Enable robots and other agents to develop broadly intelligent behavior through learning and interaction. Exploring the intersection of machine learning and robotic control, including
- end-to-end learning of visual perception and robotic manipulation skills,
- deep reinforcement learning of general skills from autonomously collected experience,
- imitation learning,
- learning from various sources of human feedback,
- learning from interaction with other agents,
- meta-learning algorithms that can enable fast learning of new concepts and behaviors.
| EE ACTIVE FACULTY | ||
| Chelsea Finn | Dorsa Sadigh | Shuran Song |
| ALL FACULTY | |
| Robotics - View all associated faculty | |
| COURTESY FACULTY | ||
| Grace X. Gao | Marco Pavone | |