Affordance-feasible planning with manipulator wrench spaces

Abstract

This work introduces an affordance characterization employing mechanical wrenches as a metric for predicting and planning with workspace affordances. Although affordances are a commonly used high-level paradigm for robotic task-level planning and learning, the literature has been sparse regarding how to characterize the agent in this object-agent-environment framework. In this work, we propose decomposing a behavior into a vocabulary of characteristic requirements and capabilities that are suitable to predict the affordances of various parts of the workspace. Specifically, we investigate mechanical wrenches as a viable representation of these affordance requirements and capabilities. We then use this vocabulary in a planning system to compose complex motions from simple behavior types in continuous space. The utility of the framework for complex planning is demonstrated on example scenarios both in simulation and with real-world industrial manipulators.