Paper Citation Record · LEDGER

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning

As of 17 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 2 inbound Pith citation observations for arXiv:2411.14519.

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

pith.paper-citation-record.v1
2411.14519 v2

Coverage vector

measured 74 of 74 reference resolution

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measured 76 of 76 standing notices

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T15:42:41.519963Z

Reference resolution

74 of 74 outbound references displayed

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External citation measurements

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Outbound references

Observation e76a8e97-79ba-468e-836e-5a6112fd5acd · outbound

This paper cites GPT-4 Technical Report.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning GPT-4 Technical Report

Reference 1

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Observation ca6f0531-3904-481b-bae6-6be0fe576d2f · outbound

This paper cites Learning dexterous in-hand manipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Learning dexterous in-hand manipulation

Reference 2

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Observation 15939cf6-db5c-4240-b959-1c74bbcaecb3 · outbound

This paper cites Affordances from human videos as a versatile representation for robotics.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Affordances from human videos as a versatile representation for robotics

Reference 3

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Observation 5bdf3e32-60c4-4a04-a5ab-4af93540cb09 · outbound

This paper cites Towards generalizable zero-shot manipu- lation via translating human interaction plans.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Towards generalizable zero-shot manipu- lation via translating human interaction plans

Reference 4

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Observation 71091a9c-b5e0-48c2-baad-708eb421607e · outbound

This paper cites Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation

Reference 5

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Observation b7bb51ca-a1d9-48a8-a736-59b47afd6daf · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 6

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Observation 3b9210df-3aea-43ea-8033-4a04b708c986 · outbound

This paper cites Robocat: A self-improving generalist agent for robotic manipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Robocat: A self-improving generalist agent for robotic manipulation

Reference 7

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Observation 3e3e7b1f-c975-4fad-acdb-d94b5977f815 · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning RT-1: Robotics Transformer for Real-World Control at Scale

Reference 8

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Observation 91d73b72-41e1-486c-8197-770a754d7160 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 9

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Observation 9ade7395-c0aa-4b4e-92b6-87b1d25ee74c · outbound

This paper cites Language Models are Few-Shot Learners.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Language Models are Few-Shot Learners

Reference 10

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Observation 017cfc46-a674-4191-af92-771c90aab5c5 · outbound

This paper cites AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

Reference 11

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Observation b55da891-a4cc-42cf-aca1-1742f54ca69a · outbound

This paper cites A Survey on Mixture of Experts in Large Language Models.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning A Survey on Mixture of Experts in Large Language Models

Reference 12

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Observation dd911fe8-8f17-4549-bdff-bb5dd559e985 · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning An empirical study of training self-supervised vision transformers

Reference 13

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Observation 350a58e4-105a-4ddd-89c1-41b467d27d75 · outbound

This paper cites Mod-squad: Designing mixtures of experts as modular multi- task learners.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Mod-squad: Designing mixtures of experts as modular multi- task learners

Reference 14

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Observation 29d77a9c-7217-4cd3-a596-6354ee35bc86 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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Observation f2f3e09d-885c-4fe9-aa96-fef6bb27d538 · outbound

This paper cites Scaling cross-embodied learning: One policy for manipulation, navigation, locomotion and aviation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Scaling cross-embodied learning: One policy for manipulation, navigation, locomotion and aviation

Reference 16

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Observation ed1538de-5a0e-47c5-a9c9-a31ac4204de6 · outbound

This paper cites Video language planning.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Video language planning

Reference 17

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Observation 13f7223b-13d4-4a37-8814-8388dccec121 · outbound

This paper cites Learning universal policies via text-guided video generation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Learning universal policies via text-guided video generation

Reference 18

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Observation 908140ee-8f1e-45a6-b400-86965f7ce07d · outbound

This paper cites Rh20t: A robotic dataset for learning diverse skills in one-shot.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Rh20t: A robotic dataset for learning diverse skills in one-shot

Reference 19

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Observation 4575b838-ff9e-4ae7-9a2e-46af16e8ca85 · outbound

This paper cites Flip: Flow-centric generative planning as general-purpose manipulation world model.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Flip: Flow-centric generative planning as general-purpose manipulation world model

Reference 20

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Observation 4929f139-7fd1-45ae-bbb5-14f945a57ca2 · outbound

This paper cites Rt-trajectory: Robotic task generalization via hindsight trajectory sketches.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Rt-trajectory: Robotic task generalization via hindsight trajectory sketches

Reference 21

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Observation d65d958c-a7c4-41d4-90e6-6b44a16244c3 · outbound

This paper cites Instruction-driven history-aware policies for robotic manipu- lations.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Instruction-driven history-aware policies for robotic manipu- lations

