Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T17:34:16.621055Z
Paper Citation Record · LEDGER
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
As of 21 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 3 inbound Pith citation observations for arXiv:2508.11049.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-12T00:23:04.763773Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T23:37:27.637033Z
Reference resolution
60 of 60 outbound references displayed
- verified exact0
- verified fuzzy44
- unresolved15
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation eb2d25f2-b3f1-4378-8b1e-bf33b484f530 · outbound
This paper cites Exploration by random network distillation, 2018.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Exploration by random network distillation, 2018
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.629157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 02bb7123-ae58-4b66-a5ac-cc5741486ca3 · outbound
This paper cites Karen Liu.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Karen Liu
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.613563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 7f77e170-a143-4b33-ab06-9268e4548b52 · outbound
This paper cites Diffusion policy: Visuomotor policy learning via action dif- fusion, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Diffusion policy: Visuomotor policy learning via action dif- fusion, 2024
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.597041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 9e65b646-b51f-457b-b5f3-586943d30094 · outbound
This paper cites Tapir: Tracking any point with per-frame initialization and temporal refinement, 2023.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Tapir: Tracking any point with per-frame initialization and temporal refinement, 2023
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.580743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5a1fb9ea-0913-42c0-a6d9-e209ba345f62 · outbound
This paper cites PaLM-E: An Embodied Multimodal Language Model.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning PaLM-E: An Embodied Multimodal Language Model
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.348711Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d327a378-c4da-4f6c-9a93-8af49c59be55 · outbound
This paper cites Video prediction models as rewards for reinforcement learning.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Video prediction models as rewards for reinforcement learning
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.561330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fb2bc097-3094-477a-a969-00460b62d0a9 · outbound
This paper cites Taming Transformers for High-Resolution Image Synthesis.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Taming Transformers for High-Resolution Image Synthesis
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.359323Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a4e0bf5-ae17-4c69-9644-64a00ab25a60 · outbound
This paper cites Learning robust re- wards with adversarial inverse reinforcement learning, 2018.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Learning robust re- wards with adversarial inverse reinforcement learning, 2018
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.542728Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1c2bee6e-15a7-42cd-8a1a-8db6bd2f101a · outbound
This paper cites FLIP: Flow-centric generative planning for general-purpose manipulation tasks.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning FLIP: Flow-centric generative planning for general-purpose manipulation tasks
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.524137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 74de8419-f25a-4f31-aa5c-28e0aa8e6ec8 · outbound
This paper cites Animatediff: Animate your personalized text-to- image diffusion models without specific tuning, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Animatediff: Animate your personalized text-to- image diffusion models without specific tuning, 2024
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.506127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1ab73b7d-863e-4310-a367-5ec3857c99b7 · outbound
This paper cites Bridging the human to robot dexterity gap through object-oriented rewards, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Bridging the human to robot dexterity gap through object-oriented rewards, 2024
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.489017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 755eda83-2570-4ea8-8c70-f123ec868fb3 · outbound
This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.473662Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 71b8bf93-f9da-4f17-884b-7e4573cd9d4b · outbound
This paper cites Learning prehensile dexterity by imitating and emulating state-only observations, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Learning prehensile dexterity by imitating and emulating state-only observations, 2024
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.457944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c0f37dad-f589-47ef-970d-286462bf8537 · outbound
This paper cites Generative adversarial im- itation learning, 2016.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Generative adversarial im- itation learning, 2016
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.442799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 53cffb84-2d3e-4673-a4be-afb0e93b9945 · outbound
This paper cites Video dif- fusion models.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Video dif- fusion models
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.397189Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 67133045-aa3c-4be8-ae70-32133e3b1f59 · outbound
This paper cites Spot: Se(3) pose trajectory diffusion for object-centric manipulation, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Spot: Se(3) pose trajectory diffusion for object-centric manipulation, 2024
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.416386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8e9f7e31-8c0e-46b5-81f7-7dbdcd7b3d10 · outbound
This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.400210Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c1901959-e516-461e-ade2-eece8f923d41 · outbound
This paper cites Ardup: Active region video diffusion for uni- versal policies.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Ardup: Active region video diffusion for uni- versal policies
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.384137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2b4bed6e-67ea-4ba6-b186-795b74d0c7a1 · outbound
This paper cites Dif- fusion reward: Learning rewards via conditional video dif- fusion.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Dif- fusion reward: Learning rewards via conditional video dif- fusion
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.368295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 64b6d281-e425-4718-8a89-b27b2fd796ef · outbound
This paper cites Rekep: Spatio-temporal reasoning of rela- tional keypoint constraints for robotic manipulation.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Rekep: Spatio-temporal reasoning of rela- tional keypoint constraints for robotic manipulation
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.352656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5879df8a-510b-4c6d-9c63-ca4fd7a97cff · outbound
