Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:12:51.255232Z
Paper Citation Record · LEDGER
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation
As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2505.10522.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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
Reference resolution
29 of 29 outbound references displayed
- verified exact0
- verified fuzzy22
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 094534ba-2ba0-4eb3-9943-b260e313936f · outbound
This paper cites of Aerospace & Mechanical Engineering University of Southern California Los Angeles, USA xinruiw@usc.edu Yan Jin* Dept.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation of Aerospace & Mechanical Engineering University of Southern California Los Angeles, USA xinruiw@usc.edu Yan Jin* Dept
Reference 1
- Resolution
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- raw_fallback, observed 2026-08-15T21:12:51.656108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2d7d6132-f442-443f-95fa-5770fcb3bc9c · outbound
This paper cites In the domain of robotic manipulation, machine learning has significantly advanced its capabilities in handling objects and executing complex tasks.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation In the domain of robotic manipulation, machine learning has significantly advanced its capabilities in handling objects and executing complex tasks
Reference 2
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- raw_fallback, observed 2026-08-15T21:12:51.643794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ed8b8e33-af71-4294-8536-44b1fa8786bb · outbound
This paper cites Reinforcement learning is one of the solutions, allowing agents to understand and optimize the process through continuous interaction with their environment [14-16].
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Reinforcement learning is one of the solutions, allowing agents to understand and optimize the process through continuous interaction with their environment [14-16]
Reference 3
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- raw_fallback, observed 2026-08-15T21:12:51.631314Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0ce980a8-43fc-4db9-9c3a-ed3c98acf719 · outbound
This paper cites #$%=𝟏&!((.
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:12:51.617590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e06f5607-d1e3-42f4-8770-368cf6e15da7 · outbound
This paper cites (𝑜/,𝑒)−250∆.
Reference 5
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- raw_fallback, observed 2026-08-15T21:12:51.604739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d13aca93-5f51-432d-a75e-71487d9cdafe · outbound
This paper cites The default learning rate of 1e-4, as provided by the baseline, is used for all pre-training phases in the curriculum.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation The default learning rate of 1e-4, as provided by the baseline, is used for all pre-training phases in the curriculum
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:12:51.591622Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a6568886-91ba-4975-9d29-213d09772f2b · outbound
This paper cites Our model significantly improves the top block's fractional success regardless of the conditions and causing learning sequence.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Our model significantly improves the top block's fractional success regardless of the conditions and causing learning sequence
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:12:51.577108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 330ae382-4758-40d5-a5f4-84c114d54daf · outbound
This paper cites an unresolved cited work.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Unresolved cited work
Reference 8
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T21:12:51.563780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6d66dcb8-2603-4a77-9f25-d819b42cdd1a · outbound
This paper cites This task requires the agent to differentiate between blocks and operate with higher precision to avoid collisions while successfully constructing the assembly.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation This task requires the agent to differentiate between blocks and operate with higher precision to avoid collisions while successfully constructing the assembly
Reference 9
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- raw_fallback, observed 2026-08-15T21:12:51.550964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e338ef10-ae63-462a-b96f-0ab4c1e480a4 · outbound
This paper cites CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:12:51.173384Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b75660b-bce3-4331-97c0-1d47a1117793 · outbound
This paper cites Robust Multi-Agent Reinforcement Learning with State Uncertainty.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Robust Multi-Agent Reinforcement Learning with State Uncertainty
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:12:51.177543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b7cf3e55-3223-4b65-9b9f-44f7b84a56bf · outbound
This paper cites Enhancing Efficiency in Collision Avoidance: A Study on Transfer Reinforcement Learning in Autonomous Ships’ Navigation.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Enhancing Efficiency in Collision Avoidance: A Study on Transfer Reinforcement Learning in Autonomous Ships’ Navigation
Reference 12
- Resolution
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- raw_fallback, observed 2026-08-15T21:12:51.538397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1937d59d-7920-46e6-8fa2-72d348f323ab · outbound
This paper cites Simultaneous Thermal-Electrical Cloak and Camouflage via Level-Set-Based Topology Optimization.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Simultaneous Thermal-Electrical Cloak and Camouflage via Level-Set-Based Topology Optimization
Reference 13
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- raw_fallback, observed 2026-08-15T21:12:51.525243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 022e1b39-7d7d-4490-ac4b-1a6acac6ffde · outbound
This paper cites Topology optimization of multimaterial thermoelectric structures.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Topology optimization of multimaterial thermoelectric structures
