A goal of human-robot interaction is to allow one user to operate multiple robots simultaneously. In such a scenario the robots provide leverage to the user's attention. The number of such robots that can be operated is called the fan-out of a human-robot team. Robots that have high neglect tolerance and lower interaction time will achieve higher fan-out. We define an equation that relates fan-out to a robot's activity time and its interaction time. We describe how to measure activity time and fan-out. We then use the fan-out equation to compute interaction effort. We can use this interaction effort as a measure of the effectiveness of a human-robot interaction design. We describe experiments that validate the fan-out equation and its use as a metric for improving human-robot interaction.

Fan-out: measuring human control of multiple robots

A brain-based device (BBD) having a physical mobile device NOMAD controlling and under control by a simulated nervous system The simulated nervous system is based on an intricate anatomy and physiology of the hippocampus and its surrounding neuronal regions including the cortex The BBD integrates spatial signals from numerous objects in time and provides flexible navigation solutions to aid in the exploration of unknown environments As NOMAD navigates in its real world environment, the hippocampus of the simulated nervous system organizes multi-modal input information received from sensors on NOMAD over timescales and uses this organization for the development of spatial and episodic memories necessary for navigation

Mobile brain-based device having a simulated nervous system based on the hippocampus

Autonomous robots hold the possibility of performing a variety of assistive tasks in intelligent environments. However, widespread use of robot assistants in these environments requires ease of use by individuals who are generally not skilled robot operators. In this paper we present a method of training robots that bridges the gap between user programming of a robot and autonomous learning of a robot task. With our approach to variable autonomy, we integrate user commands at varying levels of abstraction into a reinforcement learner to permit faster policy acquisition. We illustrate the ideas using a robot assistant task, that of retrieving medicine for an inhabitant of a smart home.

https://www.eecs.wsu.edu/~cook/pubs/iros03.pdf

User-guided reinforcement learning of robot assistive tasks for an intelligent environment

To make the transition from a technological curiosity to productive tools, humanoid robots will require key advances in many areas, including, mechanical design, sensing, embedded avionics, power, and navigation. Using the NASA Johnson Space Center's Robonaut as a testbed, the DARPA mobile autonomous robot software (MARS) humanoids team is investigating technologies that will enable humanoid robots to work effectively with humans and autonomously work with tools. A novel learning approach is being applied that enables the robot to learn both from a remote human teleoperating the robot and an adjacent human giving instruction. When the remote human performs tasks teleoperatively, the robot learns the salient sensory-motor features to executing the task. Once learned, the task may be carried out fusing the skills required to perform the task, guided by on-board sensing. The adjacent human takes advantage of previously learned skills to sequence the execution of these skills. Preliminary results from initial experiments using a drill to tighten lug nuts on a wheel are discussed.

/pdf/building-an-autonomous-humanoid-tool-user-24pyiyn9ny.pdf

Building an autonomous humanoid tool user

This paper describes a method to build a learning system for mobile robots based on the fuzzy control technique. Range sensors are analyzed and used. The learning system generates a set of fuzzy rules based on the robot's behaviors. Two examples illustrate the learning capabilities: 1) learn-to-avoid-obstacle, and 2) learn-to-detect-landmark behaviors. Reinforcement learning is applied to correct the fuzzy rules. Fuzzy controllers are used to control the robot using a set of fuzzy rules. The results of learn-to-avoid behavior indicate a good learning performance in dynamic environments. The results of learn-to-detect-landmark behavior show the robot can detect landmarks correctly from 68.43% to 90.09%.

Behavior-based learning fuzzy rules for mobile robots

Describes the supervisory control of mobile robots using a biologically inspired short-term memory structure called a Sensory EgoSphere. The EgoSphere is implemented as a virtual geodesic dome upon which sensory data from the surroundings of the robot are written. The EgoSphere is managed by a distinct agent in a distributed agent-based robot control architecture called the Intelligent Machine Architecture. The paper also describes a human-robot interface and a testbed for evaluating the control system.

Supervisory control of mobile robots using Sensory EgoSphere

Our research achievements under the NASA-JSC grant contributed significantly in the following areas. Multi-agent based robot control architecture called the Intelligent Machine Architecture (IMA) : The Vanderbilt team received a Space Act Award for this research from NASA JSC in October 2004. Cognitive Control and the Self Agent : Cognitive control in human is the ability to consciously manipulate thoughts and behaviors using attention to deal with conflicting goals and demands. We have been updating the IMA Self Agent towards this goal. If opportunity arises, we would like to work with NASA to empower Robonaut to do cognitive control. Applications 1. SES for Robonaut, 2. Robonaut Fault Diagnostic System, 3. ISAC Behavior Generation and Learning, 4. Segway Research.

/pdf/acquisition-of-autonomous-behaviors-by-robotic-assistants-38v0unjyw3.pdf

Acquisition of Autonomous Behaviors by Robotic Assistants

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Supervisory control of mobile robots using Sensory EgoSphere

Acquisition of Autonomous Behaviors by Robotic Assistants