Publication
JENGA: Object selection and pose estimation for robotic grasping from a stack
Sai Srinivas Jeevanandam; Sandeep Prudhvi Krishna Inuganti; Shreedhar Govil; Didier Stricker; Jason Raphael Rambach
In: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2025), October 19-25, Hangzhou, China, IEEE Robotics and Automation Society, 10/2025.
Abstract
Vision-based robotic object grasping is typically investigated in the context of isolated objects or unstructured object sets in bin picking scenarios. However, there are several settings, such as construction or warehouse automation, where a robot needs to interact with a structured object formation such as a stack. In this context, we define the problem of selecting suitable objects for grasping along with estimating an accurate 6DoF pose of these objects. To address this problem, we propose a camera-IMU based approach that prioritizes unobstructed objects on the higher layers of stacks and introduce a dataset for benchmarking and evaluation, along with a suitable evaluation metric that combines object selection with pose accuracy. Experimental results show that although our method can perform quite well, this is a challenging problem if a completely error-free solution is needed. Finally, we show results from the deployment of our method for a brick-picking application in a construction scenario.
Projects
- HumanTech - Human Centered Technologies for a Safer and Greener European Construction Industry
- TWIN4TRUCKS - Digitaler Zwilling und KI in der vernetzten Fabrik für die integrierte Nutzfahrzeugproduktion, Logistik und Qualitätssicherung