Future Predictive Success-or-Failure Classification for Long-Horizon Robotic Tasks

• URL: http://arxiv.org/abs/2404.03415v1
• Date: Thu, 4 Apr 2024 12:49:42 GMT
• Title: Future Predictive Success-or-Failure Classification for Long-Horizon Robotic Tasks
• Authors: Naoya Sogi, Hiroyuki Oyama, Takashi Shibata, Makoto Terao,
• Abstract summary: This paper proposes a future-predictive success-or-failure-classification method to obtain conditions automatically. The proposed method uses a long-horizon future-prediction method to enable success-or-failure classification without the execution of an action plan. The effectiveness of our method is demonstrated through classification and robotic-manipulation experiments.
• Score: 6.844121549749507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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