HARMONIC: A Multimodal Dataset of Assistive Human-Robot Collaboration
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Abstract (arXiv)
We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) data set. This is a large multimodal data set of human interactions with a robotic arm in a shared autonomy setting designed to imitate assistive eating. The data set provides human, robot, and environmental data views of twenty-four different people engaged in an assistive eating task with a 6 degree-of-freedom (DOF) robot arm. From each participant, we recorded video of both eyes, egocentric video from a head-mounted camera, joystick commands, electromyography from the forearm used to operate the joystick, third person stereo video, and the joint positions of the 6 DOF robot arm. Also included are several features that come as a direct result of these recordings, such as eye gaze projected onto the egocentric video, body pose, hand pose, and facial keypoints. These data streams were collected specifically because they have been shown to be closely related to human mental states and intention. This data set could be of interest to researchers studying intention prediction, human mental state modeling, and shared autonomy. Data streams are provided in a variety of formats such as video and human-readable CSV and YAML files.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jul 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1; 2025: 3; 2026: 1.
10 papers.
- 2021Core Challenges in Embodied Vision-Language Planningcited by 30
- 2021LILA: Language-Informed Latent Actionscited by 27
- 2023Towards Modeling and Influencing the Dynamics of Human Learningcited by 11
- 2025Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interactioncited by 4
- 2025LAMS: LLM-Driven Automatic Mode Switching for Assistive Teleoperationcited by 2
- 2025Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Modelscited by 1
- 2022Generalizing Multimodal Variational Methods to Setscited by 0
- 2023Core Challenges in Embodied Vision-Language Planningcited by 0
- 2024Online Behavior Modification for Expressive User Control of RL-Trained Robotscited by 0
- 2026Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transportcited by 0
- 2026Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport21 Sep 2026
- 2025Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models17 Jun 2025
- 2025Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction7 Mar 2025
- 2025LAMS: LLM-Driven Automatic Mode Switching for Assistive Teleoperation15 Jan 2025
- 2024Online Behavior Modification for Expressive User Control of RL-Trained Robots15 Aug 2024
- 2023Core Challenges in Embodied Vision-Language Planning5 Apr 2023
- 2023Towards Modeling and Influencing the Dynamics of Human Learning2 Jan 2023
- 2022Generalizing Multimodal Variational Methods to Sets19 Dec 2022
- 2021LILA: Language-Informed Latent Actions5 Nov 2021
- 2021Core Challenges in Embodied Vision-Language Planning26 Jun 2021