First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines | Paper Citation Record

Observation 458d7b62-b30f-40d7-af80-cb4f60bae17e · outbound

This paper cites Empower dexterous robotic hand for human-centric smart manufacturing: A perception and skill learning perspective,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Empower dexterous robotic hand for human-centric smart manufacturing: A perception and skill learning perspective,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.412954Z

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Observation 0f053aa2-d2e3-4231-a8ee-efd90e9f0f7c · outbound

This paper cites Dexterous manipulation for multi-fingered robotic hands with reinforcement learning: A review,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dexterous manipulation for multi-fingered robotic hands with reinforcement learning: A review,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T16:17:33.579189Z

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Observation d74e2a5d-ff9b-49c6-a419-856739e42084 · outbound

This paper cites Planning multi-fingered grasps as probabilistic inference in a learned deep network,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Planning multi-fingered grasps as probabilistic inference in a learned deep network,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.401071Z

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Observation 8c0eb585-4390-40e4-ba27-80548bf0164d · outbound

This paper cites Multi- fingered grasp planning via inference in deep neural networks,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Multi- fingered grasp planning via inference in deep neural networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.389502Z

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Observation 5276ed9f-486d-4366-92c8-a161c64e73cb · outbound

This paper cites Modeling grasp type improves learning-based grasp planning,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Modeling grasp type improves learning-based grasp planning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.377885Z

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Observation 07bdb966-6f9f-480d-a941-ce51bae59ee6 · outbound

This paper cites Multi-fingered active grasp learning,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Multi-fingered active grasp learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.365357Z

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Observation 5c253253-c7cc-4e9c-acaa-3cd4dca40d67 · outbound

This paper cites Learning continuous 3d reconstructions for geometrically aware grasping,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Learning continuous 3d reconstructions for geometrically aware grasping,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.352043Z

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Observation 55654e8b-57cc-4c0f-9b2b-ffe34076c4f0 · outbound

This paper cites Planning visual-tactile precision grasps via complementary use of vision and touch,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Planning visual-tactile precision grasps via complementary use of vision and touch,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.340654Z

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Observation ab5e8f64-ee72-44f6-9f89-c63fa954c25f · outbound

This paper cites Learning robust real-world dexterous grasping policies via implicit shape augmentation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Learning robust real-world dexterous grasping policies via implicit shape augmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.329021Z

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Observation 6d04f9ad-d86a-4a6b-96ff-5031e8ef53e8 · outbound

This paper cites Neural geometric fabrics: Efficiently learning high-dimensional policies from demonstration,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Neural geometric fabrics: Efficiently learning high-dimensional policies from demonstration,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.315442Z

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Observation ad237aba-87a1-4625-9e95-1c235b8ca695 · outbound

This paper cites Learning dexterous in-hand manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Learning dexterous in-hand manipulation,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.153425Z

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Observation 7d014f24-6607-4c32-9ec8-52bdcc7ec921 · outbound

This paper cites Dextreme: Transfer of agile in-hand manipulation from simulation to reality,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dextreme: Transfer of agile in-hand manipulation from simulation to reality,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.302222Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1dcb9fdd-cfed-4ab5-ad1b-fe67894e8e11 · outbound

This paper cites Relaxed-rigidity constraints: Kinematic trajectory optimization and collision avoidance for in-grasp manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Relaxed-rigidity constraints: Kinematic trajectory optimization and collision avoidance for in-grasp manipulation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.290266Z

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Observation 3e73359d-2df1-4f65-93dd-58b08e61a51a · outbound

This paper cites Geometric in-hand regrasp planning: Alternating optimization of finger gaits and in-grasp manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Geometric in-hand regrasp planning: Alternating optimization of finger gaits and in-grasp manipulation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.277323Z

Source-reported events for the cited work

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Observation b85679cc-aedd-47c1-a897-3ddea00f3d8c · outbound

This paper cites Dexter- ous manipulation with deep reinforcement learning: Efficient, general, and low-cost,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dexter- ous manipulation with deep reinforcement learning: Efficient, general, and low-cost,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.265496Z

