Visible to the public CPS: Medium: Learning to Sense Robustly and Act Effectively

Project Details
Lead PI:Benjamin Kuipers
Co-PI(s):silvio savarese
Performance Period:09/01/09 - 08/31/14
Institution(s):University of Michigan Ann Arbor
Sponsor(s):National Science Foundation
Award Number:0931474
3055 Reads. Placed 12 out of 803 NSF CPS Projects based on total reads on all related artifacts.
Abstract: The physical environment of a cyber-physical system is unboundedly complex, changing continuously in time and space. An embodied cyber-physical system, embedded in the physical world, will receive a high bandwidth stream of sensory information, and may have multiple effectors with continuous control signals. In addition to dynamic change in the world, the properties of the cyber-physical system itself ? its sensors and effectors ? change over time. How can it cope with this complexity? The hypothesis behind this proposal is that a successful cyber-physical system will need to be a learning agent, learning the properties of its sensors, effectors, and environment from its own experience, and adapting over time. Inspired by human developmental learning, the assertion is that foundational concepts such as Space, Object, Action, etc., are essential for such a learning agent to abstract and control the complexity of its world. To bridge the gap between continuous interaction with the physical environment, and discrete symbolic descriptions that support effective planning, the agent will need multiple representations for these foundational domains, linked by abstraction relations. To achieve this, the team is developing the Object Semantic Hierarchy (OSH), which shows how a learning agent can create a hierarchy of representations for objects it interacts with. The OSH shows how the ?object abstraction? factors the uncertainty in the sensor stream into object models and object trajectories. These object models then support the creation of action models, abstracting from low-level motor signals. To ensure generality across cyber-physical systems, these methods make only very generic assumptions about the nature of the sensors, effectors, and environment. However, to provide a physical test bed for rapid evaluation and refinement of our methods, the team has designed a model laboratory robotic system to be built from off-the-shelf components, including a stereo camera, a pan-tilt-translate base, and a manipulator arm. For dissemination and replication of research results, the core system will be affordable and easily duplicated at other labs. There are plans to distribute the plans, the control software, and the software for experiments, to encourage other labs to replicate and extend the work. The same system will serve as a platform for an open-ended set of undergraduate laboratory tasks, ranging from classroom exercises, to term projects, to independent study projects. There is a preliminary design for a very inexpensive version of the model cyberphysical system that can be constructed from servo motors and pan-tilt webcams, for use in collaborating high schools and middle schools, to communicate the breadth and excitement of STEM research.