Reference 22

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Observation 0772ebed-68f0-4b49-9ad8-105aa54ce0ed · outbound

This paper cites Masked autoencoders are scalable vision learners.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Masked autoencoders are scalable vision learners

Reference 23

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This paper cites Tutel: Adaptive mixture-of-experts at scale.Proceedings of Machine Learning and Systems, 5:269–287, 2023.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Tutel: Adaptive mixture-of-experts at scale.Proceedings of Machine Learning and Systems, 5:269–287, 2023

Reference 24

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This paper cites Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers

Reference 25

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This paper cites Rlbench: The robot learning benchmark & learning environment.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Rlbench: The robot learning benchmark & learning environment

Reference 26

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Observation 5da094b6-a38d-43c3-b6bc-eea934a37144 · outbound

This paper cites Bc-z: Zero-shot task generalization with robotic imitation learning.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Bc-z: Zero-shot task generalization with robotic imitation learning

Reference 27

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Observation 4eeb20b3-39b9-4095-b6fd-6d45f78c9839 · outbound

This paper cites Mixtral of Experts.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Mixtral of Experts

Reference 28

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Observation e8b328fd-d7be-4b43-87ae-1c0d28e545de · outbound

This paper cites VIMA: General Robot Manipulation with Multimodal Prompts.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning VIMA: General Robot Manipulation with Multimodal Prompts

Reference 29

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Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Scaling Laws for Neural Language Models

Reference 30

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Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning CoTracker: It is Better to Track Together

Reference 31

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This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 32

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Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning OpenVLA: An Open-Source Vision-Language-Action Model

Reference 33

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This paper cites Learning to act from actionless videos through dense correspondences.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Learning to act from actionless videos through dense correspondences

Reference 34

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Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Low-cost robot arm

Reference 35

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Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 36

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This paper cites End-to-end training of deep visuomotor policies.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning End-to-end training of deep visuomotor policies

Reference 37

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This paper cites Vision-language foundation models as effective robot imitators.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Vision-language foundation models as effective robot imitators

Reference 38

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Observation 03a4c7d7-8f6d-4071-884a-5169d6981318 · outbound

This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 39

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Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 40

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This paper cites Libero: Benchmarking knowledge transfer for lifelong robot learning.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Libero: Benchmarking knowledge transfer for lifelong robot learning

Reference 41

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Observation 107cc837-3443-44dd-a0a9-c0f414e3b8ec · outbound

This paper cites Moka: Open-vocabulary robotic manipulation through mark- based visual prompting.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Moka: Open-vocabulary robotic manipulation through mark- based visual prompting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.752621Z

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Observation 9c8179ae-bed3-45ce-a015-f4aaf64e0c10 · outbound

This paper cites Unified-io: A unified model for vision, language, and multi-modal tasks.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Unified-io: A unified model for vision, language, and multi-modal tasks

Reference 43

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Observation ad8a6fd2-89f4-4dd4-bd29-5a0e2479855f · outbound

This paper cites Learning latent plans from play.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Learning latent plans from play

Reference 44

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Observation 2da8d602-26fb-4665-a995-0c917dd94d2c · outbound

This paper cites Interactive language: Talking to robots in real time.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Interactive language: Talking to robots in real time

Reference 45

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Observation 786a3219-5367-4820-84ac-2271d019952d · outbound

This paper cites RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation

Reference 46

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This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 47

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This paper cites From Sparse to Soft Mixtures of Experts.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning From Sparse to Soft Mixtures of Experts

Reference 48

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Observation 18615cb2-b253-4354-8ea7-4f6546e4c3b9 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Learning transferable visual models from natural language supervision

Reference 49

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Observation 8e8e28e1-3b04-4fe7-ad42-ca36c179fd83 · outbound

This paper cites Vision-based multi-task manipu- lation for inexpensive robots using end-to-end learning from demonstration.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Vision-based multi-task manipu- lation for inexpensive robots using end-to-end learning from demonstration

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.717035Z

Source-reported events for the cited work

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Observation b8c7fe4a-02a7-421f-af6b-adc69c51320e · outbound

This paper cites A Generalist Agent.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning A Generalist Agent

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.346190Z

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Observation 4a758242-71be-4296-9e3a-b7445456bd39 · outbound

This paper cites Reinforcement learning with action-free pre-training from videos.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Reinforcement learning with action-free pre-training from videos

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.709726Z

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Observation 29718719-2f1d-4d7c-a649-d830b827faa0 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.351160Z

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Observation 49bdfe6b-7074-4655-9cf7-dec7ca66ad2e · outbound

