Data-Efficient Robot Learning

Data-efficient robot learning studies how robots can learn useful skills without costly collections of demonstrations or extensive trial and error on physical hardware. Common approaches include imitation learning, sample-efficient reinforcement learning, world models that support planning or simulated practice, and methods that select, augment, or reuse demonstrations. Human videos, pretrained vision-language-action models, and transfer across robot embodiments can provide useful starting points, while benchmarks such as LIBERO and RoboCasa test manipulation performance in simulation and on real robots. A central challenge is that gains on offline prediction or simulated tasks do not always translate to reliable real-world behavior, and adaptation must improve new skills without losing old ones or requiring excessive supervision.