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
Sequential Manipulation of Deformable Linear Object Networks with Endpoint Pose Measurements using Adaptive Model Predictive Control
Tyler Toner, Vahidreza Molazadeh, Miguel Saez, Dawn M. Tilbury, Kira Barton
Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
Cheng Chi, Zhenjia Xu, Chuer Pan, Eric Cousineau, Benjamin Burchfiel, Siyuan Feng, Russ Tedrake, Shuran Song