Language-conditioned robot behavior plays a vital role in executing complex
tasks by associating human commands or instructions with perception and
actions. The ability to compose long-horizon tasks based on unconstrained
language instructions necessitates the acquisition of a diverse set of
general-purpose skills. However, acquiring inherent primitive skills in a
coupled and long-horizon environment without external rewards or human
supervision presents significant challenges. In this paper, we evaluate the
relationship between skills and language instructions from a mathematical
perspective, employing two forms of mutual information within the framework of
language-conditioned policy learning. To maximize the mutual information
between language and skills in an unsupervised manner, we propose an end-to-end
imitation learning approach known as Language Conditioned Skill Discovery
(LCSD). Specifically, we utilize vector quantization to learn discrete latent
skills and leverage skill sequences of trajectories to reconstruct high-level
semantic instructions. Through extensive experiments on language-conditioned
robotic navigation and manipulation tasks, encompassing BabyAI, LORel, and
CALVIN, we demonstrate the superiority of our method over prior works. Our
approach exhibits enhanced generalization capabilities towards unseen tasks,
improved skill interpretability, and notably higher rates of task completion
success.

通过数学方法评估语言条件化策略学习框架中技能与语言指令之间的关系，提出了一种称为语言条件化技能发现（LCSD）的端到端模仿学习方法，通过最大化语言和技能之间的互信息，在无监督的情况下学习离散潜在技能并利用技能序列重构高级语义指令，通过在 BabyAI、LORel 和 CALVIN 上进行大量实验，展示了我们方法在语言条件化的机器人导航和操作任务中的优越性，包括对未知任务的增强泛化能力、改进的技能可解释性和显著提高的任务完成成功率。