This paper cites Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.427277Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d9624034-9316-4472-a1a6-bac8f2f26a5c · outbound
This paper cites Co- Tracker: It is better to track together.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Co- Tracker: It is better to track together
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.337050Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0a19b6a7-893d-4102-9426-4e7c40def128 · outbound
This paper cites Egomimic: Scaling imitation learning via egocentric video,.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Egomimic: Scaling imitation learning via egocentric video,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.321492Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8566e63f-2d86-4358-92a6-528c114ceee0 · outbound
This paper cites Segment Anything.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Segment Anything
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.444312Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31d82e04-ad2a-45e3-b24f-79c6f16855b4 · outbound
This paper cites Learning to Act from Actionless Videos through Dense Correspondences.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Learning to Act from Actionless Videos through Dense Correspondences
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.449552Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f3946725-2d7f-4ec8-ab43-b38349dbf675 · outbound
This paper cites Learning hand-eye coordination for robotic grasping with large-scale data col- lection.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Learning hand-eye coordination for robotic grasping with large-scale data col- lection
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.306340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 943040e0-5092-4bc6-bc61-03e27eea92df · outbound
This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.459990Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e4a506c-8e99-45fe-9511-56b2e7f0abf8 · outbound
This paper cites P3-po: Prescriptive point priors for visuo- spatial generalization of robot policies, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning P3-po: Prescriptive point priors for visuo- spatial generalization of robot policies, 2024
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.290798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 72be033c-8922-431d-b58e-8dd05173c27e · outbound
This paper cites LEAGUE++: EMPOWERING CONTINUAL ROBOT LEARNING THROUGH GUIDED SKILL ACQUISITION WITH LARGE LANGUAGE MODELS.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning LEAGUE++: EMPOWERING CONTINUAL ROBOT LEARNING THROUGH GUIDED SKILL ACQUISITION WITH LARGE LANGUAGE MODELS
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.274212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 77d08b0a-44bb-468a-bc25-65087bfc8a0b · outbound
This paper cites Code as Policies: Language Model Programs for Embodied Control.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Code as Policies: Language Model Programs for Embodied Control
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.474395Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 982eec53-54ac-4390-9dbe-77f9028ebe5b · outbound
This paper cites Dreamitate: Real-World Visuomotor Policy Learning via Video Generation.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Dreamitate: Real-World Visuomotor Policy Learning via Video Generation
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.479752Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74e115f8-588c-4583-9384-d92df23dc8e4 · outbound
This paper cites Flowretrieval: Flow-guided data retrieval for few-shot imitation learning.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Flowretrieval: Flow-guided data retrieval for few-shot imitation learning
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.258689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fd085c73-15cd-4159-af43-f8ff9b4a736a · outbound
This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.243468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3f39f1da-1f17-4387-ac31-cb60afa4ae0e · outbound
This paper cites Decoupled weight decay regularization, 2019.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Decoupled weight decay regularization, 2019
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.228096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4a5b35cd-5124-43bd-a251-fcbc8cda5b1b · outbound
This paper cites Liv: Language-image representations and rewards for robotic control, 2023.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Liv: Language-image representations and rewards for robotic control, 2023
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.212375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e0b60559-47d9-410f-b2f2-97e0a36bb506 · outbound
This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Eureka: Human-Level Reward Design via Coding Large Language Models
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.503192Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 345bf1f2-5844-4758-9904-187156968881 · outbound
This paper cites A real-to-sim-to-real approach to robotic manip- ulation with vlm-generated iterative keypoint rewards, 2025.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning A real-to-sim-to-real approach to robotic manip- ulation with vlm-generated iterative keypoint rewards, 2025
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.197318Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b36f2b0f-2fa0-4b35-8506-e177db68e7a8 · outbound
This paper cites Learning transferable visual models from natural language supervision, 2021.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Learning transferable visual models from natural language supervision, 2021
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.181916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 00c4cd89-31f7-4178-a6f3-0cb205ac52fb · outbound
This paper cites High-resolution image syn- thesis with latent diffusion models, 2022.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning High-resolution image syn- thesis with latent diffusion models, 2022
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.165278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 16d9d283-2a4c-4d8f-b784-09d8c44b1401 · outbound
This paper cites Proximal policy optimization algo- rithms, 2017.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Proximal policy optimization algo- rithms, 2017
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.148342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0f15d7e8-70d5-4f9b-8e39-0b4927fcdc11 · outbound
This paper cites Motion before action: Diffusing object mo- tion as manipulation condition, 2025.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Motion before action: Diffusing object mo- tion as manipulation condition, 2025
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.132851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation be256581-29cf-4e5a-9002-1ac67f0860ca · outbound
This paper cites Embodiment-agnostic action plan- ning via object-part scene flow, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Embodiment-agnostic action plan- ning via object-part scene flow, 2024