This paper cites Cliport: What and where pathways for robotic manipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Cliport: What and where pathways for robotic manipulation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.702531Z

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Observation 6ddd0013-ed09-4b6a-9bd3-6d95d7a71eab · outbound

This paper cites Open-world object manipulation using pre-trained vision-language models.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Open-world object manipulation using pre-trained vision-language models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.695017Z

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Observation ff5dad4e-20cd-4868-ae96-6d80253af783 · outbound

This paper cites Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.687919Z

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Observation 1f7857a6-f220-42ce-868f-f986adea060f · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Octo: An Open-Source Generalist Robot Policy

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.361180Z

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Observation 1d520ebe-34e9-4114-8a52-3300ae1b4622 · outbound

This paper cites Video- mae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Video- mae: Masked autoencoders are data-efficient learners for self-supervised video pre-training

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.680781Z

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Observation 6a0224d0-576e-45e5-a103-1d1492cc9a96 · outbound

This paper cites Robotap: Tracking arbitrary points for few-shot visual imitation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Robotap: Tracking arbitrary points for few-shot visual imitation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.673186Z

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Observation 3f9a1c5f-def7-4045-9bd8-56781935d973 · outbound

This paper cites Bridgedata v2: A dataset for robot learning at scale.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Bridgedata v2: A dataset for robot learning at scale

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.665196Z

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Observation 18c0248c-6f85-478c-b111-cb80e8f62596 · outbound

This paper cites Mim- icplay: Long-horizon imitation learning by watching human play.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Mim- icplay: Long-horizon imitation learning by watching human play

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.657569Z

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Observation 4a62146a-4dde-4a37-9ea5-2a5f07ba8b5f · outbound

This paper cites Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.373667Z

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Observation cf5ec680-3bc5-489d-b3c8-23730cf806b1 · outbound

This paper cites Any-point Trajectory Modeling for Policy Learning.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Any-point Trajectory Modeling for Policy Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.376352Z

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Observation 2c038356-96b6-4ba5-85cd-b0fa82db3398 · outbound

This paper cites RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.379285Z

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Observation d340b5c4-335e-4ba8-b0b0-1e54c905a7cd · outbound

This paper cites Flow as the Cross-Domain Manipulation Interface.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Flow as the Cross-Domain Manipulation Interface

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.381859Z

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Observation 3390c0f5-6982-4c83-a3d0-22a2511b51c6 · outbound

This paper cites Transferring Foundation Models for Generalizable Robotic Manipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Transferring Foundation Models for Generalizable Robotic Manipulation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.384630Z

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Observation 8dc95691-0a73-41f8-8627-1375a5f2f967 · outbound

This paper cites Spatiotemporal Predictive Pre-training for Robotic Motor Control.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Spatiotemporal Predictive Pre-training for Robotic Motor Control

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.387288Z

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Observation 1ce67939-179f-4d17-bbe2-0cec97035ef9 · outbound

This paper cites Transferring foundation models for generalizable robotic ma- nipulation.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Transferring foundation models for generalizable robotic ma- nipulation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.649925Z

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Observation 83a7eecd-c47d-4b46-9074-ac460f30f60a · outbound

This paper cites Latent Action Pretraining from Videos.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Latent Action Pretraining from Videos

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.392475Z

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Observation f939e7f1-44fb-459f-9665-7091b9354ca4 · outbound

This paper cites General Flow as Foundation Affordance for Scalable Robot Learning.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning General Flow as Foundation Affordance for Scalable Robot Learning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.395456Z

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Observation b94d0da4-ae9f-4bcb-91c0-3e6387d1facd · outbound

This paper cites Uni- perceiver-moe: Learning sparse generalist models with condi- tional moes.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Uni- perceiver-moe: Learning sparse generalist models with condi- tional moes

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.641298Z

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Observation a9ebe078-b94c-4fed-8946-ee9cd25adfd3 · outbound

This paper cites Uni-perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Uni-perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.633443Z

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Observation 16f898de-c26a-423f-a128-efe41bd7c247 · outbound

This paper cites Learn- ing generalizable manipulation policies with object-centric 3d representations.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning Learn- ing generalizable manipulation policies with object-centric 3d representations

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:24:14.625683Z

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Observation 127bf289-abab-410c-9b5f-7873f8afe9cc · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T15:24:14.405481Z

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Pith citing papers

Observation e1d8b0a8-5381-4bc9-bd51-22bf5b3618a4 · inbound

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers cites this paper.

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:07:25.504405Z

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Observation 48604d1a-4b26-428d-809a-6c534db4262f · inbound

DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge cites this paper.

DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-16T15:42:41.521798Z

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