Haptic training: Which types facilitate (re)learning of which motor task and for whom Answers by a review

Ekin Basalp, Peter Wolf, Laura Marchal-Crespo

Research output: Contribution to journalReview articleScientificpeer-review

4 Citations (Scopus)
35 Downloads (Pure)


The use of robots has attracted researchers to design numerous haptic training methods to support motor learning. However, investigations of new methods yielded inconclusive results regarding their effectiveness to enhance learning due to the diversity of tasks, haptic designs, participants skill level, and study protocols. In this review, we developed a taxonomy to identify generalizable findings out of publications on haptic training. In the taxonomy, we grouped the results of studies on healthy learners based on participants skill level and tasks characteristics. Our inspection of included studies revealed that: i) Performance-enhancing haptic methods were beneficial for novices, ii) Training with haptics was as effective as training with other feedback modalities, and iii) Performance-enhancing and performance-degrading haptic methods were useful for the learning of temporal and spatial aspects, respectively. We also observed that these findings are in line with results from robot-aided neurorehabilitation studies on patients. Our review suggests that haptic training can be effective to foster learning, especially when the information cannot be provided with other feedback modalities. We believe the findings from the taxonomy constitute a general guide, which can assist researchers when designing studies to investigate the effectiveness of haptics on learning different tasks.

Original languageEnglish
Pages (from-to)722-739
JournalIEEE Transactions on Haptics
Issue number4
Publication statusPublished - 2021

Bibliographical note

Accepted Author Manuscript


  • augmented haptic feedback
  • Haptic interfaces
  • motor learning and neurorehabilitation
  • motor task classification
  • Robot sensing systems
  • robot-assisted training
  • Robots
  • skill level
  • Task analysis
  • Taxonomy
  • taxonomy
  • Training
  • Visualization


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