Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-11T00:18:08.485089Z
Paper Citation Record · LEDGER
As of 11 August 2026, this Paper Citation Record lists 100 of 102 outbound references and 0 inbound Pith citation observations for arXiv:2607.06501.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-11T00:18:08.485089Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 102 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation aea881c8-b91f-4c42-922c-ffa368c53292 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning From pixels to predicates: Learn- ing symbolic world models via pretrained vlms.IEEE Robotics and Automation Letters, 2026
Reference 1
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Active visual object search in unknown environments using uncertain semantics.IEEE Transactions on Robotics, 29(4):986–1002, 2013
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Observation 24dceb17-dfb7-4201-926d-6eba9b6b2bf4 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Do as i can, not as i say: Grounding language in robotic affordances
Reference 7
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Language-augmented symbolic planner for open-world task planning
Reference 8
Source-reported events for the cited work
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Observation 4130d129-e6bd-4922-9a9b-52eff9a0d721 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Situated bayesian reasoning framework for robots operating in diverse everyday environments
Reference 9
Source-reported events for the cited work
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Observation 91a463d6-c1c7-4a22-a200-a6eafc10843f · outbound
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Reference 10
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Observation 77b6204a-28df-4a3a-b426-7cca30a9d688 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Trust the proc3s: Solving long-horizon robotics problems with llms and constraint satisfaction
Reference 11
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Observation 59e8090a-22f6-4a1d-a00e-f97099717bca · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Partially observable task and motion planning with uncertainty and risk awareness
Reference 12
Source-reported events for the cited work
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Observation d85a1918-01c1-461f-9999-b4fc9e804f70 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Llm-guided probabilistic program induction for pomdp model estimation
Reference 13
Source-reported events for the cited work
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Observation d3392c79-c2e0-4035-ba29-34e7b48df8a6 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Embodied question answering
Reference 14
Source-reported events for the cited work
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Observation 6fd3689e-6bd3-45b3-8cc3-118903368284 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Procthor: large-scale embodied ai using procedural generation
Reference 15
Source-reported events for the cited work
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Observation ffd2cb9b-16ee-4b27-8908-394d4c6679e6 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Procthor: Large-scale embodied ai using procedural gen- eration.Advances in Neural Information Processing Systems, 35:5982–5994, 2022
Reference 16
Source-reported events for the cited work
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Observation bde3f545-5ee3-408d-b7a0-5d713b7920da · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Automated generation of robotic planning do- mains from observations
Reference 17
Source-reported events for the cited work
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Observation 4dec85b2-12ad-4dc6-a9cd-9f0902fb7992 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Integrating action knowledge and llms for task planning and situation handling in open worlds.Au- tonomous Robots, 47(8):981–997, 2023
Reference 18
Source-reported events for the cited work
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Observation 9078c7dc-facb-45cb-a55d-5df4c7969f0c · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning PaLM-E: An Embodied Multimodal Language Model
Reference 19
Source-reported events for the cited work
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Observation 1323cbe4-d864-4923-8dd0-39c29019297e · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning University of Massachusetts Amherst, 2002
Reference 20
Source-reported events for the cited work
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Observation a71552ee-8b82-4fa0-952d-2ca1d8fa5ee4 · outbound
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Reference 21
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning
Reference 22
Source-reported events for the cited work
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Observation 1b5c4be5-43e4-44c7-ac80-6f59975f1312 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Online replanning in belief space for partially observable task and motion problems
Reference 23
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 60bde9c6-d47e-407e-a39c-7f312a71d2f4 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Integrated task and motion plan- ning.Annual review of control, robotics, and autonomous systems, 4(1):265–293, 2021
Reference 24
Source-reported events for the cited work
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Observation 33712fec-8707-435f-800a-f46449de8787 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Improved techniques for grid mapping with rao- blackwellized particle filters.IEEE transactions on Robotics, 2007
Reference 25
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Ros navigation: Concepts and tutorial
Reference 26
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning The fast downward planning system
Reference 27
Source-reported events for the cited work
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Reference 28
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Reference 29
Source-reported events for the cited work
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Observation 0267b05a-e6b0-49ac-a6a9-cae6d2baca17 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Inner monologue: Embodied reasoning through planning with language models
Reference 30
Source-reported events for the cited work
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Observation c4b311f9-ac5b-4981-8405-f4341de3e254 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
