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Manuel Catalano

Publications and source records attributed to Manuel Catalano.

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Agonist-Antagonist Neural Coordination without Mechanical Coupling after Targeted Muscle Reinnervation

Following limb amputation and targeted muscle reinnervation (TMR), nerves that originally innervated agonist and antagonist muscles are rerouted into one or more residual target muscles. This rerouting profoundly alters the natural mechanical coupling and afferent signalling that normally link muscle groups in intact limbs. Despite this disruption, in this study we demonstrate, using high-density intramuscular microelectrode arrays implanted in reinnervated muscles of three TMR participants, that motor units (MUs) associated with agonist and antagonist tasks remain functionally coupled. Specifically, over 40% of motor units active during agonist tasks were also recruited during the corresponding antagonist tasks, even though no visual feedback on antagonist neural activity was provided. These motor units exhibited significantly different firing rates depending on their functional role. These results provide the first motor-unit-level evidence that the central nervous system preserves coordinated agonist-antagonist control after TMR and inform restorative surgical strategies and prosthetic systems capable of regulating both limb kinematics and dynamics based on agonist-antagonist commands interplay.

q-bio.NC

Next-Gen Museum Guides: Autonomous Navigation and Visitor Interaction with an Agentic Robot

Autonomous robots are increasingly being tested into public spaces to enhance user experiences, particularly in cultural and educational settings. This paper presents the design, implementation, and evaluation of the autonomous museum guide robot Alter-Ego equipped with advanced navigation and interactive capabilities. The robot leverages state-of-the-art Large Language Models (LLMs) to provide real-time, context aware question-and-answer (Q&A) interactions, allowing visitors to engage in conversations about exhibits. It also employs robust simultaneous localization and mapping (SLAM) techniques, enabling seamless navigation through museum spaces and route adaptation based on user requests. The system was tested in a real museum environment with 34 participants, combining qualitative analysis of visitor-robot conversations and quantitative analysis of pre and post interaction surveys. Results showed that the robot was generally well-received and contributed to an engaging museum experience, despite some limitations in comprehension and responsiveness. This study sheds light on HRI in cultural spaces, highlighting not only the potential of AI-driven robotics to support accessibility and knowledge acquisition, but also the current limitations and challenges of deploying such technologies in complex, real-world environments.

cs.RO