Where To Start? Transferring Simple Skills to Complex Environments
This addresses the challenge of deploying pre-trained robot skills in real-world, cluttered environments, which is an incremental improvement over existing methods.
The paper tackles the problem of transferring simple robot skills like grasping from open environments to cluttered ones, and demonstrates that their method enables collision-free execution in unseen complex settings for grasping and placing tasks.
Robot learning provides a number of ways to teach robots simple skills, such as grasping. However, these skills are usually trained in open, clutter-free environments, and therefore would likely cause undesirable collisions in more complex, cluttered environments. In this work, we introduce an affordance model based on a graph representation of an environment, which is optimised during deployment to find suitable robot configurations to start a skill from, such that the skill can be executed without any collisions. We demonstrate that our method can generalise a priori acquired skills to previously unseen cluttered and constrained environments, in simulation and in the real world, for both a grasping and a placing task.