Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:22:55.700213Z
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2608.09138.
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 2026-08-14T04:22:55.700213Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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
40 of 40 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation f32deceb-dae3-42db-b0ea-817fda4a79c6 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Reference 1
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Observation 3bda84c4-1899-4867-a854-6c8f027a7cb7 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Alvinn: An autonomous land ve- hicle in a neural network,
Reference 2
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Observation 0ae4ce74-c564-41fb-987e-ffb79098112c · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Is imitation learning the route to humanoid robots?
Reference 3
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Observation 4845da9e-db0f-42fc-acee-3f371cd3d515 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning The dlr bimanual haptic device with optimized workspace,
Reference 4
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Observation 181028f8-d65f-41c3-aac5-825a143d51a4 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning A low cost modular actuator for dynamic robots,
Reference 5
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Observation be838120-fae1-4598-9032-087fbfe690b5 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Nimbro avatar: Interactive immersive telepresence with force-feedback telemanipulation,
Reference 6
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Observation 923afce6-70b5-483b-b0ff-c23316fd896d · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Unresolved cited work
Reference 7
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Observation bdb5d2a0-7c24-4fee-8054-676f6564fc6d · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Attention Is All You Need
Reference 8
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Observation f75968bd-c54b-4a06-8b9a-a0967e30f948 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
Reference 9
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Observation dd037313-7bed-4859-9c1c-3a3728db3300 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Implicit Behavioral Cloning
Reference 10
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Observation 4819dbdf-b972-40a4-aa30-df7071c58951 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Bc-z: Zero-shot task generalization with robotic imitation learning,
Reference 11
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Observation 9b14d44a-0762-463e-8791-75232f2e624e · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Should i run offline reinforcement learning or behavioral cloning?
Reference 12
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Observation c4e25021-4194-41a2-b5d4-be7ee02a929c · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Q-learning,
Reference 13
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Observation 400c0f6d-8e17-4701-9536-71233a7268d2 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Unresolved cited work
Reference 14
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Observation cd42cb95-2f36-4525-b783-e01b87e6001b · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Policy gradient methods for reinforcement learning with function approximation,
Reference 15
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Observation 2b101cfe-f947-4a1c-a9ab-7960454721f6 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Actor-critic algo- rithms,
Reference 16
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Observation 141311c1-189d-4163-aaab-f2c497978507 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Continuous control with deep reinforcement learning
Reference 17
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Observation d350b227-c2ad-43d9-812b-66a4fe976aee · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,
Reference 18
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Observation 30924381-8641-4304-a10a-2fe84585cea0 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Learning dexterous in-hand manipulation,
Reference 19
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Observation 9136e27b-77d6-47b7-8020-337c780ae643 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning How to train your robot with deep reinforcement learning: Lessons we have learned,
Reference 20
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Observation 380fbe95-2ba3-4aaa-9cff-6820c9559a95 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Never stop learning: The effectiveness of fine-tuning in robotic reinforcement learning,
Reference 21
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Observation 40f597be-7d24-443f-8c2c-dd32cb01e8e7 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Robot fine-tuning made easy: Pre-training rewards and policies for autonomous real-world rein- forcement learning,
Reference 22
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Observation 581add2b-f3c0-4348-85d8-490464014a48 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Learning to play table tennis from scratch using muscular robots,
Reference 23
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Observation 9861f1b7-e9f4-4f27-965a-29f6e19a9686 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Achieving human level competitive robot table tennis,
Reference 24
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Observation b19a6da8-a37d-4abe-a27b-8d97052a23c0 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Knotting manipulation of a flexible rope using a high-speed multifingered hand and high-speed visual and tactile sensory feedback,
Reference 25
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Observation fd837fd1-75ff-40d6-9861-cdddd6d42335 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Motion planning for dynamic knotting of a flexible rope with a high-speed robot arm,
Reference 26
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Observation 02a868d3-f1b2-4811-9786-42f37b01fd36 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Dynamic high-speed knotting of a rope by a manipulator,
Reference 27
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Observation 24e8b64b-fbc0-461d-a834-ab34166a1d40 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables
Reference 28
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Observation 35ade4ab-7c5b-47b8-9145-48ba0b8fe03e · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Tossingbot: Learning to throw arbitrary objects with residual physics,
Reference 29
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Observation 143e25eb-cc38-4383-a954-a66110e3385b · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Flingbot: The unreasonable effec- tiveness of dynamic manipulation for cloth unfolding,
Reference 30
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Observation 9a7ace48-4f88-46d9-9720-f8a6dcf6e731 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Par4: Very high speed parallel robot for pick-and-place,
Reference 31
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Observation 2e6ec1e4-1565-4634-8564-72004f6e4dc0 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Toward fast and optimal robotic pick-and-place on a moving con- veyor,
Reference 32
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Observation f3b25567-91e3-4a3b-8331-2a77d30bf087 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Critically fast pick-and- place with suction cups,
Reference 33
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Observation fcee40a1-f6f1-45f9-90c9-a2e29ec906ed · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Rainbow: Combining improvements in deep reinforcement learning,
Reference 34
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Observation fd78f773-2e4e-4eb2-9fcf-ee968042003f · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Auto-Encoding Variational Bayes
Reference 35
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Observation a1c43d05-a67c-4755-a7a9-a940f6160235 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Learning structured output representation using deep conditional genera- tive models,
Reference 36
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Observation 5fa93002-ca77-4194-981f-9eb7855e514d · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Deep reinforce- ment learning with double q-learning,
Reference 37
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Observation fc61cf26-22eb-40bf-8e1e-afc72e10fb7c · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Prioritized Experience Replay
Reference 38
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Observation 1bf4ba4b-b11e-4a4b-ac4d-f63a7fb66272 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning Dueling network architectures for deep reinforcement learning,
Reference 39
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Observation 5de7b227-14c4-49f6-825e-7b106d4e9983 · outbound
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning A dis- tributional perspective on reinforcement learning,
Reference 40
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No inbound Pith citation observations are available.