SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching
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| Format: | Preprint |
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2025
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| author | Sumner, Emily Gopinath, Deepak E. Dees, Laporsha Gomez, Patricio Reyes Cui, Xiongyi Silva, Andrew Costa, Jean Morgan, Allison Schrum, Mariah Chen, Tiffany L. Balachandran, Avinash Rosman, Guy |
| author_facet | Sumner, Emily Gopinath, Deepak E. Dees, Laporsha Gomez, Patricio Reyes Cui, Xiongyi Silva, Andrew Costa, Jean Morgan, Allison Schrum, Mariah Chen, Tiffany L. Balachandran, Avinash Rosman, Guy |
| contents | Curated datasets are essential for training and evaluating AI approaches, but are often lacking in domains where language and physical action are deeply intertwined. In particular, few datasets capture how people acquire embodied skills through verbal instruction over time. To address this gap, we introduce SimCoachCorpus: a unique dataset of race car simulator driving that allows for the investigation of rich interactive phenomena during guided and unguided motor skill acquisition. In this dataset, 29 humans were asked to drive in a simulator around a race track for approximately ninety minutes. Fifteen participants were given personalized one-on-one instruction from a professional performance driving coach, and 14 participants drove without coaching. \name\ includes embodied features such as vehicle state and inputs, map (track boundaries and raceline), and cone landmarks. These are synchronized with concurrent verbal coaching from a professional coach and additional feedback at the end of each lap. We further provide annotations of coaching categories for each concurrent feedback utterance, ratings on students' compliance with coaching advice, and self-reported cognitive load and emotional state of participants (gathered from surveys during the study). The dataset includes over 20,000 concurrent feedback utterances, over 400 terminal feedback utterances, and over 40 hours of vehicle driving data. Our naturalistic dataset can be used for investigating motor learning dynamics, exploring linguistic phenomena, and training computational models of teaching. We demonstrate applications of this dataset for in-context learning, imitation learning, and topic modeling. The dataset introduced in this work will be released publicly upon publication of the peer-reviewed version of this paper. Researchers interested in early access may register at https://tinyurl.com/SimCoachCorpusForm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14548 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching Sumner, Emily Gopinath, Deepak E. Dees, Laporsha Gomez, Patricio Reyes Cui, Xiongyi Silva, Andrew Costa, Jean Morgan, Allison Schrum, Mariah Chen, Tiffany L. Balachandran, Avinash Rosman, Guy Robotics Human-Computer Interaction Curated datasets are essential for training and evaluating AI approaches, but are often lacking in domains where language and physical action are deeply intertwined. In particular, few datasets capture how people acquire embodied skills through verbal instruction over time. To address this gap, we introduce SimCoachCorpus: a unique dataset of race car simulator driving that allows for the investigation of rich interactive phenomena during guided and unguided motor skill acquisition. In this dataset, 29 humans were asked to drive in a simulator around a race track for approximately ninety minutes. Fifteen participants were given personalized one-on-one instruction from a professional performance driving coach, and 14 participants drove without coaching. \name\ includes embodied features such as vehicle state and inputs, map (track boundaries and raceline), and cone landmarks. These are synchronized with concurrent verbal coaching from a professional coach and additional feedback at the end of each lap. We further provide annotations of coaching categories for each concurrent feedback utterance, ratings on students' compliance with coaching advice, and self-reported cognitive load and emotional state of participants (gathered from surveys during the study). The dataset includes over 20,000 concurrent feedback utterances, over 400 terminal feedback utterances, and over 40 hours of vehicle driving data. Our naturalistic dataset can be used for investigating motor learning dynamics, exploring linguistic phenomena, and training computational models of teaching. We demonstrate applications of this dataset for in-context learning, imitation learning, and topic modeling. The dataset introduced in this work will be released publicly upon publication of the peer-reviewed version of this paper. Researchers interested in early access may register at https://tinyurl.com/SimCoachCorpusForm. |
| title | SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.14548 |