Paper Citation Record · LEDGER

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation

As of 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2602.08557.

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

pith.paper-citation-record.v1
2602.08557 v2

Coverage vector

measured 62 of 62 reference resolution

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measured 63 of 63 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T06:36:28.586778Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:37:26.736986Z

Reference resolution

62 of 62 outbound references displayed

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Outbound references

Observation 0a78c7f9-4d01-4aaf-acf9-3bc444364f34 · outbound

This paper cites Solving Rubik's Cube with a Robot Hand.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Solving Rubik's Cube with a Robot Hand

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:39.888001Z

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Observation 71d2c116-5951-4170-a71b-2eab23533629 · outbound

This paper cites Hindsight experience replay.Ad- vances in neural information processing systems, 30, 2017.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Hindsight experience replay.Ad- vances in neural information processing systems, 30, 2017

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:40.003451Z

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Observation 74d95574-de97-4af4-a0ea-dbef81513d00 · outbound

This paper cites Consensus complementarity control for multi-contact mpc.IEEE Transactions on Robotics, 2024.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Consensus complementarity control for multi-contact mpc.IEEE Transactions on Robotics, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:40.080431Z

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Observation 348d7fe1-08e8-49de-95a5-d57b619e308a · outbound

This paper cites Guided goal generation for hindsight multi- goal reinforcement learning.Neurocomputing, 359: 353–367, 2019.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Guided goal generation for hindsight multi- goal reinforcement learning.Neurocomputing, 359: 353–367, 2019

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:40.226418Z

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Observation de9c0539-9241-44f2-847b-cfe9c94f4f23 · outbound

This paper cites Efficient online reinforcement learn- ing with offline data.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Efficient online reinforcement learn- ing with offline data

Reference 5

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Observation 6faf0550-222e-46fc-bbcb-aa66f777754e · outbound

This paper cites Cambridge university press, 2004.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Cambridge university press, 2004

Reference 6

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Observation 8bfcef64-9018-465c-9e9b-250679ec11b6 · outbound

This paper cites Trajectory First: A Curriculum for Discovering Diverse Policies.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Trajectory First: A Curriculum for Discovering Diverse Policies

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:40.489773Z

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Observation 092c6880-7c3d-4b24-b553-d1fe6c3fdb00 · outbound

This paper cites Tenenbaum, Tim Rockt¨aschel, and Edward Grefenstette.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Tenenbaum, Tim Rockt¨aschel, and Edward Grefenstette

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:40.659846Z

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Observation a11b49b1-6d7b-4230-a3bc-3dca10e0e33a · outbound

This paper cites Goal-conditioned reinforcement learning with imagined subgoals.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Goal-conditioned reinforcement learning with imagined subgoals

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:40.791116Z

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Observation 9312a0e2-cd30-46b3-b62e-910765ff927b · outbound

This paper cites Whole-body motion planning with centroidal dynamics and full kinematics.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Whole-body motion planning with centroidal dynamics and full kinematics

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:40.924236Z

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Observation af2ab279-dcbd-4450-a988-4f1d92f8ff45 · outbound

This paper cites Imitating Task and Motion Planning with Visuomotor Transformers.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Imitating Task and Motion Planning with Visuomotor Transformers

Reference 11

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Observation 0c82b822-c67b-4582-bd46-3986c1f99f1d · outbound

This paper cites Footstep planning on uneven terrain with mixed-integer convex opti- mization.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Footstep planning on uneven terrain with mixed-integer convex opti- mization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.080155Z

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Observation 63a70379-42df-4df1-96cf-80e300968bd9 · outbound

This paper cites Curriculum-guided hindsight experience replay.Advances in neural information processing systems, 32, 2019.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Curriculum-guided hindsight experience replay.Advances in neural information processing systems, 32, 2019

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.128554Z

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Observation 1710f86c-1cef-413e-af9e-743cc411e77d · outbound

This paper cites Reverse cur- riculum generation for reinforcement learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Reverse cur- riculum generation for reinforcement learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.182925Z

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Observation 4c89126a-fed1-403d-b17b-86c02b785592 · outbound

This paper cites A mini- malist approach to offline reinforcement learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation A mini- malist approach to offline reinforcement learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.241596Z

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Observation f1b6f8b0-a930-487f-9b2b-12a429152eef · outbound

This paper cites Addressing function approximation error in actor- critic methods.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Addressing function approximation error in actor- critic methods

Reference 16

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Observation 17333686-f503-4f84-94e3-3adf917ffa2a · outbound

