Robot Person
Robot person research focuses on creating robots capable of interacting naturally and effectively with humans, encompassing tasks from simple navigation to complex manipulation and social interaction. Current research emphasizes developing robust control algorithms (like Kalman filters and Model Predictive Control), integrating advanced perception models (including Vision-Language Models and sensor fusion), and improving human-robot interaction through multimodal communication and shared autonomy. This field is significant for advancing robotics capabilities in various sectors, including healthcare, manufacturing, and service industries, by enabling robots to perform tasks more safely, efficiently, and intuitively alongside humans.
Papers
ViewVR: Visual Feedback Modes to Achieve Quality of VR-based Telemanipulation
A. Erkhov, A. Bazhenov, S. Satsevich, D. Belov, F. Khabibullin, S. Egorov, M. Gromakov, M. Altamirano Cabrera, D. Tsetserukou
Sthymuli: a Static Educational Robot. Leveraging the Thymio II Platform
Manuel Bernal-Lecina, Alejandrina Hernández, Adrien Pannatier, Léa Pereyre, Francesco Mondada
"Can you be my mum?": Manipulating Social Robots in the Large Language Models Era
Giulio Antonio Abbo, Gloria Desideri, Tony Belpaeme, Micol Spitale
Implementation Of Wildlife Observation System
Neethu K N, Rakshitha Y Nayak, Rashmi, Meghana S
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Ali Imran, Giovanni Beltrame, David St-Onge
Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination
Leonardo Barcellona, Andrii Zadaianchuk, Davide Allegro, Samuele Papa, Stefano Ghidoni, Efstratios Gavves
Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding
Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara