# Human-Aware Robot Behaviour in Self-Driving Labs

arXiv: https://arxiv.org/abs/2603.08420 · HTML: https://mlsanity.com/paper/2603.08420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization](https://mlsanity.com/paper/2401.06949/) (2024)
- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation](https://mlsanity.com/paper/2405.17418/) (2024)
- [OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot Social Navigation](https://mlsanity.com/paper/2409.13675/) (2024)
- [ARChemist: Autonomous Robotic Chemistry System Architecture](https://mlsanity.com/paper/2204.13571/) (2022)
- [SOLIS: Autonomous Solubility Screening using Deep Neural Networks](https://mlsanity.com/paper/2203.10970/) (2022)
- [PREVENT: Proactive Risk Evaluation and Vigilant Execution of Tasks for Mobile Robotic Chemists using Multi-Modal Behavior Trees](https://mlsanity.com/paper/2510.21438/) (2025)
- [Multimodal Behaviour Trees for Robotic Laboratory Task Automation](https://mlsanity.com/paper/2506.20399/) (2025)
- [Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration](https://mlsanity.com/paper/2511.08732/) (2025)

## Abstract (arXiv)

Self-driving laboratories (SDLs) are rapidly transforming research in chemistry and materials science to accelerate new discoveries. Mobile robot chemists (MRCs) play a pivotal role by autonomously navigating the lab to transport samples, effectively connecting synthesis, analysis, and characterisation equipment. The instruments within an SDL are typically designed or retrofitted to be accessed by both human and robotic chemists, ensuring operational flexibility and integration between manual and automated workflows. In many scenarios, human and robotic chemists may need to use the same equipment simultaneously. Currently, MRCs rely on simple LiDAR-based obstruction detection, which forces the robot to passively wait if a human is present. This lack of situational awareness leads to unnecessary delays and inefficient coordination in time-critical automated workflows in human-robot shared labs. To address this, we present an initial study of an embodied, AI-driven perception method that facilitates proactive human-robot interaction in shared-access scenarios. Our method features a hierarchical human intention prediction model that allows the robot to distinguish between preparatory actions (waiting) and transient interactions (accessing the instrument). Our results demonstrate that the proposed approach enhances efficiency by enabling proactive human-robot interaction, streamlining coordination, and potentially increasing the efficiency of autonomous scientific labs.