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Paper Citation Record · LEDGER

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent

As of 13 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2411.08566.

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

pith.paper-citation-record.v1
2411.08566 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:34:34.308025Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:30:53.210150Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-12T18:16:22.223912Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact9
  • verified fuzzy0
  • unresolved16
  • parse uncertain0
  • malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 28d87d55-fbe0-417e-853a-01bd39d68599 · outbound

This paper cites Data-driven grasp synthesis—A survey,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Data-driven grasp synthesis—A survey,

Reference 1

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Observation 0eae6d8e-fe39-4a79-899f-4ffd00765608 · outbound

This paper cites Trends and challenges in robot manipula- tion,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Trends and challenges in robot manipula- tion,

Reference 2

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doi, observed 2026-08-12T21:34:34.463127Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation da6b3c9c-25f0-4454-b1b3-8b1eebbcab78 · outbound

This paper cites Prehensile pushing: In-hand manipulation with push-primitives,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Prehensile pushing: In-hand manipulation with push-primitives,

Reference 3

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Observation 8f683992-e636-473f-bf97-4b73248ff72b · outbound

This paper cites Robot learning of manipulation actions with deep reinforcement learning based on object-oriented state representation,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Robot learning of manipulation actions with deep reinforcement learning based on object-oriented state representation,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 257f926d-5ed2-4499-a907-7162b5450446 · outbound

This paper cites A survey of multi-robot interaction research and its applications,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent A survey of multi-robot interaction research and its applications,

Reference 5

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Observation 4e87909e-45a8-411c-9bd5-f0ab4b57741a · outbound

This paper cites Robot grasping in cluttered environments: Towards robust 3D object recognition with noise resilience,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Robot grasping in cluttered environments: Towards robust 3D object recognition with noise resilience,

Reference 6

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Observation bf4690dc-7bef-4162-bf40-d43f35e31830 · outbound

This paper cites Reinforcement learning in la- tent action sequence space,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Reinforcement learning in la- tent action sequence space,

Reference 7

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Observation 66db2b32-fd78-4d08-907d-2f92e2697bbf · outbound

This paper cites A review of robot learn- ing for manipulation: Challenges, representations, and algorithms,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent A review of robot learn- ing for manipulation: Challenges, representations, and algorithms,

Reference 8

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Observation d6766b9c-c1fe-4ecd-90f8-9709ffaac5b3 · outbound

This paper cites Robot skill learning in the latent space of a deep autoencoder,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Robot skill learning in the latent space of a deep autoencoder,

Reference 9

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Observation bc491b89-6b09-424f-ba8e-969a63f3e599 · outbound

This paper cites Generating multi-fingered robotic grasps via deep learning,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Generating multi-fingered robotic grasps via deep learning,

Reference 10

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Observation 34da8c78-404f-40dc-b677-31cf842ea85b · outbound

This paper cites Deep reinforcement learning based moving object grasping,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Deep reinforcement learning based moving object grasping,

Reference 12

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Observation 60a89b48-0bcd-40ed-8d57-1a2e0933b046 · outbound

This paper cites Visual-based robotic grasping under unstructured environment,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Visual-based robotic grasping under unstructured environment,

Reference 13

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Observation 8e519efb-aaf2-4e10-b373-b33522783247 · outbound

This paper cites Data-efficient deep rein- forcement learning for dexterous manipulation,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Data-efficient deep rein- forcement learning for dexterous manipulation,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 640dcb7f-6eac-4bc3-8391-b0fdb87da1a2 · outbound

This paper cites Robotic grasping using deep rein- forcement learning,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Robotic grasping using deep rein- forcement learning,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 20e8e749-6cbd-4acb-94a4-c921128f6196 · outbound

This paper cites Learning the latent space of robot dynamics for cutting interaction inference,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Learning the latent space of robot dynamics for cutting interaction inference,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 39a47585-16fc-4498-8cb7-35c91cc4fede · outbound

