Robots need to effectively interact with a large variety of objects that appear in warehouses and factories as well as homes and offices. This requires robust grasping and dexterous manipulation of everyday objects through low cost robots and low complexity solutions. Traditionally, robots use rigid hands and analytical models for such tasks, which often fail in the presence of even small errors. New compliant hands promise improved performance, while minimizing complexity, and increased robustness. Nevertheless, they are inherently difficult to sense and model. This project combines ideas from different robotics sub-fields to address this limitation. It utilizes progress in machine learning and builds on a strong tradition in robot modeling. The objective is to provide adaptive, compliant robots that are better in grasping objects in the presence of multiple unknown contact points and sliding or rolling objects in-hand. The broader impact will be strengthened by the open release of new or modified robot hand designs, improved control algorithms and software, as well as corresponding data sets. Furthermore, academic dissemination will be accompanied by educational outreach to undergraduate and high school students.
Towards the above objective, the first step will be the definition of new hybrid models appropriate for adaptive, compliant hands. This will happen by improving analytical solutions and extending them to allow adaptation based on data via novel, time-efficient learning methods. The objective is to capture model uncertainty inherent in real-world interactions; a process that suffers from data scarcity. In order to reduce the amount of data required for learning, different models will be tailored to specific tasks through an automated discovery of these tasks and of underlying motion primitives for each one of them. This task identification process will operate iteratively with learning and utilize improved models to discover new tasks. It can also provide feedback for improved hand design. Once these learning-based and task-focused models are available, they will be used to learn and synthesize controllers for grasping and in-hand manipulation. To learn controllers, this work will consider a model-based, reinforcement learning approach, which will be evaluated against alternatives. For controller synthesis, existing tools for this purpose will be integrated with task planning primitives and extended through learning processes to identify the preconditions under which different controllers can be chained together. The project involves extensive evaluation on a variety of novel adaptive hands and robotic arms designed in the PIs' labs. Modern vision-based solutions will be used to track grasped objects and provide feedback for learning and closed-loop control. The evaluation will measure whether the developed hybrid models can significantly improve robustness of grasping and the effectiveness of dexterous manipulation.
