Research and Innovation
are Key

Roboception is actively engaged in research and innovation, cooperates with Universities and Research Institutions is involved in strengthening the robotic competences across Europe. In addition to actively coordinating the Topic Group “Perception” in euRobotics aisbl, Roboception is involved in publicly funded projects on national (BFS, BMWi, BMBF) and European level.

Associations

Roboception is a proud member of the following associations and organizations:

Current Projects

07/2023-06/2026

AutASa

AutASa focuses on enhancing efficiency by integrating intelligent robotic assistance in semi-autonomous waste disposal, empowering humans to leverage their cognitive skills for a more productive collaboration with assistive robots.

01/2023-12/2025

Smarthandle

Manual and automated production lines must evolve to “produce more and diverse with less”, however they need to address shortcomings such as e.g. lack of cognitive perception systems to allow autonomous reasoning. SMARTHANDLE will research technologies to address these needs and support European industry.

11/2020-04/2025

Ki5GRob

KI5GRob

KI5GRob addresses in a new way how machine learning can be efficiently used in robotics by tightly linking cloud technologies and robotics in a tight control loop, and how the concept pursued can fundamentally simplify the programming and maintenance of robotic systems.

03/2021-02/2025

5GSmartFact

The 5gsmartfact project (Future Wireless Connected and Automated Industry Enabled by 5G) is to study, develop, optimize and assess the deployment of 5G networks factory environments and automation applications.

12/2021-02/2025

KI4HE

The focus of the KI4HE project is on improved environmental perception, self-localization, and assisted remote control of vehicles in hard-to-reach difficult-to-access environments.

01/2021-12/2025

ODIN

The ODIN project aims to strengthen the EU production companies’ trust in utilizing advanced robotics by demonstrating that novel robot-based production systems are not only technically feasible but also efficient and sustainable for immediate introduction at the shopfloor.

01/2020-12/2023

RoMaFo

Supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy (StMWi), the consortium is working towards a robotic assistant system and machine vision for the assembly of unstable components in customer-individual products.

Completed Projects

Roboception is the coordinator of this project, funded by the Federal Ministry of Education and Research (BMBF). Its objective is the development of human motion prediction using a combination of three deep neural networks (NN).

Robust perception capabilities for robots to support elderly users in a home environment, funded by the German Federal Ministry of Education and Research (BMBF).

Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems, funded under the project number 723616 within the Horizon 2020 FoF2 – 2016 program.

Pace-keeping 3D-reconstruction and analysis (Schritthaltende 3D-Rekonstruktion und -Analyse), funded by the Bavarian Science Foundation (BFS).

Mobile, ad-hoc cooperating robot teams (Mobile, ad-hoc kooperierende Roboterteams), funded by the Bavarian Science Foundation (BFS).

Robot-supported interaction systems for performance-impaired employees, funded by the Bavarian State Ministry of Economic Affairs, Regional Development and Energy.

The goal of the project is to develop concepts and coordinated evaluation electronics that make it possible to combine a wide range of sensors, measurement processes and AI methods into a safety-oriented sensor system. The aim is to enable robots to be operated more cost-effectively and efficiently, yet safely in the human workspace.

Datasets

Roboception is actively engaged in research and innovation. For that reason, we are creating publicly available data sets.

Synthetic dataset for learning parallel-jaw grasping from stereo images: includes four different datasets, with automatically labelled training data for predicting parallel jaw grasps from stereo-matched depth images. For more details, please refer to the publication:

“Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images”

Justus Drögemüller, Carlos X. Garcia, Elena Gambaro, Michael Suppa, Jochen Steil, Maximo A. Roa.

Proc. IEEE Int Conf. On Advanced Robotics (ICAR), 2021.

Research Partners

Roboception is proud to regularly provide demo products for testing and development purposes to research facilities such as:

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Research and Innovation are Key