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.115537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 416383d9-8976-4fcf-8936-ff22d02b25dc · outbound
This paper cites Gpt-4 technical report, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Gpt-4 technical report, 2024
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.097535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 049b3a01-4d59-4fef-8492-df1a31f4ec8c · outbound
This paper cites Mujoco: A physics engine for model-based control.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Mujoco: A physics engine for model-based control
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.080485Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e5fdc360-2d92-4ea1-be5e-e7b4fad60523 · outbound
This paper cites Llama: Open and efficient foundation lan- guage models, 2023.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Llama: Open and efficient foundation lan- guage models, 2023
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.546041Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16fc3d5a-1d4c-4210-9798-f7ff9dc399a8 · outbound
This paper cites This&That: Language-Gesture Controlled Video Generation for Robot Planning.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning This&That: Language-Gesture Controlled Video Generation for Robot Planning
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.551453Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fbfd0454-a140-49a1-8c8b-070157e6256b · outbound
This paper cites Rl-vlm-f: Rein- forcement learning from vision language foundation model feedback, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Rl-vlm-f: Rein- forcement learning from vision language foundation model feedback, 2024
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.051643Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 111b3318-2c11-4155-be3a-463b9ffbde6c · outbound
This paper cites ivideogpt: Interactive videogpts are scalable world models.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning ivideogpt: Interactive videogpts are scalable world models
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.034198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 9cdfc636-5d5c-4231-9b8f-0ec7a361df0b · outbound
This paper cites Text2reward: Reward shaping with language models for re- inforcement learning, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Text2reward: Reward shaping with language models for re- inforcement learning, 2024
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:17.006797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d7a2141d-d51b-4901-856f-ab3e77ebc146 · outbound
This paper cites Learning by watching: Physical imitation of manipulation skills from human videos.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Learning by watching: Physical imitation of manipulation skills from human videos
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:16.990550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 538cc65c-f21d-47c6-bbcd-1ea8730901f5 · outbound
This paper cites Flow as the cross-domain manipulation interface, 2024.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Flow as the cross-domain manipulation interface, 2024
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:16.972680Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ee421b08-1027-43ba-86b9-674f42207e33 · outbound
This paper cites Videogpt: Video generation using vq-vae and trans- formers, 2021.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Videogpt: Video generation using vq-vae and trans- formers, 2021
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.581174Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 065c43ce-3d36-4f1f-a82d-94c11e170b46 · outbound
This paper cites Rank2reward: Learning shaped reward functions from passive video.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Rank2reward: Learning shaped reward functions from passive video
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:16.943647Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d8fcf0c8-a946-4fd2-9e59-c666925803dd · outbound
This paper cites Mastering visual continuous control: Improved data- augmented reinforcement learning.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Mastering visual continuous control: Improved data- augmented reinforcement learning
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:16.925332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 27a6d6c5-8097-4dfa-a4c2-8861cc628179 · outbound
This paper cites Mimictouch: Leveraging multi-modal human tactile demonstrations for contact-rich manipulation.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Mimictouch: Leveraging multi-modal human tactile demonstrations for contact-rich manipulation
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:16.907420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c09db6e3-57b8-4626-a817-38dc89209adb · outbound
This paper cites Sketch-to-skill: Bootstrap- ping robot learning with human drawn trajectory sketches,.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Sketch-to-skill: Bootstrap- ping robot learning with human drawn trajectory sketches,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:16.890128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5d9a7c67-f249-4403-9341-d4437bd7d581 · outbound
This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforce- ment learning, 2021.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Meta-world: A benchmark and evaluation for multi-task and meta reinforce- ment learning, 2021
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:34:16.873795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2e9e7cc1-fbca-4fc3-b507-efb8227213f3 · outbound
This paper cites General Flow as Foundation Affordance for Scalable Robot Learning.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning General Flow as Foundation Affordance for Scalable Robot Learning
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.610116Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1197e062-09bd-4a73-b38d-aacb2df96217 · outbound
This paper cites TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:16.615001Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54776263-f9c2-40e8-bfb8-79538158b79a · outbound
This paper cites Same as the Im2FLow2Act [51], we encode the object flow into a latent space and train the generative model based on it.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning Same as the Im2FLow2Act [51], we encode the object flow into a latent space and train the generative model based on it
Reference 60
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-15T17:34:16.857087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
Observation 6e638db2-5f31-415b-b615-fc95019e14fd · inbound
HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos cites this paper.
HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T15:45:48.690090Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4e1ef823-aea7-4996-bf5f-564dc17c4738 · inbound
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video cites this paper.
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T23:37:27.638497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 47379a3c-ab9a-44b6-871f-a182c7fcfbed · inbound
OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies cites this paper.
OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T00:23:04.763773Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.