Source-reported events for the cited work

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Observation b77a0fba-46cb-4e0b-a053-00d6b0bf4e7c · outbound

This paper cites Robopianist: Dexterous piano playing with deep reinforcement learning,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Robopianist: Dexterous piano playing with deep reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.251933Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b4d1f82a-3592-4762-b799-29cccba53be7 · outbound

This paper cites Dex1b: Learning with 1b demonstrations for dexterous manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dex1b: Learning with 1b demonstrations for dexterous manipulation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.178803Z

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Observation 26b28c89-8210-4434-9703-dc6dfa2c1014 · outbound

This paper cites Ugg: Unified generative grasping,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Ugg: Unified generative grasping,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.230751Z

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Observation ca0c3098-0314-44a9-b0ce-8fcf00bd5540 · outbound

This paper cites Unigrasp: Learning a unified model to grasp with multifingered robotic hands,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Unigrasp: Learning a unified model to grasp with multifingered robotic hands,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.217484Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation de6468ad-918a-47d4-9cc6-bb8e3b411a4d · outbound

This paper cites Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.203670Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 515d5684-fef5-4ac2-8b59-c1612856dca4 · outbound

This paper cites Ffhnet: Generating multi-fingered robotic grasps for unknown objects in real-time,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Ffhnet: Generating multi-fingered robotic grasps for unknown objects in real-time,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.188019Z

Source-reported events for the cited work

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Observation ec967d9f-8a9f-4899-a98a-7c8bd95fbe68 · outbound

This paper cites Dexdiffuser: Generating dexterous grasps with diffusion models,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dexdiffuser: Generating dexterous grasps with diffusion models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.176151Z

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 254ca9f9-e291-4d27-9ce1-5017a2e65c36 · outbound

This paper cites Get a grip: Multi-finger grasp evaluation at scale enables robust sim-to-real transfer,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Get a grip: Multi-finger grasp evaluation at scale enables robust sim-to-real transfer,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.163028Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 97427bce-708f-4824-9036-c235aae56446 · outbound

This paper cites DextrAH-g: Pixels-to- action dexterous arm-hand grasping with geometric fabrics,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines DextrAH-g: Pixels-to- action dexterous arm-hand grasping with geometric fabrics,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.149763Z

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Observation 98afada8-ee07-4034-9d79-d3a69c51751c · outbound

This paper cites Grasp’d: Differentiable contact-rich grasp synthesis for multi-fingered hands,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Grasp’d: Differentiable contact-rich grasp synthesis for multi-fingered hands,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.134319Z

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Observation 6c9eea80-20a7-4595-857d-97f32188dbfb · outbound

This paper cites Fast-grasp’d: Dexterous multi-finger grasp generation through differentiable simulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Fast-grasp’d: Dexterous multi-finger grasp generation through differentiable simulation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.120539Z

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Observation d0d6a8b3-f48c-4f4b-ab23-3d78c5cceac6 · outbound

This paper cites Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.107125Z

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 294ac76f-f27d-4e04-948d-226731928043 · outbound

This paper cites Deep differentiable grasp planner for high-dof grippers,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Deep differentiable grasp planner for high-dof grippers,

Reference 28

Resolution: verified exact; doi, observed 2026-08-15T16:17:33.419724Z

Source-reported events for the cited work

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Observation 96e592c6-6362-4217-8ead-2569029d1c0c · outbound

This paper cites Curobo: Parallelized collision-free robot motion gen- eration,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Curobo: Parallelized collision-free robot motion gen- eration,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.092221Z

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 5b734f60-6f94-44dc-91b6-9c7490efc6ca · outbound

This paper cites cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.232671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2345d78-415d-4886-bf7a-6e4ef539a091 · outbound

This paper cites Geometric fabrics: Generalizing classical mechanics to capture the physics of behavior,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Geometric fabrics: Generalizing classical mechanics to capture the physics of behavior,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.077028Z

