LAPP Framework for Lab Automation

Updated 6 August 2025

LAPP Framework is a reference architecture for plug-and-play lab automation using standardized optical markers and digital twins.
It integrates robotics, machine vision, and cloud-based metadata to eliminate manual configuration and enable rapid reconfiguration.
The system supports seamless multi-vendor interoperability, dynamic workflow reconfiguration, and robust device discovery in research labs.

The Laboratory Automation Plug & Play (LAPP) Framework is a reference architecture for achieving rapid, vendor-agnostic, and reconfigurable integration of robotic and software systems in research laboratories. Its central aim is to provide “plug-and-play” capability, allowing devices from heterogeneous manufacturers to be discovered, identified, and operated by robots or other automation agents with minimal or no manual configuration, including elimination of robot teaching and position calibration. LAPP unifies approaches from robotics, digital twinning, machine vision, standardized device metadata, and cloud-based knowledge infrastructures to create a universally interoperable environment, particularly for life science and pharmaceutical laboratories but extensible to materials, chemistry, and digital biology domains (Wolf et al., 2021, Wolf et al., 2022).

1. Core Architecture and “Plug-and-Play” Principles

LAPP is structured around the notion of “plug-and-play” at both the physical (hardware) and informational (software, protocol, and data) levels. When a laboratory device is deployed, it must present two standardized optical markers: a barcode encoding device identification and a fiducial marker providing a device-attached coordinate system (CS) for vision-based pose estimation (Wolf et al., 2021, Wolf et al., 2022).

Upon robotic encounter, the following occurs:

The mobile manipulator (MoMa) vision system detects the marker to compute the rigid-body transformation matrix:

$T = \begin{bmatrix} R & t \ 0 & 1 \end{bmatrix}$

where $R$ is rotation and $t$ is translation.

The robot reads the barcode (e.g., DMRE DataMatrix format), retrieves a unique device ID, and queries a universal cloud database (Universal Integration Knowledge Base, UIKB).
The API returns all integration-relevant digital twin data, including geometrical, functional, and protocol parameters, as well as “Action Primitives” (LAPP-APs) describing manipulation sequences in the marker’s CS.

A device can thus be “plugged in” without local teaching or per-device programming. The robot planning stack is parameterized entirely by the digital twin, enabling both “teaching-free” and “vendor-neutral” integration (Wolf et al., 2022).

2. Digital Twin Encoding and Information Schema

The digital twin (DT) sits at the core, providing a structured, hierarchical representation of each device’s static and dynamic properties (Wolf et al., 2022):

Layer	Principle Parameters	Examples
Form	Geometry, marker layout	3D mesh, marker position, CS axes
Functionality	Supported operations	Pipetting, heating, shaking
Location	Site positions, transforms	$T_4(m \rightarrow s)$ , $T_6(w \rightarrow d)$
Process	Timing, sequence models	Task duration, action dependencies
State	Instance status, calibration	Live device status, corrections

Parameters are classified as prototype (model-invariant, e.g., for all S2k centrifuges) and instance (specific to a calibrated device), supporting robust, transferable automation (Wolf et al., 2022).

Positions required for robot operation—such as “device approach,” “hand-over site,” and “home” locations—are specified in the device’s CS, referenced relative to the marker, and made automatically discoverable by the robot’s vision system.

3. Robotics Integration and Action Primitives

Robots are integrated without manual teaching. Before initiating a task (e.g., pick-and-place of a microplate), the robot:

Retrieves key positions from the DT (e.g., approach and hand-over sites).
Uses the fiducial marker and vision system to localize its end effector relative to the device.
Executes sequences of “Action Primitives” (LAPP-APs), each primitive being a manipulation segment (e.g., grasp, open lid, insert) specified in the device’s CS.

This approach is broadly compatible with both benchtop and mobile manipulators (4–7 degrees of freedom) with parallel grippers, and is anticipated to extend to tool-changers, multi-fingered hands, and more complex end-effectors as standardization of device-side interfaces evolves (Wolf et al., 2021, Wolf et al., 2022).