Reference 31
Source-reported events for the cited work
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Reference 32
Source-reported events for the cited work
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Observation f925b0b4-a234-4e81-82f0-eb46a9b09c47 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving
Reference 33
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c8613774-9991-4885-a345-c66a22879a51 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Inte- grated task and motion planning in belief space.The International Journal of Robotics Research, 32(9-10): 1194–1227, 2013
Reference 34
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Reference 35
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning AI2-THOR: An Interactive 3D Environment for Visual AI
Reference 36
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Open-world task and motion plan- ning via vision-language model generated constraints
Reference 37
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Kinodynamic task and motion planning using vlm-guided and interleaved sampling
Reference 38
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Code as policies: Language model programs for em- bodied control
Reference 39
Source-reported events for the cited work
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Observation b9c4443e-3c04-4c01-8afd-5ff3c291e2ca · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Visualpredicator: Learning abstract world models with neuro-symbolic predicates for robot planning
Reference 40
Source-reported events for the cited work
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Observation 2c6ef8c3-4d72-4125-85ac-fdecac99b7a5 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Microsoft coco: Common objects in context
Reference 41
Source-reported events for the cited work
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Observation 3c63efe9-95b6-46f1-93e8-2f326039b35a · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
Reference 42
Source-reported events for the cited work
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Observation fc7dfe1f-be66-4bf2-9417-119d4239d845 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Learning compositional behaviors from demonstration and language
Reference 43
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Uncertainty quantification and confidence calibration in large language models: A survey
Reference 44
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Reflect: Summarizing robot experiences for failure explanation and correction
Reference 45
Source-reported events for the cited work
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Observation 7c2d6767-73c4-4b79-b4c2-854aef7a981a · outbound
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Reference 46
Source-reported events for the cited work
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Observation 928c0000-ed42-456e-9ec1-d125e64fe304 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Scaling open-vocabulary object detection.Advances in Neural Information Processing Systems, 36
Reference 47
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Behavior trees in robot control systems.Annual Review of Control, Robotics, and Autonomous Systems, 5(1):81–107, 2022
Reference 48
Source-reported events for the cited work
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Reference 49
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Explore until confident: Efficient exploration for embodied question answering
Reference 50
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Heuristics for numeric planning via subgoaling
Reference 51
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning TiPToP: A Modular Open-Vocabulary Robot Manipulation System That Plans
Reference 52
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Reflexion: Language Agents with Verbal Reinforcement Learning
Reference 53
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Pddl planning with pretrained large language models
Reference 54
Source-reported events for the cited work
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Reference 55
Source-reported events for the cited work
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Reference 56
Source-reported events for the cited work
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Reference 57
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Interactive planning using large language models for partially observable robotic tasks
Reference 58
Source-reported events for the cited work
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Observation 89a8255a-fd6a-4cce-85db-e332cbeec63f · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Tru-pomdp: Task planning under uncertainty via tree of hypotheses and open-ended pomdps
Reference 59
Source-reported events for the cited work
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Observation 975f68e9-8f1e-43bd-a164-f7664975485d · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning 10610948
Reference 60
Source-reported events for the cited work
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Observation 79bf3ca0-17ff-43b9-ba6e-75662b33b4ac · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Robot placement based on reachability inversion
Reference 61
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Observation 8032207b-cfa7-4b3b-8753-16c98df2f447 · outbound
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Chatgpt for robotics: Design principles and model abilities.Microsoft Auton
Reference 62
Source-reported events for the cited work
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Multi-object search using object-oriented pomdps
Reference 63
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Reference 64
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Jarvis-1: Open-world multi-task agents with memory-augmented multimodal language models.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
Reference 65
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Reference 67
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Reference 68
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning Translating Natural Language to Planning Goals with Large-Language Models
Reference 69
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Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning We provide a simple example prompt for the mobile manipulation setting.: Listing 10
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