Gaussian Process based Subsumption of a Parasitic Control Component

Many existing control architectures assume that the main control system being designed is the only controller that governs a system's actuators. However, with the increasing availability of off-the shelf controls packages, the number of internal unadjustable control systems is increasing. Some of these control systems may behave in parasitic way by enforcing a rigid set of behaviors that could disrupt a desired system behavior. We present a control architecture that can subsume parasitic control behavior through iteratively shaping the main control command with an intelligent feed-forward term. Our architecture requires very little prior knowledge about the subsystem whose behavior is to be subsumed, rather it relies on online learned sparsified predictive Gaussian Process (GP) models. We provide rigorous quantifiable bounds relating the sparsification of the GP to the accuracy in estimating and subsuming the parasitic subsystem. The presented subsumption architecture is realized using a variant of D-Type iterative learning control (ILC) and is validated through a series of flight tests on a Parrot AR Drone 2.0 quadrotor where the quadrotor's sonar based altitude control loop's behavior of maintaining a fixed altitude over ground surfaces is subsumed through a main controller via a feed-forward term.

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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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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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