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:12:51.512523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3c049545-b5f0-48b4-b5a2-f9312a5c5641 · outbound
This paper cites Explainable reinforcement learning: A survey and comparative review.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Explainable reinforcement learning: A survey and comparative review
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:12:51.500165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c4fc7021-c907-4e88-bd2e-befd2b218ab8 · outbound
This paper cites Continuous control with deep reinforcement learning.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Continuous control with deep reinforcement learning
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:12:51.197950Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0055748f-8fbc-4c64-a66d-83de6248a2b5 · outbound
This paper cites Human-level control through deep reinforcement learning.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Human-level control through deep reinforcement learning
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:12:51.487507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 341c739f-c76d-4a1e-a255-0c33c7ecb8ae · outbound
This paper cites Data-driven distributionally robust optimization for vehicle balancing of mobility-on-demand systems.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Data-driven distributionally robust optimization for vehicle balancing of mobility-on-demand systems
Reference 18
- Resolution
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- raw_fallback, observed 2026-08-15T21:12:51.474832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8c4bbfca-3279-446b-995d-69d0141eb296 · outbound
This paper cites Explicable reward design for reinforcement learning agents.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Explicable reward design for reinforcement learning agents
Reference 19
- Resolution
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- raw_fallback, observed 2026-08-15T21:12:51.461966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b0fc9f39-c2a3-4440-adec-00f775f87bc4 · outbound
This paper cites Curriculum learning for reinforcement learning domains: A framework and survey.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Curriculum learning for reinforcement learning domains: A framework and survey
Reference 20
- Resolution
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- raw_fallback, observed 2026-08-15T21:12:51.448542Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f5378b93-24d1-4507-a476-c427814956a1 · outbound
This paper cites Source task creation for curriculum learning.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Source task creation for curriculum learning
Reference 21
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- raw_fallback, observed 2026-08-15T21:12:51.435035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 92ed0bea-4ae2-4f71-bb20-b76a74f9e243 · outbound
This paper cites CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
Reference 22
- Resolution
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- no resolver link, observed 2026-08-15T21:12:51.222480Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dca62682-2a8b-4b20-9a25-3e8cb334d1de · outbound
This paper cites Proximal Curriculum for Reinforcement Learning Agents.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Proximal Curriculum for Reinforcement Learning Agents
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:12:51.226856Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3da57851-e655-400a-a054-2ed60e23416f · outbound
This paper cites Residual learning from demonstration: Adapting dmps for contact-rich manipulation.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Residual learning from demonstration: Adapting dmps for contact-rich manipulation
Reference 24
- Resolution
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- raw_fallback, observed 2026-08-15T21:12:51.420694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 570de16d-d99f-4e97-b08a-452764ee4085 · outbound
This paper cites Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models
Reference 25
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- no resolver link, observed 2026-08-15T21:12:51.234894Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d5ae36a-06b4-4659-8ec6-da68d75f6c7f · outbound
This paper cites $\mathrm {R}^{3} $: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation $\mathrm {R}^{3} $: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Reference 26
- Resolution
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- raw_fallback, observed 2026-08-15T21:12:51.407401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2fe001b1-00d4-4b4b-908c-010d9f8d0625 · outbound
This paper cites A critical review of Knowledge-Based Engineering: An identification of research challenges.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation A critical review of Knowledge-Based Engineering: An identification of research challenges
Reference 27
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- raw_fallback, observed 2026-08-15T21:12:51.394737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bc9a14f5-9119-48c1-a727-126205750094 · outbound
This paper cites Machine learning model towards evaluating data gathering methods in manufacturing and mechanical engineering.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation Machine learning model towards evaluating data gathering methods in manufacturing and mechanical engineering
Reference 33
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- raw_fallback, observed 2026-08-15T21:12:51.381212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8e1058d3-e557-499c-a344-2b7c29f9774b · outbound
This paper cites A recurrent neural network architecture for failure prediction in deep drawing sensory time series data.
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation A recurrent neural network architecture for failure prediction in deep drawing sensory time series data
Reference 35
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- raw_fallback, observed 2026-08-15T21:12:51.367627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.