Collaborative Goal and Policy Learning from Human Operators of Construction Co-Robots

Human operators of real-world co-robots, such as excavator, require extensive experience to skillfully handle these complicated machines in uncertain safety-critical environments. We consider the problem of human-robot collaborative learning and task execution, where efficient human-robot interaction is critical to safely and efficiently accomplish complex tasks in uncertain environments. Our collaborative learning algorithm enables a construction co-robot to learn latent task subgoals from the demonstrations of skilled human operators which can then be used to guide novice human operators in completing complex tasks under uncertainty. The effectiveness our algorithm is demonstrated through experimentation on a scaled model of an excavator with guided and unguided human operators. Our results demonstrate that when the co-robot’s inferred subgoals are communicated back to the novice human operator, task performance significantly improves.

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Airborne Detection and Tracking of Geologic Leakage Sites

Safe storage of CO2 to reduce greenhouse gas emissions without adversely affecting energy use or hindering economic growth requires development of monitoring technology that is capable of validating storage permanence while ensuring the integrity of sequestration operations. Soil gas monitoring has difficulty accurately distinguishing gas flux signals related to leakage from those associated with meteorologically driven changes of soil moisture and temperature. Integrated ground and airborne monitoring systems are being deployed capable of directly detecting CO2 concentration in storage sites. Two complimentary approaches to detecting leaks in the carbon sequestration fields are presented. The first approach focuses on reducing the requisite network communication for fusing individual Gaussian Process (GP) CO2 sensing models into a global GP CO2 model. The GP fusion approach learns how to optimally allocate the static and mobile sensors. The second approach leverages a hierarchical GP-Sigmoidal Gaussian Cox Process for airborne predictive mission planning to optimally reducing the entropy of the global CO2 model. Results from the approaches will be presented.

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Human Aware UAS Path Planning in Urban Environments using Nonstationary MDPs

A growing concern with deploying Unmanned Aerial Vehicles (UAVs) in urban environments is potential violation of human privacy, and the backlash this could entail. Therefore, there is a need for UAV path planning algorithms that minimize the likelihood of invading human privacy. Such algorithms would be useful for pipeline and agricultural survey, wildfire monitoring and other missions where surveillance of humans should be avoided. We formulate the problem of human-aware path planning as a nonstationary Markov Decision Process, and provide a novel model-based reinforcement learning solution that leverages Gaussian process clustering. Our algorithm is flexible enough to accommodate changes in human population densities, and is real-time computable, as opposed to competing approaches employing Bayesian nonparametrics. The approach is validated experimentally on a large-scale long duration experiment with both simulated and real UAVs.

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Method and Apparatus for Testing Quality of Seal and Package Integrity

Embodiments relate to a method and apparatus for determining information relating to a leak in a package. In an embodiment, a solenoid/gravity system is used to rapidly pressurize a flexible package to a desired pressure and to rapidly withdraw the pressurizing agent, where another solenoid is used to rapidly and retractably impact a region on the package under test. Sensors are used to sense data corresponding to a wave in the package generated from the region of impact. The data is acquired and processed to determine information regarding a leak in the package, such as whether there is a leak in the package under test, the size of the leak, and/or the location of the leak.


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