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
Learning Reward Functions for Robotic Manipulation by Observing Humans
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid
ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds
Daniel Seita, Yufei Wang, Sarthak J. Shetty, Edward Yao Li, Zackory Erickson, David Held
SEIL: Simulation-augmented Equivariant Imitation Learning
Mingxi Jia, Dian Wang, Guanang Su, David Klee, Xupeng Zhu, Robin Walters, Robert Platt
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces
Xinghao Zhu, Wenzhao Lian, Bodi Yuan, C. Daniel Freeman, Masayoshi Tomizuka