We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models. We present extensive experiments on this finding, and we provide both intuitive insight and theoretical arguments distinguishing the properties of implicit models compared to their explicit counterparts, particularly with respect to approximating complex, potentially discontinuous and multi-valued (set-valued) functions. On robotic policy learning tasks we show that implicit behavioral cloning policies with energy-based models (EBM) often outperform common explicit (Mean Square Error, or Mixture Density) behavioral cloning policies, including on tasks with high-dimensional action spaces and visual image inputs. We find these policies provide competitive results or outperform state-of-the-art offline reinforcement learning methods on the challenging human-expert tasks from the D4RL benchmark suite, despite using no reward information. In the real world, robots with implicit policies can learn complex and remarkably subtle behaviors on contact-rich tasks from human demonstrations, including tasks with high combinatorial complexity and tasks requiring 1mm precision.
翻译:我们发现,在一系列广泛的机器人政策学习设想中,以隐含模型处理受监督的政策学习通常比通常使用的显性模型平均表现更好。我们介绍了关于这一发现的广泛实验,并且提供了直观的洞察力和理论论据,将隐含模型的特性与其直观的对应功能区分开来,特别是在近似复杂、潜在不连续和多价值(定值)的功能方面。关于机器人政策学习任务,我们表明,带有能源模型(EBM)的隐含行为性克隆政策往往优于常见的显性(中方错误,或混合密度)行为克隆政策,包括具有高维度行动空间和视觉图像投入的任务。我们发现,这些政策提供了竞争性的结果,或者超越了D4RL基准成套挑战性(定值)的人类专家任务的离线式强化方法,尽管没有奖赏信息。在现实世界,含隐含政策的机器人可以学习人类演示中接触丰富任务的复杂和微妙行为,包括具有高调复杂性和需要1毫米精确性的任务。