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 8eea6f6b-b682-4fcc-81f2-c4c41aecfd0c · outbound

This paper cites Global Tensor Motion Planning.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Global Tensor Motion Planning

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:17:33.499957Z

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Observation f94afaf9-ee56-46f0-bace-d7d0e3d030be · outbound

This paper cites Gpu-enabled parallel trajectory optimization framework for safe motion planning of autonomous vehicles,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Gpu-enabled parallel trajectory optimization framework for safe motion planning of autonomous vehicles,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.245513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bdb2a6f-1959-4ed1-bd88-3a884b65fe60 · outbound

This paper cites Hand posture subspaces for dexterous robotic grasping,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Hand posture subspaces for dexterous robotic grasping,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.053941Z

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 99858ea2-d180-48a2-8fa7-9159d6060c7f · outbound

This paper cites A probabilistic framework for uncertainty-aware high-accuracy precision grasping of unknown objects,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines A probabilistic framework for uncertainty-aware high-accuracy precision grasping of unknown objects,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.039590Z

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 2689404d-f337-45a0-a32d-ba6d7b79a3bf · outbound

This paper cites Synthesis and optimization of force closure grasps via sequential semidefinite programming,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Synthesis and optimization of force closure grasps via sequential semidefinite programming,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.025872Z

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 ce2747b9-d4bd-4700-acda-2266c4b8a227 · outbound

This paper cites Examples of 3d grasp quality computations,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Examples of 3d grasp quality computations,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:34.012759Z

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 cb89d7a0-67b0-4f30-9d67-71f7f37157f6 · outbound

This paper cites Computation of independent contact regions for grasping 3-d objects,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Computation of independent contact regions for grasping 3-d objects,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.996841Z

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 ae04f872-73aa-49dc-89a5-11b8482ce30a · outbound

This paper cites Synthesizing grasp configurations with specified contact regions,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Synthesizing grasp configurations with specified contact regions,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.982584Z

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 b1b92ba9-3f8f-4ccc-8d5e-050d19fa283e · outbound

This paper cites Coping with the grasping uncertainties in force-closure analysis,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Coping with the grasping uncertainties in force-closure analysis,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.967519Z

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 9d5143d9-7da7-4a50-b207-144035001a73 · outbound

This paper cites Hierarchical fingertip space: A unified framework for grasp planning and in-hand grasp adaptation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Hierarchical fingertip space: A unified framework for grasp planning and in-hand grasp adaptation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.954113Z

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 7faf52e6-70b8-47e9-8a6c-9251aed57f9d · outbound

This paper cites Grasp stability prediction for a dexterous robotic hand combining depth vision and haptic bayesian exploration,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Grasp stability prediction for a dexterous robotic hand combining depth vision and haptic bayesian exploration,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.939404Z

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 33b7b98c-17af-4fca-9d38-9e814433aa84 · outbound

This paper cites Neural grasp distance fields for robot manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Neural grasp distance fields for robot manipulation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.926149Z

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 b49c2588-0af2-4bd0-9701-d1f169c7118d · outbound

This paper cites Real-time grasp detection using convo- lutional neural networks,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Real-time grasp detection using convo- lutional neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.913189Z

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 e9f29063-bd85-47f3-8e70-6357e1b392df · outbound

This paper cites Real- time generative grasping with spatio-temporal sparse convolution,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Real- time generative grasping with spatio-temporal sparse convolution,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.899529Z

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 5dfe0b91-ec30-46f1-b1d3-299a62db300c · outbound

This paper cites Se(3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimiza- tion through diffusion,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Se(3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimiza- tion through diffusion,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.886329Z

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 898beb6d-338b-4763-aac1-27beb67a0cce · outbound

This paper cites Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.304652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28480750-a1f4-4330-a8fb-929addf76de0 · outbound

This paper cites An affordance keypoint detection network for robot manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines An affordance keypoint detection network for robot manipulation,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.307901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 730e93ff-e460-4abc-abdf-5fba73921f3a · outbound