This paper cites For sale: State-action representation learning for deep reinforcement learning.Advances in neural information processing systems, 36:61573–61624, 2023.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation For sale: State-action representation learning for deep reinforcement learning.Advances in neural information processing systems, 36:61573–61624, 2023

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.406600Z

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Observation 7c78450a-397a-4d42-9f27-14a11cce383f · outbound

This paper cites Goal-conditioned on-policy reinforcement learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Goal-conditioned on-policy reinforcement learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.454298Z

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Observation 5595e818-1a2f-4e3e-888d-15de75507ab3 · outbound

This paper cites Rosati Papini, Patrick M.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Rosati Papini, Patrick M

Reference 19

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Observation 6c0cfc75-03bc-423f-8874-4307bbfcd60a · outbound

This paper cites Hopkins, Georg Wiedebach, Jared Bishop, Steven Pickles, David M ¨uller, and Moritz B ¨acher.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Hopkins, Georg Wiedebach, Jared Bishop, Steven Pickles, David M ¨uller, and Moritz B ¨acher

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.582037Z

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Observation 94958bf7-0d87-4884-9555-ba89a3a63f56 · outbound

This paper cites Relay policy learning: Solving long-horizon tasks via imitation 9 and reinforcement learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Relay policy learning: Solving long-horizon tasks via imitation 9 and reinforcement learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.641014Z

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Observation 686709a8-a2dd-49f8-980a-6aff9f8b3769 · outbound

This paper cites Com- pletely derandomized self-adaptation in evolution strategies.Evolutionary computation, 9(2):159–195, 2001.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Com- pletely derandomized self-adaptation in evolution strategies.Evolutionary computation, 9(2):159–195, 2001

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.684555Z

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Observation 8e96e5d1-1881-4c09-bf9f-4d7c9d25a6e8 · outbound

This paper cites Adaptive curriculum generation from demon- strations for sim-to-real visuomotor control.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Adaptive curriculum generation from demon- strations for sim-to-real visuomotor control

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.759263Z

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Observation 728ce637-c309-4b5c-8bff-67e6ef070395 · outbound

This paper cites Imitation bootstrapped reinforcement learn- ing.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Imitation bootstrapped reinforcement learn- ing

Reference 24

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Observation 33707ae8-1d4d-40fe-8621-96ddfdea4268 · outbound

This paper cites MRHER: model-based relay hindsight experience replay for sequential object manipulation tasks with sparse rewards.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation MRHER: model-based relay hindsight experience replay for sequential object manipulation tasks with sparse rewards

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:41.956297Z

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Observation 5399d154-392f-4eac-9870-fa665a2caff4 · outbound

This paper cites $\pi^{*}_{0.6}$: a VLA That Learns From Experience.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation $\pi^{*}_{0.6}$: a VLA That Learns From Experience

Reference 26

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Observation 4d20cb4d-a467-42ce-ba69-623f086e7026 · outbound

This paper cites SEIL: Simulation-augmented Equivariant Imitation Learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation SEIL: Simulation-augmented Equivariant Imitation Learning

Reference 27

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Observation 04e2b77d-3975-48b9-9ac1-8b0d25681ae2 · outbound

This paper cites Reinforcement learning from imperfect demon- strations under soft expert guidance.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Reinforcement learning from imperfect demon- strations under soft expert guidance

Reference 28

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Observation eadb4438-bd95-400c-9f20-78815afb413b · outbound

This paper cites An Introduction to Zero-Order Optimization Techniques for Robotics.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation An Introduction to Zero-Order Optimization Techniques for Robotics

Reference 29

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Observation 4cc63ff3-eba0-4b79-9195-a5b20d03e085 · outbound

This paper cites Grasping with chopsticks: Combating covariate shift in model-free imitation learning for fine ma- nipulation.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Grasping with chopsticks: Combating covariate shift in model-free imitation learning for fine ma- nipulation

Reference 30

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Observation 8a8f1ca8-9c55-422b-9214-e9a3ef1746d9 · outbound

This paper cites CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning

Reference 31

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Observation 393fe058-23e8-443f-be5f-2d834d94b650 · outbound

This paper cites Prehensile manipulation planning: Modeling, algorithms and implementation.IEEE Transactions on Robotics, 38 (4):2370–2388, 2021.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Prehensile manipulation planning: Modeling, algorithms and implementation.IEEE Transactions on Robotics, 38 (4):2370–2388, 2021

Reference 32

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Observation 967d1248-c825-40aa-a5ae-3c598343bde9 · outbound

