3 accepted papers
Intelligent and reliable task planning is a core capability for generalized robotics, which requires a descriptive domain representation that sufficiently models all object and state information for the scene. We present CLIMB, a continual learning framework for robot task planning that leverages fo
In order to accomplish various missions, autonomous ground vehicles must operate on a wide range of terrain. While many systems such as wheels and whegs can navigate some types of terrain, none are optimal across all. This creates a need for physical adaptation. This paper presents a broad new appro…
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 ho