Robotics Domain
Robotics research currently focuses on enhancing robot autonomy, safety, and dexterity, particularly in unstructured environments. Key areas include developing robust control algorithms (like Model Predictive Control and reinforcement learning), improving perception through advanced sensor fusion and generative models, and creating more efficient and adaptable robot designs. These advancements are driving progress in diverse applications such as agriculture, healthcare, and manufacturing, ultimately aiming to create more capable and reliable robots for a wider range of tasks.
Papers
Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots
Angelo Bratta, Michele Focchi, Niraj Rathod, Claudio Semini
World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator
Tatsuya Matsushima, Yuki Noguchi, Jumpei Arima, Toshiki Aoki, Yuki Okita, Yuya Ikeda, Koki Ishimoto, Shohei Taniguchi, Yuki Yamashita, Shoichi Seto, Shixiang Shane Gu, Yusuke Iwasawa, Yutaka Matsuo
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models
Or Wertheim, Dan R. Suissa, Ronen I. Brafman
Modelling the Turtle Python library in CSP
Dara MacConville, Marie Farrell, Matt Luckcuck, Rosemary Monahan
Task Allocation using a Team of Robots
Haris Aziz, Arindam Pal, Ali Pourmiri, Fahimeh Ramezani, Brendan Sims