  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.
