Future Predictive Success-or-Failure Classification for Long-Horizon
  Robotic Tasks
Naoya Sogi Hiroyuki Oyama Takashi Shibata Makoto Terao
Automating long-horizon tasks with a robotic arm has been a central research
topic in robotics. Optimization-based action planning is an efficient approach
for creating an action plan to complete a given task. Construction of a
reliable planning method requires a design process of conditions, e.g., to
avoid collision between objects. The design process, however, has two critical
issues: 1) iterative trials--the design process is time-consuming due to the
trial-and-error process of modifying conditions, and 2) manual redesign--it is
difficult to cover all the necessary conditions manually. To tackle these
issues, this paper proposes a future-predictive
success-or-failure-classification method to obtain conditions automatically.
The key idea behind the proposed method is an end-to-end approach for
determining whether the action plan can complete a given task instead of
manually redesigning the conditions. The proposed method uses a long-horizon
future-prediction method to enable success-or-failure classification without
the execution of an action plan. This paper also proposes a regularization term
called transition consistency regularization to provide easy-to-predict feature
distribution. The regularization term improves future prediction and
classification performance. The effectiveness of our method is demonstrated
through classification and robotic-manipulation experiments.