The transition toward high-mix low-volume manufacturing demands flexibility in robotic manipulation. However, conventional vision systems remain a bottleneck, requiring extensive data collection and model retraining whenever a new object is introduced to the production line. To overcome this rigidity, we present xperception, a zero-shot 6D pose estimation technology that eliminates the need for object-specific fine-tuning and laborious data annotation. By directly utilizing typical CAD models and integrating the rich semantic features of foundation models (e.g. DINOv2, GeDi), xperception achieves millimeter-accurate 6D pose estimation. xperception showed robustness against severe occlusions in industrial tasks like bin picking and is engineered for deployment on industrial edge hardware, such as NVIDIA Jetson Thor. Validated at a TRL of 6, the core methodology behind xperception is based on the FreeZe algorithm, which won the international BOP Challenge 2024, paving the way for scalable, plug-and-play robotic automation in unstructured high-mix low-volume manufacturing industries.
A modular perception pipeline for household manipulation tasks, with a focus on dishware handling in kitchen environments, that integrates open-vocabulary object detection, multi-view segmentation, instance-aware 3D reconstruction, and a 2D-3D feature fusion strategy for 6D pose estimation and grasp planning is present...
Robostral Navigate, an 8B vision-language model built around this scalability objective, is introduced, which consumes only a stream of monocular RGB images - the most ubiquitous sensor across robotic platforms and predicts waypoints by pointing to the next target location in the current camera view.
This work adapts Hugging Face's SmolVLA for Universal Robots lightweight robots, and releases the open-source repository ROS2SmolVLA that implements an interface for ROS 2 to SmolVLA, and makes it applicable for industrial-grade hardware.
Nils Mandischer, Noah Böckmann, Ludwig Holl et al.· 0 citations
This work proposes a plug-and-play fix to automatically generate scene-specific, task-specific, adaptively updating abstractions on top of existing planning and control stacks, and shows that LENS improves classical planning, model-based control, and a vision-language-action model, across a diverse set of highly clutte...
A shared-autonomy framework that assists the operator throughout this process ofTeleoperating a robotic manipulator in industrial environments demands precision that camera-based interfaces alone struggle to deliver and is validated on a quadruped mobile manipulator.
Murilo Vinicius da Silva, Ricardo V. Godoy, Juliano Negri et al.· arXiv.org· 0 citations
The outcomes demonstrate the efficacy of combining edge intelligence with closed-loop robotic control by confirming consistent behavior throughout simulation and limited physical testing.
Xiaoming Liu, Wei Su, Jie Zhang et al.· Scientific Reports· 0 citations
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