机器人
计算机科学
交错
稳健性(进化)
可观测性
任务(项目管理)
部分可观测马尔可夫决策过程
稳健性
运动规划
人工智能
人机交互
工程类
机器学习
操作系统
系统工程
化学
马尔可夫模型
生物化学
基因
马尔可夫链
应用数学
离群值
数学
作者
Shuo Yang,Xinjun Mao,Shuo Wang,Huaiyu Xiao,Yuanzhou Xue
标识
DOI:10.1109/icra48506.2021.9561756
摘要
Robots operating in open environments expect to have robust plans to achieve tasks successfully under environment uncertainties. However, both partial observability and dynamics of environment states have significantly decreased the robustness of task achievement, making robot task planning much more challenging. The partially observable states require the robot to obtain observations for optimally acting of the task goal. Also, state dynamics expects the robot to continuously observe surroundings for acting safely. Both challenges practically demand the purposeful and tight interactions between robot state-changing actuating actions and sensor-based observation actions. This paper proposes a novel model of Adjoint Sensing and Acting (ASA) that explicitly defines two parallel and sequential interaction schemes between actuating and observation actions, as well as an extended Behavior Tree for a concrete implementation of above schemes. We further propose an interleaving task planning approach for planning ASA-style plans, which integrates a deliberative POMDP planner for pursuing task goals, and a reactive Behavior Tree executive for fast responding to unexpected events. We experimentally demonstrate that ASA interaction schemes are practical and applicable to model and plan the open environment robot tasks. The plans from the interleaving task planning approach are both reactive in run-time response and efficient in task achievement.
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