  Lifelong learning offers a promising paradigm of building a generalist agent
that learns and adapts over its lifespan. Unlike traditional lifelong learning
problems in image and text domains, which primarily involve the transfer of
declarative knowledge of entities and concepts, lifelong learning in
decision-making (LLDM) also necessitates the transfer of procedural knowledge,
such as actions and behaviors. To advance research in LLDM, we introduce
LIBERO, a novel benchmark of lifelong learning for robot manipulation.
Specifically, LIBERO highlights five key research topics in LLDM: 1) how to
efficiently transfer declarative knowledge, procedural knowledge, or the
mixture of both; 2) how to design effective policy architectures and 3)
effective algorithms for LLDM; 4) the robustness of a lifelong learner with
respect to task ordering; and 5) the effect of model pretraining for LLDM. We
develop an extendible procedural generation pipeline that can in principle
generate infinitely many tasks. For benchmarking purpose, we create four task
suites (130 tasks in total) that we use to investigate the above-mentioned
research topics. To support sample-efficient learning, we provide high-quality
human-teleoperated demonstration data for all tasks. Our extensive experiments
present several insightful or even unexpected discoveries: sequential
finetuning outperforms existing lifelong learning methods in forward transfer,
no single visual encoder architecture excels at all types of knowledge
transfer, and naive supervised pretraining can hinder agents' performance in
the subsequent LLDM. Check the website at https://libero-project.github.io for
the code and the datasets.
