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
HiER: Highlight Experience Replay and Easy2Hard Curriculum Learning for Boosting Off-Policy Reinforcement Learning Agents
Dániel Horváth, Jesús Bujalance Martín, Ferenc Gábor Erdős, Zoltán Istenes, Fabien Moutarde
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Yafei Hu, Quanting Xie, Vidhi Jain, Jonathan Francis, Jay Patrikar, Nikhil Keetha, Seungchan Kim, Yaqi Xie, Tianyi Zhang, Hao-Shu Fang, Shibo Zhao, Shayegan Omidshafiei, Dong-Ki Kim, Ali-akbar Agha-mohammadi, Katia Sycara, Matthew Johnson-Roberson, Dhruv Batra, Xiaolong Wang, Sebastian Scherer, Chen Wang, Zsolt Kira, Fei Xia, Yonatan Bisk
UniTeam: Open Vocabulary Mobile Manipulation Challenge
Andrew Melnik, Michael Büttner, Leon Harz, Lyon Brown, Gora Chand Nandi, Arjun PS, Gaurav Kumar Yadav, Rahul Kala, Robert Haschke