Lio -- A Personal Robot Assistant for Human-Robot Interaction and Care Applications
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Abstract (arXiv)
Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis. Lio is intrinsically safe by having full coverage in soft artificial-leather material as well as having collision detection, limited speed and forces. Furthermore, the robot has a compliant motion controller. A combination of visual, audio, laser, ultrasound and mechanical sensors are used for safe navigation and environment understanding. The ROS-enabled setup allows researchers to access raw sensor data as well as have direct control of the robot. The friendly appearance of Lio has resulted in the robot being well accepted by health care staff and patients. Fully autonomous operation is made possible by a flexible decision engine, autonomous navigation and automatic recharging. Combined with time-scheduled task triggers, this allows Lio to operate throughout the day, with a battery life of up to 8 hours and recharging during idle times. A combination of powerful on-board computing units provides enough processing power to deploy artificial intelligence and deep learning-based solutions on-board the robot without the need to send any sensitive data to cloud services, guaranteeing compliance with privacy requirements. During the COVID-19 pandemic, Lio was rapidly adjusted to perform additional functionality like disinfection and remote elevated body temperature detection. It complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jun 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 0; 2025: 0; 2026: 2.
6 papers.
- 2023Grasp-Anything: Large-scale Grasp Dataset from Foundation Modelscited by 33
- 2026Dual-Anchoring: Addressing State Drift in Vision-Language Navigationcited by 7
- 2023Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventionscited by 3
- 2021Internet-of-Things Devices and Assistive Technologies for Healthcare: Applications, Challenges, and Opportunitiescited by 2
- 2021Controllable Response Generation for Assistive Use-casescited by 0
- 2026RoboSubtaskNet: Temporal Sub-task Segmentation for Human-to-Robot Skill Transfer in Real-World Environmentscited by 0
- 2026Dual-Anchoring: Addressing State Drift in Vision-Language Navigation19 Apr 2026
- 2026RoboSubtaskNet: Temporal Sub-task Segmentation for Human-to-Robot Skill Transfer in Real-World Environments10 Feb 2026
- 2023Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions20 Sep 2023
- 2023Grasp-Anything: Large-scale Grasp Dataset from Foundation Models18 Sep 2023
- 2021Controllable Response Generation for Assistive Use-cases4 Dec 2021
- 2021Internet-of-Things Devices and Assistive Technologies for Healthcare: Applications, Challenges, and Opportunities11 Jul 2021