lifelong-robot-learning/libero

LIBERO is a benchmark and research framework for lifelong and multitask robot learning. The main workflow selects a task suite and demonstrations, trains a visuomotor policy with a lifelong-learning method, and evaluates task success in simulated manipulation environments. Its runtime spans task/environment construction, demonstration datasets, policy networks, learning algorithms, and evaluation. The README documents suite, policy, algorithm, and evaluation capabilities; sampled training and environment code shows parts of their wiring. The benchmark implementation itself is absent from the supplied tree, so its internals are not mapped.