cites TD- MPC2: Scalable, Robust World Models for Continuous Control.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration TD- MPC2: Scalable, Robust World Models for Continuous Control

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.529910Z

Source-reported events for the cited work

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Observation b0937f8c-ffde-4a5e-aba7-75daea73a028 · outbound

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

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration ANYmal parkour: Learning agile navigation for quadrupedal robots.Science Robotics, 9(88):eadi7566

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.528271Z

Source-reported events for the cited work

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Observation 8d3cadd7-713a-4937-a8fe-528f741c9767 · outbound

This paper cites Humanoid Parkour Learning.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Humanoid Parkour Learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.551149Z

Source-reported events for the cited work

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Observation fdfa1623-a6bb-4a54-b44f-ebde7bb25c52 · outbound

This paper cites DTC: Deep Tracking Control.Science Robotics, 9(86):eadh5401.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration DTC: Deep Tracking Control.Science Robotics, 9(86):eadh5401

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.531591Z

Source-reported events for the cited work

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Observation 1ad36c02-bde2-46a5-b9d5-684bb9405548 · outbound

This paper cites DeX- treme: Transfer of Agile In-hand Manipulation from Simulation to Reality.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration DeX- treme: Transfer of Agile In-hand Manipulation from Simulation to Reality

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.540622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 047fe86d-563c-4c1f-be5b-af3f19367cc7 · outbound

This paper cites Reconciling Reality through Simulation: A Real-To-Sim-to-Real Approach for Robust Manipulation.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Reconciling Reality through Simulation: A Real-To-Sim-to-Real Approach for Robust Manipulation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.538737Z

Source-reported events for the cited work

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Observation 94b379d2-28ba-472c-b410-dc930e90ae7e · outbound

This paper cites Newton: GPU-accelerated physics simulation for robotics, and simulation research.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Newton: GPU-accelerated physics simulation for robotics, and simulation research

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.544200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1834196-e2ec-43ba-9346-5c0fba12f344 · outbound

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

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration When to Trust Your Model: Model-Based Policy Optimization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.573402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4fd541f-2194-404e-b955-832d5ef4801d · outbound

This paper cites Robotic world model: A neural network simulator for robust policy optimization in robotics.arXiv preprint arXiv:2501.10100, 2025a.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Robotic world model: A neural network simulator for robust policy optimization in robotics.arXiv preprint arXiv:2501.10100, 2025a

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:12:18.067237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c4a9c9a-88c1-4711-a2d5-b2d584af149d · outbound

This paper cites Kronecker- Factored Approximate Curvature for Modern Neural Net- work Architectures.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Kronecker- Factored Approximate Curvature for Modern Neural Net- work Architectures

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.481905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c92c54c2-7598-42db-a5e8-4b08b4b82492 · outbound

This paper cites Springer Science & Business Media.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Springer Science & Business Media

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.575112Z

Source-reported events for the cited work

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Observation e97a2a44-9f32-4114-8733-ec643cdfe5ba · outbound

This paper cites Society for Industrial and Applied Mathematics.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Society for Industrial and Applied Mathematics

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.554448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb9f0941-615c-48e4-9564-cfa529c2b681 · outbound

This paper cites Universal statistics of fisher information in deep neural networks: Mean field approach.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Universal statistics of fisher information in deep neural networks: Mean field approach

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.523042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 93a54b74-e241-40c9-8c17-6f8d2c125db0 · outbound

This paper cites Hauber, Marcus Rosen- blatt, Christian T ¨onsing, and Jens Timmer.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Hauber, Marcus Rosen- blatt, Christian T ¨onsing, and Jens Timmer

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.561389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ba655fb-0e62-4c83-940b-efeff3fc07b5 · outbound

This paper cites The cross-entropy method for com- binatorial and continuous optimization.Methodology and computing in applied probability, 1(2):127–190.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration The cross-entropy method for com- binatorial and continuous optimization.Methodology and computing in applied probability, 1(2):127–190

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.563356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af845e80-02cf-4c2a-9676-10c9069c23d5 · outbound

This paper cites DART: Dense Articulated Real-Time Tracking.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration DART: Dense Articulated Real-Time Tracking

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.517170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fff1691e-0e8a-4cf3-85e0-2a9a4b398b1f · outbound

This paper cites Radhakrishna Rao.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Radhakrishna Rao

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.515455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6062a18d-4a0a-4cec-acc9-68926425b908 · outbound

This paper cites Accelerated greedy algorithms for maximizing submodular set functions.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Accelerated greedy algorithms for maximizing submodular set functions

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.508657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7b06775-facd-4d0b-a800-aad7b405dcfe · outbound

This paper cites One Step Diffusion via Shortcut Models.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration One Step Diffusion via Shortcut Models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.570157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 372febc0-40c8-49d1-ad27-ee91270f0c87 · outbound

This paper cites Neural ordinary differential equations.Advances in neural information processing systems, 31.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Neural ordinary differential equations.Advances in neural information processing systems, 31

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.578815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b52960a2-ab04-45d3-8d3f-05051737da3e · outbound

This paper cites Attention is All you Need.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Attention is All you Need

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.568511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1452a6c5-a93e-452e-b1f6-e0ecd76498f8 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Proximal Policy Optimization Algorithms

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:12:18.063858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd502d59-20e2-4985-9964-861b0a49f36a · outbound

This paper cites Understanding Domain Randomization for Sim- to-real Transfer.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Understanding Domain Randomization for Sim- to-real Transfer

