启发式满足人类和机器人的积极感知中的推断决策
Yucheng Chen1, Pingping Zhu2, Anthony Alers3
1Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, United States.
Frontiers in robotics and AI
|November 27, 2024
概括
机器人现在可以通过积极感知来调整他们的搜索策略,在最佳和启发式方法之间切换,在复杂的环境中成功,尽管有意想不到的挑战. 这提高了机器人在现实世界中完成任务的情况.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 决策算法在现实世界机器人中经常因为违反假设和外部压力而失败.
- 人类通过平衡最佳和启发式策略,利用环境线索,有效地应对不确定性.
研究的目的:
- 为机器人在压力下工作开发积极感知解决方案的总体方法.
- 研究和建模机器人如何在最佳和启发式决策之间进行调节.
主要方法:
- 在虚拟世界中模拟"寻宝"问题,以学习可概括的策略.
- 为配备相机的机器人开发了主动感知算法.
- 在高准确度的数值模拟和物理实验中测试算法.
主要成果:
- 新的积极感知算法在"寻宝"基准中表现优于现有方法 (细胞分解,信息路线图,信息潜力).
- 算法在时间限制,资源限制和恶劣天气 (雾) 等意想不到的条件下表现出有效性.
结论:
- 开发的积极感知策略使机器人能够调整他们的决策,在充满挑战的动态环境中成功完成任务.
- 这种方法为现实世界机器人应用提供了强大的解决方案,其中环境模型不完整或压力很高.
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