Collaborative Research: Self-Identification for Robot Manipulation under Uncertainty Aided by Passive Adaptability

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This grant supports research that will contribute novel knowledge at the intersection of robotic manipulation and active perception, promoting both the progress of science and the advancement of national prosperity. To enable general-purpose robots that can offer sophisticated physical interactions with the world, the capability of robots to work under limited perception is essential. However, as traditional approaches sequentially formulate perception and manipulation into decoupled system components, robot manipulation skills have been passively constrained by the perception system. This award supports research to establish a new paradigm for enabling the interactions between perception and manipulation, and will fundamentally transform the roles of both to actively facilitate each other. The key concept, termed as self-identification, is a process where robots start to manipulate objects without full knowledge of the system, or even of itself, while in the meantime creating opportunities for the perception component to acquire necessary information that were impossible otherwise. In turn, the manipulation capability is significantly upgraded with the extra information obtained. As this new ability can improve many real-world robot applications, such as industrial production, household services, and healthcare applications, the results from this research will benefit the U.S. economy and society. This research involves several topics ranging from computer science, mechanical engineering, and sensor technology, to control theory and artificial intelligence. The multi-disciplinary framework will broaden the participation of underrepresented groups and positively impact the engineering education.This project leverages various types of passive (or low-level) adaptability in robot manipulators, especially that coming from mechanical compliance (springs or soft structures), low-level impedance control, or underactuated mechanisms, to allow the robot to conduct exploratory motions that are externally observed and used within an adaptive estimation scheme to self-identify the system. The robot-object-environment system will be actively reconfigured while maintaining the desired states and stability in an essentially open-loop way, while external observations of these changes are used to generate online estimations and controllers. Essentially, it changes the traditional paradigm from “sense, plan, act” to “act, sense, plan”, with an emphasis on efficient and effective task execution under limited sensing. The research team will establish generic self-identification frameworks for model-complete, model-incomplete, and model-free manipulation systems to: 1) compensate for limited perception abilities, where needed, to accomplish tasks that are traditionally infeasible; 2) maximize their perception capabilities to further improve the system’s task-awareness and robustness; and 3) incorporate the results or the entire process of self-identification into manipulation planning and control to enable robust manipulation under physical and sensing limitations.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.