UC San Diego

Tool use and improvisational tool use is a hallmark of human (and animal) intelligence. When an appropriate tool for a task is not available, we can innovate and design a new tool, or use an existing object in a non-canonical way to accomplish the task, by reasoning about the underlying nature of the task. Despite the impressive capabilities of robots to learn narrow yet complex tasks using tools, innovative tool use by robots still remains an open and a significant challenge. We seek to address this challenge in the context of causal affordance based planning for the robot to reason about the constraints of the task, and thereby select the appropriate tool and its usage. This is done through providing semantic annotations to task preconditions in a Planning Domain Definition Language (PDDL) framework, derived from the inherent constraints of the task. Subsequently, we extend a standard planning algorithm to exploit these semantics, and demonstrate its application in improvisational tool use.