This paper cites LaValle.Planning Algorithms.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation LaValle.Planning Algorithms

Reference 33

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Observation c2d5c605-85bc-4c4a-9dac-e6d62e0538b8 · outbound

This paper cites Leveraging randomized smoothing for optimal control of nonsmooth dynamical systems.Nonlinear Analysis: Hybrid Systems, 52:101468, 2024.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Leveraging randomized smoothing for optimal control of nonsmooth dynamical systems.Nonlinear Analysis: Hybrid Systems, 52:101468, 2024

Reference 34

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Observation 5e35741b-8585-4856-96b7-a9ebb544b128 · outbound

This paper cites Learning Multi-Level Hierarchies with Hindsight.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Learning Multi-Level Hierarchies with Hindsight

Reference 35

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Observation 92117946-ab01-4571-9eb4-6d650de3df14 · outbound

This paper cites Diversity Progress for Goal Selection in Discriminability-Motivated RL.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Diversity Progress for Goal Selection in Discriminability-Motivated RL

Reference 36

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Observation b9365b04-1e10-45c2-b593-8f08d08621fd · outbound

This paper cites Opt2skill: Imi- tating dynamically-feasible whole-body trajectories for versatile humanoid loco-manipulation.IEEE Robotics and Automation Letters, 2025.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Opt2skill: Imi- tating dynamically-feasible whole-body trajectories for versatile humanoid loco-manipulation.IEEE Robotics and Automation Letters, 2025

Reference 37

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Observation 637b6f5f-cc41-4723-871b-1056ee1704ea · outbound

This paper cites Learn goal-conditioned policy with intrinsic motivation for deep reinforcement learn- ing.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Learn goal-conditioned policy with intrinsic motivation for deep reinforcement learn- ing

Reference 38

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Observation 94bb907f-f133-47cb-82c3-123359e34b53 · outbound

This paper cites A simple motion-planning algorithm for general robot manipulators.IEEE Journal on Robotics and Automation, 3(3):224–238, 2003.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation A simple motion-planning algorithm for general robot manipulators.IEEE Journal on Robotics and Automation, 3(3):224–238, 2003

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:43.412356Z

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Observation 4a56b452-3a57-4d72-8451-f492c2f28916 · outbound

This paper cites Crocoddyl: An efficient and versatile framework for multi-contact optimal control.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Crocoddyl: An efficient and versatile framework for multi-contact optimal control

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Observation 25fd7c43-c00a-4e41-9f68-03bc08d462ee · outbound

This paper cites Discovery of complex behaviors through contact-invariant optimization.ACM Transactions on Graphics (ToG), 31(4):1–8, 2012.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Discovery of complex behaviors through contact-invariant optimization.ACM Transactions on Graphics (ToG), 31(4):1–8, 2012

Reference 41

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Observation 46a9d70e-cab1-4fbb-ae47-762cf9c9a4b9 · outbound

This paper cites Data-efficient hierarchical reinforce- ment learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Data-efficient hierarchical reinforce- ment learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:43.644522Z

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Observation 80d062c6-0d99-4778-a510-c19360bbdee0 · outbound

This paper cites Visual rein- forcement learning with imagined goals.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Visual rein- forcement learning with imagined goals

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:43.721053Z

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Observation bc0baf5d-532c-4ac4-9572-71aa39e25154 · outbound

This paper cites Deep exploration via bootstrapped DQN.Advances in neural information processing systems, 29, 2016.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Deep exploration via bootstrapped DQN.Advances in neural information processing systems, 29, 2016

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:43.804347Z

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Observation 1959e961-7c8b-4779-9b5a-9c0f6f534144 · outbound

This paper cites Deep black-box reinforcement learning with movement primitives.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Deep black-box reinforcement learning with movement primitives

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:43.868995Z

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Observation 7aeaa312-056c-4fd9-9775-3a0ec292c050 · outbound

This paper cites Learning from trajectories via subgoal discovery.Advances in Neural Information Processing Systems, 32, 2019.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Learning from trajectories via subgoal discovery.Advances in Neural Information Processing Systems, 32, 2019

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:43.986784Z

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Observation fc03dd7c-d556-4940-8c4c-55f49da96e0a · outbound

This paper cites Deepmimic: Example- guided deep reinforcement learning of physics- based character skills.ACM Transactions On Graphics (TOG), 37(4):1–14, 2018.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Deepmimic: Example- guided deep reinforcement learning of physics- based character skills.ACM Transactions On Graphics (TOG), 37(4):1–14, 2018

