Kevin R. Dixon

RAVE: a real and virtual environment for multiple mobile robot systems

To focus on the research issues surrounding collaborative behavior in multiple mobile-robotic systems, a great amount of low-level infrastructure is required. To facilitate our on-going research into multi-robot systems, we have developed RAVE, a software framework that provides a real and virtual environment for running and managing multiple heterogeneous mobile-robot systems. This framework simplifies the implementation and development of collaborative robotic systems by providing the following capabilities: the ability to run systems off-line in simulation, user-interfaces for observing and commanding simulated and real robots, transparent transference of simulated robot programs to real robots, the ability to have simulated robots interact with real robots, and the ability to place virtual sensors on real robots to augment or experiment with their performance.

Learning by observation with mobile robots: a computational approach

We present a computational approach to learning by observation (LBO) that allows users to program mobile robots by demonstrating a task. Unlike previous approaches, our system incorporates statistical-learning techniques and concepts from control theory to reduce the amount of domain knowledge needed to infer the intent of the user. To improve the generalization ability of the system, the user can demonstrate the task multiple times. We extract task subgoals from these demonstrations and automatically associate them with objects in the environment. As these objects move, the subgoals are updated accordingly. This gives our system the ability to learn from demonstrations performed in different environments. In this paper, we present the concepts used in our LBO system as well as experimental laboratory results in learning motor-skill tasks.

Port-based adaptable agent architecture

To facilitate the design of large-scale, self-adaptive systems, we have developed the Port-Based Adaptable Agent Architecture. This distributed, multiagent architecture allows systems to be created with the flexibility and modularity required for the rapid construction of software systems that analyze and dynamically modify themselves to improve performance. This architecture provides user-level access to the three forms of software adaptability: parametric fine tuning, algorithmic change, and code mobility. In this paper, we present the architecture, describe port-based agents, and outline several applications where this flexible architecture has proven useful.

Trajectory representation using sequenced linear dynamical systems

In this paper we present a novel approach for representing trajectories using sequenced linear dynamical systems. This method uses a closed-form least-squares procedure to fit a single linear dynamical system (LDS) to a simple trajectory. These LDS estimates form the elemental building blocks used to describe complicated trajectories through an automatic segmentation procedure that can represent complicated trajectories with high accuracy. Each estimated LDS induces a control law, mapping current state to desired state, that encodes the target trajectory in a generative manner. We provide a proof of stability of the control law and show how multiple trajectories can be incorporated to improve the generalization ability of the system.

Predictive Robot Programming: Theoretical and Experimental Analysis

As the capabilities of manipulator robots increase, they are performing more complex tasks. The cumbersome nature of conventional programming methods limits robotic automation due to the lengthy programming time. We present a novel method for reducing the time needed to program a manipulator robot: predictive robot programming (PRP). The PRP system constructs a statistical model of the user by incorporating information from previously completed tasks. Using this model, the PRP system computes predictions about where the user will move the robot. The user can reduce programming time by allowing the PRP system to complete the task automatically. In this paper, we derive a learning algorithm that estimates the structure of continuous-density hidden Markov models from tasks the user has already completed. We analyze the performance of the PRP system on two sets of data. The first set is based on data from complex, real-world robotic tasks. We show that the PRP system is able to compute predictions for about 25% of the waypoints with a median prediction error less than 0.5% of the distance traveled during prediction. We also present laboratory experiments showing that the PRP system results in a significant reduction in programming time, with users completing simple robot-programming tasks over 30% faster when using the PRP system to compute predictions of future positions.

Software systems facilitating self-adaptive control software

Self-adaptive control software is a new paradigm to create robust, fault-tolerant mobile robots. This type of software analyzes its performance and dynamically modifies itself to operate better in adverse and rapidly changing conditions. We have created two systems that facilitate the creation of self-adaptive control software: PB3A and RAVE. PB3A, the Port-Based Adaptable Agent Architecture, is a mobile, agent-based framework that allows software to adapt itself at all levels. RAVE, the Real And Virtual Environment, is a mixed-reality simulation environment for mobile robots. Together these two systems allow for the creation, testing, and analysis of self-adaptive control software by on- and off-line simulation. We give brief overviews of PB3A and RAVE and present applications that demonstrate robotic systems using self-adaptive control software.

Predictive robot programming

One of the main barriers to automating a particular task with a robot is the amount of time needed to program the robot. Decreasing the programming time would facilitate automation in domains previously off limits. In this paper, we present a novel method for leveraging the previous work of a user to decrease future programming time: predictive robot programming. The decrease in programming time is accomplished by predicting waypoints in future robot programs and automatically moving the manipulator end-effector to the predicted position. To this end, we develop algorithms that construct simple continuous-density hidden Markov models by a state-merging algorithm based on waypoints from prior robot programs. We then use these models to predict the waypoints in future robot programs. While the focus of this paper is the application of predictive robot programming, we also give an overview of the underlying algorithms used and present experimental results.

Programming complex robot tasks by prediction: experimental results

One of the main obstacles to automating production is the time needed to program the robot. Decreasing the programming time would increase the appeal of automation in many industries. In this paper we analyze the performance of a Predictive Robot Programming (PRP) system on complex, real-world robotic tasks. The PRP system attempts to decrease programming time by predicting the waypoints of a robot program based on previous examples of user behavior. We show that the PRP system is able to generate a large percentage of useful and highly accurate predictions, resulting in a potentially great amount of time saved.