This paper cites CVML-Pose: Convolutional AE based multi-level network for object 3D pose estimation,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent CVML-Pose: Convolutional AE based multi-level network for object 3D pose estimation,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 63e5e3c0-2fef-49a8-a5a7-ccd70eeae240 · outbound

This paper cites PoseRBPF: A Rao–Blackwellized particle filter for 6-D object pose tracking,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent PoseRBPF: A Rao–Blackwellized particle filter for 6-D object pose tracking,

Reference 18

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c76eb22c-44c2-4f40-a26b-c96fd2bcc8e2 · outbound

This paper cites Neighborhood geometric structure- preserving variational autoencoder for smooth and bounded data sources,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Neighborhood geometric structure- preserving variational autoencoder for smooth and bounded data sources,

Reference 19

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Observation 27218e53-9d64-414a-a1ea-306b5086cf53 · outbound

This paper cites 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation,

Reference 23

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Observation 870cf8a2-a2b6-4bf7-95ee-245e14090800 · outbound

This paper cites Contact- GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Contact- GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes,

Reference 24

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Observation 2d9c5987-5f29-4795-a979-e82a9990f4e1 · outbound

This paper cites A model-free 6- DOF grasp detection method based on point clouds of local sphere area,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent A model-free 6- DOF grasp detection method based on point clouds of local sphere area,

Reference 25

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Observation 8726a3e4-6e1d-43bd-88db-dc30c11daadb · outbound

This paper cites Efficient Heatmap-Guided 6-DoF Grasp Detection in Cluttered Scenes,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Efficient Heatmap-Guided 6-DoF Grasp Detection in Cluttered Scenes,

Reference 26

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Observation 5ef11523-16d8-4c32-bf68-110eaa68baee · outbound

This paper cites LieGrasPFormer: Point Transformer- Based 6-DOF Grasp Detection with Lie Algebra Grasp Rep- resentation,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent LieGrasPFormer: Point Transformer- Based 6-DOF Grasp Detection with Lie Algebra Grasp Rep- resentation,

Reference 27

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Observation 70000ec5-f8bd-44b3-9b0b-696a00fd65c5 · outbound

This paper cites Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

Reference 28

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Observation e35caeb8-5688-482a-95a4-634f982957d1 · outbound

This paper cites GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF

Reference 29

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Observation ccf1f7b2-f199-4d67-8197-8e0c20bfe2ee · outbound

This paper cites FeatureNet: Machining feature recognition based on 3D Convolutional Neural Network,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent FeatureNet: Machining feature recognition based on 3D Convolutional Neural Network,

Reference 30

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation bee5eb48-38c2-4f00-8021-b95ea024196f · outbound

This paper cites Policy search for motor primitives in robotics,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Policy search for motor primitives in robotics,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 38cbd7f0-42ce-4b95-832b-bd9ab4fda526 · outbound

This paper cites Estimating the center of mass of an object with nonuniform density using robotic pushing,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Estimating the center of mass of an object with nonuniform density using robotic pushing,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 8f8e5356-9916-4326-8b73-88834924bcb0 · outbound

This paper cites Vision-based moment of inertia estimation for noncooperative space objects,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Vision-based moment of inertia estimation for noncooperative space objects,

Reference 33

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation cf61853d-de73-4ca9-b821-eb523b9238fc · outbound

This paper cites Reducing the dimensionality of data with neural networks,.

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent Reducing the dimensionality of data with neural networks,

Reference 34

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Pith citing papers

Observation 13ec60a0-fe64-4b80-a852-784291c79cc4 · inbound

Continuous In-Situ and Remote Sun Observation for Space Weather Monitoring and Mitigation of Infrastructure Threats Through an Optimized Heliocentric Satellite Constellation cites this paper.

Continuous In-Situ and Remote Sun Observation for Space Weather Monitoring and Mitigation of Infrastructure Threats Through an Optimized Heliocentric Satellite Constellation Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent

Reference 36

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local_arxiv, observed 2026-08-12T14:30:53.246195Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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