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Collaborative Robotic Grasping during Teleoperation Tasks
Description
This topic focuses on improving robotic grasping networks and advancing embodied intelligence. Grasping is a fundamental capability in robotic manipulation and often plays a decisive role in the overall success of a task. Despite significant progress in learning-based grasping, current models still struggle with generalization and robustness in unstructured environments. Our goal is to enhance the success rate of existing grasping models and deploy them in real-world scenarios, where they can provide intelligent assistance during teleoperation tasks. By leveraging pre-trained grasping networks, we aim to reduce the human operator's workload, increase autonomy, and improve manipulation efficiency in complex and dynamic settings. This work offers a unique opportunity to work at the intersection of perception, control, and learning—pushing the boundaries of what robots can achieve through smarter, more adaptive grasping.
Prerequisites
- Good Programming Skills (Python, C++)
- Knowledge about Ubuntu/Linux/ROS
- Motivation to learn and conduct research
Contact
dong.yang@tum.de
(Please attach your CV and transcript)