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.526542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1eb9796-1cc9-4f68-930a-ea6a9f874ba7 · outbound

This paper cites MuJoCo: A physics engine for model-based control.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration MuJoCo: A physics engine for model-based control

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.474232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3793f837-1846-4816-91cb-c7b9f277819f · outbound

This paper cites mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.506934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2f2c104a-3e5d-44a3-8654-7b1a0e6afc37 · outbound

This paper cites Action Flow Matching for Continual Robot Learning.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Action Flow Matching for Continual Robot Learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.559773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de2d2755-28fd-4f80-bc25-6a04a0caa443 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.470714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5804838f-f6d2-422c-8b40-0f98187929f9 · outbound

This paper cites Redeeming intrinsic rewards via constrained optimization.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Redeeming intrinsic rewards via constrained optimization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.472433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed0f8d5f-be16-45ac-b5e6-7e97b742ea52 · outbound

This paper cites Self- Supervised Exploration via Disagreement.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Self- Supervised Exploration via Disagreement

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.511986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7833e50-e7fb-4b06-9293-0f262209bf3a · outbound

This paper cites Planning to Explore via Self-Supervised World Models.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Planning to Explore via Self-Supervised World Models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.500930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2e84529-fbc3-4b31-a3c7-e800a4f5eba3 · outbound

This paper cites Discovering and Achieving Goals via World Models.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Discovering and Achieving Goals via World Models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.504849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 170713f5-24f1-4cc4-989a-dd189b419488 · outbound

This paper cites Stable-Baselines3: Reliable Reinforcement Learning Im- plementations.Journal of Machine Learning Research, 22(268):1–8.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Stable-Baselines3: Reliable Reinforcement Learning Im- plementations.Journal of Machine Learning Research, 22(268):1–8

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.502955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2424becb-0625-427c-a0ac-2555db925ba1 · outbound

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

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.510320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca6bf1ea-2193-499d-b90a-5e597216b6a8 · outbound

This paper cites Hu, James Springer, Oleh Rybkin, and Dinesh Jayaraman.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Hu, James Springer, Oleh Rybkin, and Dinesh Jayaraman

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.558078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55e482c0-d41e-4f2e-afa9-ee8c92f207a7 · outbound

This paper cites Real-to-Sim: Predict- ing Residual Errors of Robotic Systems with Sparse Data using a Learning-Based Unscented Kalman Filter.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Real-to-Sim: Predict- ing Residual Errors of Robotic Systems with Sparse Data using a Learning-Based Unscented Kalman Filter

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.513781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b155745-a770-4356-a372-ddd273b3f7d7 · outbound

This paper cites Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental V oxels.IEEE Robotics and Automation Letters, 7(2):4861–4868.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental V oxels.IEEE Robotics and Automation Letters, 7(2):4861–4868

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.480019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7fee6887-43eb-476d-9f99-b159bb31c991 · outbound

This paper cites Fast Extrinsic Calibration for Multiple Inertial Mea- surement Units in Visual-Inertial System.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Fast Extrinsic Calibration for Multiple Inertial Mea- surement Units in Visual-Inertial System

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.518949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09d28294-c511-4cf6-88db-f3d231c6e60e · outbound

This paper cites Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.478232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2a0f045a-6a14-48d1-9ee7-86ebf238d75c · outbound

This paper cites To match the dynamics model definition in the main text, we model the state increment x=s t+1 −s t Let the base noise distribution bep 0(δ) =N(0,I).

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration To match the dynamics model definition in the main text, we model the state increment x=s t+1 −s t Let the base noise distribution bep 0(δ) =N(0,I)

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.542483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9289020-1ee9-4276-9626-b2215d2c00e6 · outbound

This paper cites The total loss is L(θ) :=L FM(θ) +L SC(θ).

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration The total loss is L(θ) :=L FM(θ) +L SC(θ)

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.565042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 16d05924-8c09-419b-84d4-5ff978d47c8b · outbound

This paper cites Givenδ∼p 0(·)and conditioning c, define the one-step map Tθ(δ,c) :=δ−v θ(δ,1,c,1).

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration Givenδ∼p 0(·)and conditioning c, define the one-step map Tθ(δ,c) :=δ−v θ(δ,1,c,1)

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.524810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a990797-0f92-4a8d-86f0-81ebd800bedd · outbound

This paper cites For the shortcut-model transport map, the Jacobian is ∇δTθ(δ,c) =I− ∇ δvθ(δ,1,c,1), so the conditional log-density becomes logq θ(x|c) = logp 0(δ)−log|det(I− ∇ δvθ(δ,1,c,1))|.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration For the shortcut-model transport map, the Jacobian is ∇δTθ(δ,c) =I− ∇ δvθ(δ,1,c,1), so the conditional log-density becomes logq θ(x|c) = logp 0(δ)−log|det(I− ∇ δvθ(δ,1,c,1))|

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T10:57:39.521148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 531f9a8d-ebb1-49d5-b611-3c67a9ad7914 · outbound

This paper cites SinceF ϕ =E[gg ⊤], E[˜g˜g⊤] =E[W ⊤gg⊤W] =W ⊤F ϕW=Λ.

Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration SinceF ϕ =E[gg ⊤], E[˜g˜g⊤] =E[W ⊤gg⊤W] =W ⊤F ϕW=Λ

Reference 74

Resolution: malformed identifier; raw_fallback, observed 2026-05-13T10:57:39.476141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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