mlsanityData to 13 Oct 2026

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

arXiv 2311.18044 ↗First posted 29 Nov 2023cs.RO cs.LGcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 13 Oct 2026). Builds on 83 of its 170 references that are in the map. First posted on arXiv 29 Nov 2023.

Builds on

83 of 170 references.

References · 83

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38 more
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50 most cited of 83 All 83 (JSON)

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 6; 2025: 7; 2026: 4.

17 papers.

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  1. 2024DrEureka: Language Model Guided Sim-To-Real Transfercited by 79
  2. 2025Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trendscited by 17
  3. 2025Audio-Guided Dynamic Modality Fusion with Stereo-Aware Attention for Audio-Visual Navigationcited by 13
  4. 2024Effective Tuning Strategies for Generalist Robot Manipulation Policiescited by 10
  5. 2026What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?cited by 5
  6. 2024A Bayesian Model Selection Criterion for Selecting Pretraining Checkpointscited by 4
  7. 2024QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversitycited by 2
  8. 2025Information-Theoretic Graph Fusion with Vision-Language-Action Model for Policy Reasoning and Dual Robotic Controlcited by 2
  9. 2025EmbodiSwap for Zero-Shot Robot Imitation Learningcited by 2
  10. 2025Zero-Shot Policy Transfer in Reinforcement Learning using Buckingham's Pi Theoremcited by 1
  11. 2026Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learningcited by 1
  12. 2024Automating Transfer of Robot Task Plans using Functorial Data Migrationscited by 0
5 more
  1. 2024Towards Adapting Reinforcement Learning Agents to New Tasks: Insights from Q-Valuescited by 0
  2. 2025Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approachcited by 0
  3. 2025Graph-Fused Vision-Language-Action for Policy Reasoning in Multi-Arm Robotic Manipulationcited by 0
  4. 2026Generalizable Audio-Visual Navigation via Binaural Difference Attention and Action Transition Predictioncited by 0
  5. 2026Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contactcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Are Foundation Models the Route to Full-Stack Transfer in Robotics? ↗
  2. 2021Imitation Learning: Progress, Taxonomies and Challenges
  3. 2018An Algorithmic Perspective on Imitation Learning
  4. 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
  5. 2020Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
  6. 2025Robot Learning: A Tutorial
  7. 2022Learning and Retrieval from Prior Data for Skill-based Imitation Learning
  8. 2026Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations ↗