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Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges

Noémie Jaquier, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, Danica Kragic

TL;DR

This paper defines transfer learning in robotics and unifies its concepts under a robotics-specific taxonomy centered on three core modalities: robot, environment, and task. It surveys successes across environment (e.g., sim-to-real and domain adaptation), task (e.g., meta-learning and skill transfer), and robot transfers (e.g., cross-embodiment imitation), and identifies key challenges such as abstraction level alignment, universal representations, and reliable benchmarking. The authors argue for a holistic framework that includes robust metrics, interpretable universal representations, and scalable benchmarks to measure transfer quality and gaps, including the risks of negative transfer. The work provides a roadmap for advancing embodied AI by bridging high-level semantic transfer with low-level sensorimotor grounding and by leveraging modern models (e.g., robotics transformers, universal representations) in a principled, benchmark-driven research program.

Abstract

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept -- reusing prior knowledge to learn in and from novel situations -- is successfully leveraged by humans to handle novel situations. In recent years, transfer learning has received renewed interest from the community from different perspectives, including imitation learning, domain adaptation, and transfer of experience from simulation to the real world, among others. In this paper, we unify the concept of transfer learning in robotics and provide the first taxonomy of its kind considering the key concepts of robot, task, and environment. Through a review of the promises and challenges in the field, we identify the need of transferring at different abstraction levels, the need of quantifying the transfer gap and the quality of transfer, as well as the dangers of negative transfer. Via this position paper, we hope to channel the effort of the community towards the most significant roadblocks to realize the full potential of transfer learning in robotics.

Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges

TL;DR

This paper defines transfer learning in robotics and unifies its concepts under a robotics-specific taxonomy centered on three core modalities: robot, environment, and task. It surveys successes across environment (e.g., sim-to-real and domain adaptation), task (e.g., meta-learning and skill transfer), and robot transfers (e.g., cross-embodiment imitation), and identifies key challenges such as abstraction level alignment, universal representations, and reliable benchmarking. The authors argue for a holistic framework that includes robust metrics, interpretable universal representations, and scalable benchmarks to measure transfer quality and gaps, including the risks of negative transfer. The work provides a roadmap for advancing embodied AI by bridging high-level semantic transfer with low-level sensorimotor grounding and by leveraging modern models (e.g., robotics transformers, universal representations) in a principled, benchmark-driven research program.

Abstract

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept -- reusing prior knowledge to learn in and from novel situations -- is successfully leveraged by humans to handle novel situations. In recent years, transfer learning has received renewed interest from the community from different perspectives, including imitation learning, domain adaptation, and transfer of experience from simulation to the real world, among others. In this paper, we unify the concept of transfer learning in robotics and provide the first taxonomy of its kind considering the key concepts of robot, task, and environment. Through a review of the promises and challenges in the field, we identify the need of transferring at different abstraction levels, the need of quantifying the transfer gap and the quality of transfer, as well as the dangers of negative transfer. Via this position paper, we hope to channel the effort of the community towards the most significant roadblocks to realize the full potential of transfer learning in robotics.
Paper Structure (17 sections, 1 equation, 7 figures)

This paper contains 17 sections, 1 equation, 7 figures.

Table of Contents

  1. TheRiseofTransferLearninginRobotics
  2. TransferLearningTaxonomies:FromMachineLearningtoRobotics
  3. TaxonomyofTransferLearninginMachineLearning
  4. TaxonomyofTransferLearninginRobotics
  5. SuccessesofTransferLearninginRobotics
  6. EnvironmentTransfer
  7. TaskTransfer
  8. RobotTransfer
  9. ChallengesandPromisingResearchDirections
  10. AbstractionLevelsinRobotics
  11. RoboticsTransformers
  12. UniversalRepresentations
  13. Interpretability
  14. BenchmarkingandSimulation
  15. MetricsforTransferLearninginRobotics
  16. ...and 2 more sections

Figures (7)

  • Figure 1: Conceptoftransferlearninginrobotics.Theexperienceofarobotperformingaspecifictaskinaspecificenvironmentisleveragedtoimprovethelearningofarelatedtaskbyanotherrobotinarelatedcontext.Transfercanoccuracrossembodiments(yellowarrows),acrosstasks(purplearrows),and/oracrossenvironments(bluearrows).Itisimportanttonotethatsuccessfultransferrequirescommonalitiesbetweenthesourceandtargetrobots,tasks,andenvironments.Forinstance,ahumanoidrobotlearningtokickaballwillmostlikelynotbenefitfromtheexperienceofadual-armmanipulatorsystemsmanipulatingaboxandvice-versa.
  • Figure 2: Percentageofpapersincludingwordsfallingundertransferlearninginroboticsumbrellaovertheyearsforthetwobiggestroboticsconferences.Theresultsarebasedonasystematicsearchthroughthecontentof44067papers,excludingtheirreferences.Top:Percentageperconference.Bottom:Percentageperkeywords.Keywordsrelatedtotransferacrossembodiments,tasks,andenvironmentsaredepictedinvariationsofyellow,purple,andblue.Knowledgetransferisclassifiedindependentlyandthusisdepictedinblack.
  • Figure 3: ExampleoftransferlearningontheOffice31dataset saenko2010adapting.Transfercanoccur(1)betweenthetwolabelsets(tasks$\task_1$and$\task_2$),(2)betweenthetwosourcesusedtoobtaintheimages(domains$\domain_1$and$\domain_2$),and(3)betweenbothtasksanddomains.Thedifferenttransferlearninginstancesareillustratedwithblackarrows.
  • Figure 4: Hierarchicaltaxonomyoftransferlearninginthecontextofmachinelearning.Domaintransferoccurswhenthesourceandtargetdomaindifferandischaracterizedbyadifferenceinthefeaturespaceand/orinthemarginaldistribution.Tasktransferoccurswhenthesourceandtargettaskdifferandischaracterizedbyadifferenceinthelabelspaceand/orinthepredictivefunction.Dual-modetransferoccurswhenboththesourceandtargetdomainandtaskdiffer.
  • Figure 5: Hierarchicaltaxonomyoftransferlearninginthecontextofrobotics.Robot,environment,andtasktransferoccurwhenthesourceandtargetrobot,environment,andtaskdiffer,respectively.Dual-modeandtriple-modetransferoccurwhentwoofthesemodalitiesandthethreeofthemdiffer,respectively.
  • ...and 2 more figures

Theorems & Definitions (2)

  • Definition 2.1: TransferLearninginMachineLearning
  • Definition 2.2: TransferLearninginRobotics