Real-time Learning and Evolution in Robotic Art Installations

As with every emerging technology, new tools in the hands of artists reshape the nature of artwork creation. Current frameworks for robotics in arts deploy the robot as an autonomous creator or a collaborator, thus leaving a certain gap between the human artist and the machine. Now, we stand at the dawn of an era where artists can escape physical limitations and reshape their creative identity by inhabiting an alternative body. This new paradigm allows artists not only to command a robot remotely, but also to {\it be} a robot, to see and feel through it, experiencing a new embodied reality. Unlike virtual reality, where art is created in a digital dimension, in this case art creation is still firmly grounded in the material world: clay molded by mechanical hands, paint swept across a canvas or gestures performed on a physical stage alongside human actors. Through the robot avatar Alter-Ego, we explore the Alter-Art paradigm in dance, theater, and painting; it integrates immersive teleoperation and compliant actuation to enable a first-person creative experience. Analyzing qualitative artistic feedback, we investigate how embodiment shapes creative agency, identity and interaction with the environment. Our findings suggest that artists rapidly develop a sense of presence within the robotic body. The robot's physical constraints influence the creative process, manifesting differently across artistic domains. We highlight embodiment as a central design principle, contributing to social robotics and expanding the possibilities for telepresence and accessible artistic expression.

This chapter describes EMCAR, a novel software tool for programming robot motion that leverages the unique affordances of artistic practices such as puppetry and drawing to conceive, design, and program novel interactions and realize new use cases for HRI. The advantage of this no-code platform is that it expands creative applications for collaborative robots - putting robots directly in the hands of artists - and provides an inclusive environment that enables individuals with little or no technical backgrounds to engage meaningfully in collaborations and robotics research.

This work presents a human-centered collaborative framework that integrates Preference-Based Optimization (PBO) and Dynamic Movement Primitives (DMPs) to optimize robot-assisted tasks such as painting. The system allows the operator to perform the process while the robot adapts its behavior in real-time, dynamically adjusting the orientation of the piece in order to match the orientation of the operator's hand. The PBO framework leverages the GLISp algorithm to iteratively refine control parameters such as execution time, robot responsiveness, and rotation amplification through human feedback. Moreover, DMPs have been modified to enhance the reactive behavior of the robot and its adaptability to ergonomic requirements. The method was validated with a heterogeneous group of participants executing \rev{painting tasks}. The results show that our strategy effectively reduces operator effort while optimizing process outcomes.