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Robotics AI

Autonomous robots are one of the best carriers and a long-term goal for general artificial intelligence (AGI) research. To be able to complete tasks independently in complex environments, it requires compositional capabilities of perception, memory, reasoning, planning, decision-making, learning, etc. Our goal is to develop "intelligent brain" for robots so that it can free people from labor-intensive tasks.

Technical challenge

Sim2Real

Transferring agents trained in simulation to physical environment is one of the key technologies for efficiently training autonomous robots. Due to the gap between simulation and physical environment, it is crucial for the agent to be able to learn and adapt to the variance of image, physics, motion, feedback, etc. in the environment.

Sample Efficiency

Robots often need to adapt and learn online in the physical environment, with high demands for sample efficiency due to high cost in the physical environment. Research of batch/offline learning, model-based learning, self-supervised learning, etc. are important to continuously optimize and improve agent learning efficiency.

Generalization

To be able to generalize to unseen environments and new tasks, which is necessary for large-scale application of robots, agents need generalization capabilities in representation, multi-tasks learning, reasoning, and decision-making.

Research Direction

Control

Agents with perception, navigation, and adaptive control capability can be used for motion control and planning of robotic arms, quadruped, and wheeled robots. Research areas include deep learning, reinforcement learning, multi-modal perception, planning, and control.

Multi-agent

Use of reinforcement learning for combinatorial optimization, multi-agent reinforcement learning to optimize cooperation efficiency for a group of robots.

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