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.058986Z

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Observation c072c2b3-1ae4-4ad7-bf46-e21e5da79b58 · outbound

This paper cites Amp: Adversarial motion priors for stylized physics-based character control.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Amp: Adversarial motion priors for stylized physics-based character control

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.172977Z

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Observation 7267bc19-edfa-40cb-9961-d359c2b32dd3 · outbound

This paper cites A direct method for trajectory optimization of rigid bodies through contact.The International Journal of Robotics Research, 33(1):69–81, 2014.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation A direct method for trajectory optimization of rigid bodies through contact.The International Journal of Robotics Research, 33(1):69–81, 2014

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.243624Z

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Observation a5b8611f-2b81-4a67-810a-5bdcf99c5de6 · outbound

This paper cites Universal value function approxima- tors.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Universal value function approxima- tors

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.355935Z

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Observation ce434447-877d-453f-bac6-65438f199747 · outbound

This paper cites Manipulation planning with probabilistic roadmaps.The International Journal of Robotics Research, 23(7-8):729–746, 2004.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Manipulation planning with probabilistic roadmaps.The International Journal of Robotics Research, 23(7-8):729–746, 2004

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.432152Z

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Observation 6838d084-bdd8-4a31-b5ee-a8a6659b98c3 · outbound

This paper cites Hybrid RL: Using both offline and online data can make RL efficient.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Hybrid RL: Using both offline and online data can make RL efficient

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.493433Z

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Observation 362c1aab-8d26-4066-af5d-f5d9eb89411a · outbound

This paper cites Bundled gradients through contact via randomized smoothing.IEEE Robotics and Automation Letters, 7 (2):4000–4007, 2022.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Bundled gradients through contact via randomized smoothing.IEEE Robotics and Automation Letters, 7 (2):4000–4007, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.603041Z

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Observation 6ee97c2a-b3f9-4bb6-b078-26e58014264b · outbound

This paper cites Synthesis and stabilization of complex behaviors through online trajectory optimization.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Synthesis and stabilization of complex behaviors through online trajectory optimization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.706472Z

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Observation 392dacd3-a116-4234-a1b4-6f34dbec5488 · outbound

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

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Mu- joco: A physics engine for model-based control

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.790839Z

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Unavailable: canonical work link unavailable.

Observation a3cb649d-21ab-4002-bfe1-671502173463 · outbound

This paper cites De- scribing physics for physical reasoning: Force-based sequential manipulation planning.IEEE Robotics and Automation Letters, 5(4):6209–6216, 2020.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation De- scribing physics for physical reasoning: Force-based sequential manipulation planning.IEEE Robotics and Automation Letters, 5(4):6209–6216, 2020

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.862435Z

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Observation ea14798e-6026-478a-afa8-ac5535aa7077 · outbound

This paper cites Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards.Advances in Neural Information Processing Systems, 32, 2019.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards.Advances in Neural Information Processing Systems, 32, 2019

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:44.933637Z

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Observation d6b0321c-bb3c-4555-8804-7a28c25f61de · outbound

This paper cites Jump-start reinforcement learning.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Jump-start reinforcement learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:45.048452Z

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Observation c699018c-5239-4955-9582-2b8d5e5957f6 · outbound

This paper cites Epistemically-guided forward-backward exploration.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Epistemically-guided forward-backward exploration

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:45.143694Z

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Observation 2eb2bcfe-641d-48c4-8478-e5378ecf1f1a · outbound

This paper cites Kinodynamic motion planning for a team of multirotors transporting a cable-suspended payload in cluttered environ- ments.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Kinodynamic motion planning for a team of multirotors transporting a cable-suspended payload in cluttered environ- ments

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:45.227500Z

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Observation 6de2c14a-cfd9-45b7-87cf-64b0adadafe2 · outbound

This paper cites Numerical optimization.Springer Science, 35(67-68):7, 1999.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Numerical optimization.Springer Science, 35(67-68):7, 1999

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:45.337677Z

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Observation a4fe5061-727e-4a7f-a392-e50c572f0b22 · outbound

This paper cites Uni- versal manipulation policy network for articulated objects.IEEE robotics and automation letters, 7(2): 2447–2454, 2022.

Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation Uni- versal manipulation policy network for articulated objects.IEEE robotics and automation letters, 7(2): 2447–2454, 2022

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T03:18:45.404752Z

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Pith citing papers

Observation ba319c3b-5ffa-44f4-9cac-248638812038 · inbound

Manifold Sampling via Entropy Maximization cites this paper.

Manifold Sampling via Entropy Maximization Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-01T02:17:19.407585Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.