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.

翻译：使用机械臂自动化执行长时域任务一直是机器人学的核心研究课题。基于优化的动作规划是创建给定任务完成方案的有效手段。构建可靠的规划方法需要设计条件（例如避免物体碰撞）的构建流程。然而，该设计流程存在两个关键问题：1）迭代试错——因条件修改的试错过程导致设计耗时长；2）人工重设计——手动覆盖所有必要条件具有困难。针对这些问题，本文提出一种未来预测型成功/失败分类方法以实现条件自动获取。该方法的核心理念是采用端到端方式判断动作方案能否完成给定任务，而非手动重设计条件。所提方法利用长时域未来预测技术，无需实际执行动作方案即可完成成功/失败分类。本文还提出名为过渡一致性正则化的正则化项，用于生成易于预测的特征分布。该正则化项能提升未来预测与分类性能。通过分类实验与机械臂操作实验验证了方法的有效性。