  Learning diverse dexterous manipulation behaviors with assorted objects
remains an open grand challenge. While policy learning methods offer a powerful
avenue to attack this problem, they require extensive per-task engineering and
algorithmic tuning. This paper seeks to escape these constraints, by developing
a Pre-Grasp informed Dexterous Manipulation (PGDM) framework that generates
diverse dexterous manipulation behaviors, without any task-specific reasoning
or hyper-parameter tuning. At the core of PGDM is a well known robotics
construct, pre-grasps (i.e. the hand-pose preparing for object interaction).
This simple primitive is enough to induce efficient exploration strategies for
acquiring complex dexterous manipulation behaviors. To exhaustively verify
these claims, we introduce TCDM, a benchmark of 50 diverse manipulation tasks
defined over multiple objects and dexterous manipulators. Tasks for TCDM are
defined automatically using exemplar object trajectories from various sources
(animators, human behaviors, etc.), without any per-task engineering and/or
supervision. Our experiments validate that PGDM's exploration strategy, induced
by a surprisingly simple ingredient (single pre-grasp pose), matches the
performance of prior methods, which require expensive per-task feature/reward
engineering, expert supervision, and hyper-parameter tuning. For animated
visualizations, trained policies, and project code, please refer to:
https://pregrasps.github.io/
