Sensing Technologies and Open Challenges in Industrial Robots: A Review

Summary

Independent researcher Anirudha Gupta's preprint survey maps the landscape of robot perception for 'lights-out' industrial automation. The work covers sensor modalities such as 2D/3D vision, tactile, and force sensing; core tasks like 6D pose estimation, bin picking, and anomaly detection; learning paradigms including sim-to-real transfer and vision-language models (VLMs); and open challenges such as transparent objects and the certification of learned components.

Why it matters

Industrial robotics is currently stuck at a perception bottleneck. Most existing cells are pre-programmed. The path to true autonomy is defined by how well robots can perceive and interpret unstructured environments. This survey, which integrates modalities, tasks, and learning methods, provides practitioners and researchers with a single reference point to identify the field's weaknesses. These weaknesses are particularly evident in uncertainty quantification, handling deformable materials, and the regulatory certification of learned perception stacks.

Highlights

• Perception is positioned as the fundamental bottleneck between pre-programmed robots and truly autonomous production cells.
• Open problems explicitly include transparent/reflective objects, deformable materials, data scarcity, and the certification of learned components.
• The survey links recent advances to vision-language foundation models integrated via sim-to-real transfer, synthetic data, self-labeling, and digital twins with ROS 2.

Critical take

This article is a preprint by a single independent researcher with no corporate affiliation and has not undergone peer review. The work's extremely broad scope (perception, tasks, learning, integration, challenges) carries the risk of superficial treatment of each topic. Given the lack of peer review or a downloadable dataset, claims regarding 'state-of-the-art' should be treated as a starting map rather than an authoritative assessment.