S&AS: INT: COLLAB: Composable and Verifiable Design for Autonomous Humanoid Robots in Space Missions

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Future space missions will increasingly rely on autonomous robots like the NASA Valkyrie human-centered robot for deploying equipment, assisting astronauts, and maintaining facilities in real world partially-observable and cluttered environments. Despite significant progress in robotic mobility, manipulation, and perception, there has been relatively little progress on providing formal performance guarantees for these integrated systems. Formal guarantees are critical for achieving long term autonomy, particularly for robots performing complex tasks requiring successful execution of multiple component subtasks. Thus, the goal of this project is to develop performance guarantees for space robots operating in unstructured real world environments. Although robots are used as design examples, the project is of a basic research nature and the results can have impacts on other fields, such as sensor/actuator networks, manufacturing and transportation systems. The multidisciplinary approach taken for this project will help broaden participation of underrepresented groups and positively impact engineering and computer science education.The objective of this project is to develop new methods to synthesize coordinated manipulation and locomotion plans and control policies that verifiably adhere to formal mission specifications. There are two major thrusts. First, the PIs plan to develop manipulation, locomotion, and motion primitives that can provide performance guarantees in unstructured, partially observable, and dynamic environments. The focus will be on using methods from perception and planning under uncertainty to provide guarantees in cluttered and partially observable environments. The PIs will also leverage new tools from hybrid systems and sampling based methods to achieve controllers with verifiable guarantees through contact mode switches. Second, the PIs plan to devise methods to automatically synthesize mission plans in a way that can guarantee the accomplishment of high-level mission goals or bound the probability of failure. The focus will be on automatic and learning-based design, enabling the system to adapt to changing environments, uncertain faults and potential adversaries. Most of the work performed under this project will be demonstrated in the context of complex space tasks inspired by NASA scenarios.