In the real world, autonomous robots need to react to unexpected events such as action failures and exogenous events. In this paper, we focus on recognition of action results and error recovery. We implemented real-time robot planning system using Behavior Tree. It monitors the state and plans the order of actions and switches the plan depending on the success or failure of nodes. Therefore, it is necessary to recognize failures and to detect discrepancy in the plan caused by problems in determining success. We experimented Pick and Place using this system and show that it can error recovery when it failed to place.