Can Multi-Fingered Robots Transform Shipboard Operations and Autonomous Maintenance?

May 1, 2026

University of Southern California, Los Angeles, CA

Dexterous robot hands. (Image: Midjourney)

In dynamic, unstructured environments like ship decks and even home kitchens, robots today still struggle to perform precision tasks such as tightening bolts or handling wires. This makes critical ship maintenance tasks difficult.

USC researcher, Erdem Bıyık, aims to advance robots’ finger manipulation and integrate human feedback to enable realtime learning for robots in an upcoming three-year, $750,000 project funded by the Office of Naval Research (ONR).

With a background in computer science, robotics and electrical engineering, Bıyık will work to advance dexterous robotic manipulation — enabling machines to perform precise, humanlike tasks using multi-fingered hands. While most robots today rely on simple grippers, his research aims to unlock more sophisticated capabilities, such as tool use, fine assembly, and adaptive handling in complex environments.

His work addresses a key limitation of current systems: even a single misplaced finger can cause failure in high-precision tasks, sometimes with damaging consequences. By developing more advanced, multi-fingered hands, Bıyık aims to enable robots to operate hand tools, such as hammers, and perform tasks like tightening knobs and bolts that require a high level of precision. Such advanced finger manipulation capabilities ultimately allows robots to operate in unstructured and dynamic environments, like shifting ship decks and busy kitchens.

Another key part of the project is introducing a new framework for teaching robots through multimodal human feedback.

Because it is traditionally difficult to provide robots with the precise feedback needed to correct minute physical errors, Bıyık explained his “goal is to make robot learning more aligned with how humans teach each other. By integrating multiple forms of feedback, we can enable robots to acquire complex skills faster and more reliably.”

By combining visual demonstration — where robots learn by observing human actions — with natural language guidance, such as real-time verbal corrections, Bıyık’s work seeks to create more intuitive and efficient ways for humans to train robotic systems.

The application and technologies developed through this project goes beyond naval applications, and can be extended to everyday environments, supporting tasks in homes and even warehouses or service industries that require high levels of precision and adaptability.

By developing robots that can learn directly from human input and operate with greater dexterity, Bıyık’s work aims to bridge the gap between controlled laboratory systems and real-world deployment.

This article was written by Venice Tang for the University of Southern California. For more information, visit here .