Robots have the potential to assist people with a variety of routine tasks in homes and workplaces. From assisting a person with an activity of daily living (such as cooking or cleaning) to assisting a small business owner with a small-scale manufacturing task, assistive robots need to be capable of planning and executing motions in unstructured environments that may contain unforeseen obstacles. Further complicating the planning challenge, many assistive tasks involve significant constraints on motion. For example, when carrying a plate of food, a person knows that tilting the plate sideways, while feasible, is undesirable because it will spill the food. In order to autonomously and safely accomplish many assistive tasks, a robot must be aware of such task constraints and must plan and execute motions that consider these constraints while avoiding obstacles.
We are developing demonstration-guided motion planning (DGMP), a framework which enables robotic manipulators to compute motion plans that (1) avoid obstacles in unstructured environments and (2) aim to satisfy learned features of the motion that are required for the task to be successfully accomplished. At the core of DGMP is an asymptotically optimal sampling-based motion planner that computes motion plans that are both collision-free and globally minimize a cost metric that encodes learned features of the motion. The motivation for our cost metric is that if the robot is shown multiple demonstrations of a task in various settings, features of the demonstrations that are consistent across all the demonstrations are likely to be critical to task success, while features that vary substantially across the demonstrations are likely unimportant.
We have demonstrated the effectiveness of DGMP using the Aldebaran Nao robot and the Rethink Robotics Baxter robot performing simple household tasks in cluttered environments, including transferring powder from a container to a bowl, wiping the surface of a table, and pushing a button.
We are also investigating methods for enabling the robot to learn tasks using less human-provided information. For example, in recent work the robot automatically learns which objects in the environment (and specific features of those objects) are relevant to successfully performing the task.
This research is made possible by support from the National Science Foundation (NSF) under awards IIS-1117127, IIS-1149965, CNS-1305286, and CCF-1533844. Any opinions, findings, and conclusions or recommendations expressed on this web site do not necessarily reflect the views of NSF.