Robot Skill
Robot skill research focuses on enabling robots to learn, execute, and adapt complex tasks, aiming for greater autonomy and robustness in diverse environments. Current efforts concentrate on learning from demonstrations (including human videos), leveraging large language models for task planning and reward function design, and employing various architectures like transformers and neural networks to improve skill generalization and efficiency. This field is crucial for advancing robotics, impacting manufacturing automation, surgical assistance, and human-robot collaboration by enabling robots to handle increasingly complex and unpredictable situations.
Papers
Identifying Important Sensory Feedback for Learning Locomotion Skills
Wanming Yu, Chuanyu Yang, Christopher McGreavy, Eleftherios Triantafyllidis, Guillaume Bellegarda, Milad Shafiee, Auke Jan Ijspeert, Zhibin Li
SkiROS2: A skill-based Robot Control Platform for ROS
Matthias Mayr, Francesco Rovida, Volker Krueger