This paper cites $\mathcal{D(R,O)}$ Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines $\mathcal{D(R,O)}$ Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.312218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 038348d0-c7cd-4349-88ba-b80e5c87d3e4 · outbound

This paper cites 23 dof grasping policies from a raw point cloud,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines 23 dof grasping policies from a raw point cloud,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.853767Z

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 84806ffd-59dc-431f-9f55-7f59fd9c3e0e · outbound

This paper cites Multi-fingan: Gener- ative coarse-to-fine sampling of multi-finger grasps,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Multi-fingan: Gener- ative coarse-to-fine sampling of multi-finger grasps,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.839358Z

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 30698451-37f4-4165-a9d4-188c0f39d581 · outbound

This paper cites Dvgg: Deep variational grasp generation for dextrous manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dvgg: Deep variational grasp generation for dextrous manipulation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.825332Z

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 deec4d3e-753f-4169-9239-e359cf822e8d · outbound

This paper cites Dexterous functional grasping,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dexterous functional grasping,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.703693Z

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 3de21a7c-49c4-4ec6-a153-074dcf80ba4a · outbound

This paper cites Dexrepnet: Learning dexterous robotic grasping network with geometric and spatial hand-object representations,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dexrepnet: Learning dexterous robotic grasping network with geometric and spatial hand-object representations,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.688505Z

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 b3283464-29f7-4b07-b235-38f9de93575a · outbound

This paper cites Dexpoint: Gener- alizable point cloud reinforcement learning for sim-to-real dexterous manipulation,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Dexpoint: Gener- alizable point cloud reinforcement learning for sim-to-real dexterous manipulation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.674335Z

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 51405a5b-9dc1-447c-b15e-0e53ebbd51cb · outbound

This paper cites Ready, set, plan! planning to goal sets using generalized bayesian inference,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Ready, set, plan! planning to goal sets using generalized bayesian inference,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.661306Z

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 10c2b371-1488-48cb-b027-5c404e243d79 · outbound

This paper cites Planning under Uncertainty to Goal Distributions.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Planning under Uncertainty to Goal Distributions

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:17:33.470511Z

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 1d04b8d5-cb92-4f60-8eb1-3fa7d5a491ce · outbound

This paper cites Efficient learning on point clouds with basis point sets,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Efficient learning on point clouds with basis point sets,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.648084Z

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 4747d0b0-b359-425e-8672-2a73e3f97e88 · outbound

This paper cites Visual dexterity: In-hand reorientation of novel and complex object shapes,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Visual dexterity: In-hand reorientation of novel and complex object shapes,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.355277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61a6c89c-0190-44bb-8df8-048fb3863915 · outbound

This paper cites Bigbird: A large-scale 3d database of object instances,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Bigbird: A large-scale 3d database of object instances,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.618207Z

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 65080897-9edd-4a8c-81ad-ef4b9c488c23 · outbound

This paper cites Pick and place planning is better than pick planning then place planning,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Pick and place planning is better than pick planning then place planning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.605125Z

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 165b0f16-6e87-4882-ac5b-052dab598724 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.372377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 081ee8b0-2610-444d-bbe1-e78da874f834 · outbound

This paper cites Robust bayesian scene reconstruction by leveraging retrieval-augmented priors,.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Robust bayesian scene reconstruction by leveraging retrieval-augmented priors,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:17:33.592846Z

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 e16bbd1e-c4b5-4406-b45b-95a20e561462 · outbound

This paper cites RaySt3R: Predicting Novel Depth Maps for Zero-Shot Object Completion.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines RaySt3R: Predicting Novel Depth Maps for Zero-Shot Object Completion

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T16:17:33.381473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e80d235d-f091-4244-801c-48670f63baec · outbound

This paper cites Available: https://www.science.org/doi/abs/10.1126/ scirobotics.adc9244.

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines Available: https://www.science.org/doi/abs/10.1126/ scirobotics.adc9244

Reference 2023

Resolution: malformed identifier; no resolver link, observed 2026-08-15T16:17:33.359646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers