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About this paper
Stephen James, Michael Bloesch, Andrew J. Davison
Task-Embedded Control Networks (TecNets) from the Dyson Robotics Lab at Imperial College London enable robots to acquire new visuomotor tasks from one or few visual demonstrations. The approach achieved the first successful sim-to-real transfer for few-shot learning, demonstrating 72.97% 1-shot success on a real robot after training solely in simulation.
Key insights
- Explicit metric learning for the task embedding is vital for high success rates in few-shot learning, enabling the system to learn a structured and discriminative latent space for tasks.
- Conditioning the control policy with a compact task embedding offers a scalable solution for retaining knowledge across many tasks, mitigating the forgetting and scalability issues of gradient-based meta-learning.
- Effective domain randomization during simulation training allows for direct and successful sim-to-real transfer of few-shot learning capabilities to a physical robot.
Summary by alphaXiv.
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