Python based Robosense I allowing users to auto generate rapid code for programming contextually informed robotic tool paths

Completed ink drawing of a portrait of Edgar Allan Poe, signed by Sulla, our ABB IRB 4600 industrial robotic arm

Project II: Interactive Robotic Wine Serving

Dynamic tracking of glass object in an unpredictable environment for accurate pouring

Project III: Dynamically Adjusted Extrusion Loop

Robotic Printing With Multiple Materials

ROBOSENSE I

Research

Collaborator: Andrew Moorman

While nonlinear concepts are extensively applied in analysis and generative design within architecture, their integration into the material domain of fabrication and construction remains limited. As the gap between digital design models, shop drawings, and fabrication continues to narrow, this project aims to learn from natural systems that integrate code, geometry, pattern, material behavior, communication, and form within dynamic feedback loops of reciprocity and generative fabrication.

We addressed three interconnected challenges—1) robotic ink drawing, 2) robotic wine pouring with object detection, and 3) dynamically adjusted extrusion—to develop a comprehensive toolkit that includes software, custom digital design tools, and hardware for robotic fabrication and user interaction in cyber-physical contexts. Our primary goal is to simplify and consolidate the various platforms required to establish feedback networks for robotic fabrication into a unified and intuitive programming environment, accessible to both advanced and novice users.

Our experimentation with prototyping feedback networks in design practice reveals a remarkably consistent set of patterns in application. By identifying and leveraging these redundancies, we developed a support toolkit consisting of data structures and routines, facilitating the creation of integrated software for user-friendly programming. This approach streamlines the implementation of common roles and functionalities in dynamic robotic fabrication, promoting a feedback-oriented methodology that enhances design processes and enables more efficient collaboration between digital and physical space.