Relevant coordinate transformation chains for pose estimation and task execution follow:

$T_0(w \rightarrow \text{PoI})$ : world to point of interest
$T_1(c \rightarrow m)$ : camera to marker
$T_4(m \rightarrow s)$ : marker to site (e.g., hand-over)

Such chains are encapsulated in device metadata and automatically composed via the robot’s planning stack. The result is cross-robot consistency and rapid swap-in/out of instrumentation.

4. Standardization, Cloud Infrastructure, and Industry Interoperability

The LAPP framework achieves interoperability by:

Enforcing mandatory optical markers (barcode and fiducial) for every device.
Requiring device vendors to supply a DT for each model, describing operational affordances and geometry.
Using a cloud database (UIKB) for:
- Device identification and lifecycle/maintenance data
- Communication/control protocol definitions (REST, WebSocket, GraphQL, gRPC)
- Action Primitive sets, referenced in the marker’s CS
Supporting existing and emerging standards, especially SiLA and LADS/OPC-UA, to enable standardized communication APIs and semantic device description models (Wolf et al., 2021).

This combination enables both software and hardware “plug-and-play”—devices are discovered, digitally described, and then operated by robots using a uniform set of protocol and geometry abstractions, regardless of local configuration.

5. Applications, Use Cases, and Demonstrators

The LAPP framework targets, and has been deployed in, life science, pharmaceutical, and chemistry laboratories but is extensible to materials science and professional biology settings (Wolf et al., 2021, Wolf et al., 2022). Primary use cases include:

Sample transportation and machine tending (transport, pickup, drop-off, lid operation)
Dynamic reconfiguration (adding/removing devices without system retooling)
Multi-vendor setups in R&D and production, where instrumentation changes—but workflow logic remains constant
Cross-platform deployment (integration with ROS, use of SLAM navigation for MoMa, compatibility with open-source and proprietary devices)

In all contexts, the haLLMark trait is the ability to rapidly reconfigure laboratory workflows with minimal programming, using standardized digital and physical interfaces.

6. Technological Strategies and Future Directions

Key technological enablers identified in the LAPP literature include:

Use of ROS for robot perception, navigation (SLAM), and motion planning layers.
Reliance on digital twin platforms (e.g., Asset Administration Shell) and semantic ontologies to represent device and labware properties.
Integration of process representation languages (e.g., BPMN, LabOP) for orchestration.
Robustness under laboratory dynamics, allowing error recovery, re-positioning, and resource reallocation in the presence of disturbances.
Immediate future research focuses on:
- Formalization and universal adoption of Action Primitive data structures in marker coordinate space.
- Open digital twin updating and synchronization protocols.
- Tool standardization for end effectors and device mechanical interfaces.
- Expansion to more advanced manipulation (soft robotics, collaborative multi-robot setups).
- Robust dynamic calibration, to maximize “plug-and-play” fidelity even as physical systems drift or are replaced.

Industry-wide adoption depends on further consensus around DT schemas, barcode and marker layout, and protocol standardization. The cloud database paradigm is forecasted to allow not only static discovery but real-time querying and analytics, supporting predictive maintenance and fully digital laboratory twins.

7. Relationship to Broader Automation Paradigms

The LAPP framework occupies a central role in the transition from monolithic, hand-crafted automation solutions to modular, adaptive, and interoperable laboratory platforms. Its architecture prefigures integration with AI-enabled self-maintaining (“self-caring”) laboratories (Ochiai et al., 10 Jan 2025), foundation-model-based cognitive control (Hatakeyama-Sato et al., 14 Jun 2025), simulation-based benchmarking (Lan et al., 20 May 2025), and ultimate convergence with self-driving laboratories.

The essential distinguishing feature is the explicit “plug-and-play” ethos: any laboratory asset with a compliant digital twin and optical tagging can be autonomously mobilized, physically manipulated, and computationally controlled by a robot or automation controller, with the integration effort reduced to digital coordination rather than physical retooling or custom programming.