Human-Aware Robot Behaviour in Self-Driving Labs

• URL: http://arxiv.org/abs/2603.08420v2
• Date: Wed, 11 Mar 2026 18:02:02 GMT
• Title: Human-Aware Robot Behaviour in Self-Driving Labs
• Abstract summary: Mobile robot chemists (MRCs) play a pivotal role by autonomously navigating the lab to transport samples.<n>Currently, MRCs rely on simple LiDAR-based obstruction detection, which forces the robot passively wait if a human is present.<n>We present an embodied, AI-driven perception method that facilitates proactive human-robot interaction in shared-access scenarios.
• Score: 1.3854111346209868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Replicable Robotics Awareness Method Using LLM-Enabled Robotics Interaction: Evidence from a Corporate Challenge [0.0]
  This paper introduces and evaluates a challenge-based method for robotics awareness using large language models.<n>It was implemented through an LLM-enabled humanoid robot activity conducted with employees of AD Ports Group in the United Arab Emirates.<n>The activity was designed as a team-based, role-driven experience intended to expose participants to embodied AI and human-robot collaboration.
  arXiv  Detail & Related papers  (2026-04-23T07:45:17Z)
• Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics [8.590015229469186]
  We develop a simulation-based framework for the quantitative analysis of physical human-robot interaction.<n>At its core is a full-body musculoskeletal model serving as a predictive surrogate for the human dynamical system.<n>We demonstrate its capability in optimizing human-exoskeleton interactions, showing improved joint alignment and reduced contact forces.
  arXiv  Detail & Related papers  (2026-03-10T05:39:31Z)
• LabOS: The AI-XR Co-Scientist That Sees and Works With Humans [51.025615465050635]
  LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation.<n>By connecting multi-model AI agents, smart glasses, and human-AI collaboration, LabOS allows AI to see what scientists see, understand experimental context, and assist in real-time execution.
  arXiv  Detail & Related papers  (2025-10-16T16:36:22Z)
• Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs [0.0]
  Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation.<n>But they face challenges in orchestrating complex, integrating diverse instruments and AI models, and managing data efficiently.<n>Artificial addresses these issues with a comprehensive orchestration and scheduling system.<n>It unifies lab operations, automates, and integrates AI-driven decision-making.
  arXiv  Detail & Related papers  (2025-04-01T17:22:50Z)
• HACTS: a Human-As-Copilot Teleoperation System for Robot Learning [47.9126187195398]
  We introduce HACTS (Human-As-Copilot Teleoperation System), a novel system that establishes bilateral, real-time joint synchronization between a robot arm and teleoperation hardware.<n>This simple yet effective feedback mechanism, akin to a steering wheel in autonomous vehicles, enables the human copilot to intervene seamlessly while collecting action-correction data for future learning.
  arXiv  Detail & Related papers  (2025-03-31T13:28:13Z)
• Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition [48.65867987106428]
  We introduce a novel system for joint learning between human operators and robots. It enables human operators to share control of a robot end-effector with a learned assistive agent. It reduces the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks.
  arXiv  Detail & Related papers  (2024-06-29T03:37:29Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
  Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis.<n>The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions.<n>Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• Efficient Causal Discovery for Robotics Applications [2.1244188321694146]
  We present a practical demonstration of our approach for fast and accurate causal analysis, known as Filtered PCMCI (F-PCMCI) The provided application illustrates how our F-PCMCI can accurately and promptly reconstruct the causal model of a human-robot interaction scenario.
  arXiv  Detail & Related papers  (2023-10-23T13:30:07Z)
• Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control [17.270360447188196]
  We present a system for human-robot collaborative assembly using learning from demonstration and pose estimation. The proposed system is demonstrated using a physical 6 DoF manipulator in a collaborative human-robot assembly scenario.
  arXiv  Detail & Related papers  (2022-12-02T20:35:55Z)
• Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together [68.44697646919515]
  This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases. The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
  arXiv  Detail & Related papers  (2022-02-03T10:04:26Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.