Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete(e.g., binary) object goal states, which poses challenges for the learning of complex tasks and transferring learned policy from simulated environments to the real world. Furthermore, state discretization limits a robot's ability to follow human instructions based on the grounding of actions and states. To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve understanding object states and learning policies for continuous goals. To promote language-instructed learning, we provide expert demonstrations with template-generated language descriptions. We assess task performance by utilizing the latest language-conditioned policy learning models. Our results indicate that current models for language-conditioned manipulations continue to experience significant challenges in novel goal-state generalizations, scene generalizations, and object generalizations. These findings highlight the need to develop new algorithms that address this gap and underscore the potential for further research in this area. See our project page at: https://arnold-benchmark.github.io

为了解决任务学习中在现实世界中的固体物体状态的连续性与在现实世界的任务规划中的问题，我们提出了在现实3D场景中使用连续状态的语言条件任务学习的基准ARNOLD，该基准包括8个任务，与语言相关并涉及了连续目标下的对象状态的理解和策略学习。我们通过最新的语言条件策略学习模型对任务绩效进行评估。

ARNOLD：基于连续状态的语言驱动任务学习在逼真3D场景中的基准