Combining task and path planning for a humanoid two-arm robotic system

Abstract

The ability to perform both causal (means-end) and ge- ometric reasoning is important in order to achieve au- tonomy for advanced robotic systems. In this paper, we describe work in progress on planning for a humanoid two-arm robotic system where task and path planning capabilities have been integrated into a coherent plan- ning framework. We address a number of challenges of integrating combined task and path planning with the complete robotic system, in particular concerning perception and execution. Geometric backtracking is considered: this is the process of revisiting geometric choices (grasps, positions etc.) in previous actions in order to be able to satisfy the geometric preconditions of the action presently under consideration of the plan- ner. We argue that geometric backtracking is required for resolution completeness. Our approach is demon- strated on a real robotic platform, Justin at DLR, and in a simulation of the same robot. In the latter, we con- sider the consequences of geometric backtracking