Robotic Manipulation
Robotic manipulation research focuses on enabling robots to dexterously interact with their environment, achieving complex tasks through precise movements and object manipulation. Current efforts concentrate on improving the robustness and generalization of manipulation policies, often leveraging vision-language models, transformer architectures, and reinforcement learning techniques to enable robots to understand and respond to diverse instructions and environments. This field is crucial for advancing automation in various sectors, from manufacturing and logistics to healthcare and agriculture, by creating more adaptable and reliable robotic systems capable of handling a wider range of tasks. Furthermore, significant attention is being paid to developing more efficient data collection methods and improving the safety and reliability of these systems.
Papers
Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration
Ekrem Misimi, Alexander Olofsson, Aleksander Eilertsen, Elling Ruud Øye, John Reidar Mathiassen
CloudGripper: An Open Source Cloud Robotics Testbed for Robotic Manipulation Research, Benchmarking and Data Collection at Scale
Muhammad Zahid, Florian T. Pokorny
Machine Learning Meets Advanced Robotic Manipulation
Saeid Nahavandi, Roohallah Alizadehsani, Darius Nahavandi, Chee Peng Lim, Kevin Kelly, Fernando Bello
Neural Field Representations of Articulated Objects for Robotic Manipulation Planning
Phillip Grote, Joaquim Ortiz-Haro, Marc Toussaint, Ozgur S. Oguz
Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation
Piotr Kicki, Michał Bidziński, Krzysztof Walas