Robots reason about abstract actions, such as go to position 'l', in order to decide what to do or to generate plans for their intended course of action. The use of abstract actions enables robots to employ small action libraries, which reduces the search space for decision making. When executing the actions, however, the robot must tailor the abstract actions to the specific task and situation context at hand.

In this article we propose a novel robot action execution system that learns success and performance models for possible specializations of abstract actions. At execution time, the robot uses these models to optimize the execution of abstract actions to the respective task contexts. The robot can so use abstract actions for efficient reasoning, without compromising the performance of action execution. We show the impact of our action execution model in three robotic domains and on two kinds of action execution problems: (1) the instantiation of free action parameters to optimize the expected performance of action sequences; (2) the automatic introduction of additional subgoals to make action sequences more reliable.

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Refining the execution of abstract actions with learned action models

We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.

Inferring high-level behavior from low-level sensors

Combining declarative, procedural, and predictive knowledge to generate, execute, and optimize robot plans

Training for the job.

By observing the execution of their actions, cognitive robots become aware of their behavior. We describe a system that acquires such knowledge, and uses it to reflect future actions to autonomously avoid failures and optimize future actions. The system has three types of motor control knowledge, which are represented at different levels of abstraction, and acquired in different ways: 1) declarative knowledge to select actions, 2) procedural knowledge to execute actions, and 3) predictive knowledge to optimize action executions with respect to execution duration and success. The robots acquire predictive knowledge autonomously, by learning it from observed experience. The generality of the approach is demonstrated by applying the methods to three robotic platforms: a Pioneer soccer robot, a simulated articulated B21 in a kitchen environment, and a PowerCube arm.

Learning Predictive Knowledge to Optimize Robot Motor Control

In motor control, mapping high-level goals to low-level motor commands often leads to ambiguities, as there are usually many ways a task can be achieved. The key to resolving such ambiguities, for both humans and robots, lies in prediction. In this dissertation, a novel approach for acquiring and applying predictive robot action models is presented. Action models are first learned from observed experience, and enable robots to predict the performance and outcome of their actions. These models are then used to tailor actions to the context they are being executed in, for instance to optimize action sequences, tailor actions to novel goals, or coordinate actions of several robots. The main principle behind this approach is that in robot controller design, knowledge that robots learn themselves from observed experience complements well the abstract knowledge that humans specify.

Tailoring Robot Actions to Task Contexts using Action Models

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Tailoring Robot Actions to Task Contexts using Action Models