In collaborative tasks, people often communicate using nonverbal gestures to coordinate actions. When two people reach for the same object at the same time, they often respond to an imminent potential collision with jerky halting hand motions that we term hesitation gestures. Successful implementation of such communicative conflict response behaviour onto robots can be useful. In a myriad of human-robot interaction contexts involving shared spaces and objects, this behaviour can provide a fast and effective means for robots to express awareness of conflict and cede right-of-way during collaborative work with users. Our previous work suggests that when a six-degree-of-freedom (6-DOF) robot traces a simplified trajectory of recorded human hesitation gestures, these robot motions are also perceived by humans as hesitation gestures. In this work, we present a characteristic motion profile derived from the recorded human hesitation motions, called the Acceleration-based Hesitation Profile (AHP). We test its efficacy to generate communicative hesitation responses by a robot in a fast-paced human-robot interaction experiment.

In this study, we investigate and characterize haptic interaction in human-to-human handovers and identify key features that facilitate safe and efficient object transfer. Eighteen participants worked in pairs and transferred weighted objects to each other while we measured their grip forces and load forces. Our data show that during object transfer, both the giver and receiver employ a similar strategy for controlling their grip forces in response to changes in load forces. In addition, an implicit social contract appears to exist in which the giver is responsible for ensuring object safety in the handover and the receiver is responsible for maintaining the efficiency of the handover. Compared with prior studies, our analysis of experimental data show that there are important differences between the strategies used by humans for both picking up/placing objects on table and that used for handing over objects, indicating the need for specific robot handover strategies as well. The results of this study will be used to develop a controller for enabling robots to perform object handovers with humans safely, efficiently, and intuitively.

In the near future, as robots become more advanced and affordable, we can envision their use as intelligent assistants in a variety of domains. An exemplar human-robot task identified in many previous works is cooperatively carrying a physically large object. An important task objective is to keep the carried object level. In this work, we propose an admittance-based controller that maintains a level orientation of a cooperatively carried object. The controller raises or lowers its end of the object with a human-like behavior in response to perturbations in the height of the other end of the object (e.g., the end supported by the human user). We also propose a novel tuning procedure, and find that most users are in close agreement about preferring a slightly under-damped controller response, even though they vary in their preferences regarding the speed of the controller's response.

Unwanted conflicts are inevitable between collaborating agents that share spaces and resources. Motivated by the use of nonverbal communications as a conflict resolution mechanism by humans, this study investigates the communicative capabilities reflected in the trajectory characteristics of hesitation gestures during human-robot collaboration. Hesitation gestures and non-hesitation human arm motions were recorded from a series of reach-and-retract tasks and embodied on a 6-DOF robot arm. A total of 86 survey respondents watched and scored recordings of these motions according to whether they recognized hesitation gestures as exhibited by both the human and the robot. Using the survey's statistical evidence indicating that hesitation trajectories embodied in an articulated robot arm can be recognized by human observers, we identified trajectory characteristics of hesitation gestures. The contribution of our work is an empirically grounded robot trajectory specification that provides communicative cues for conflict resolution during collaborative reaching scenarios.

Intelligent robot assistants will be simpler for laypersons to use if the robots are able to cooperate with their users anthromimetically. However, designing an anthromimetic robot controller requires knowledge of human behavior in the domain of interest. Previous works have identified cooperative carrying as an ideal task for robotic assistants, but studies of human behavior in this domain are few. In this paper, we present an experimental study of nineteen non-expert human subjects cooperatively carrying a long object with a robot partner. Our results demonstrate that human cooperative carrying behavior is much more complex than previously supposed, which has significant implications on the development of intelligent, physically interactive robot assistants.

Behavior-based controllers for complex missions often are more easily designed by decomposing the mission into a series of smaller subtasks. When applying this technique to a multiple-robot system, the entire system should focus its work on one subtask at a time to prevent interference from robots working on conflicting subtasks simultaneously. We present a behavior-based approach to the task-sequencing problem for decentralized multiple-robot systems requiring only local inter-robot communication. Consensus is used to prevent premature task sequencing. Our algorithm is compared to globally-communicative and communication-free strategies. The proposed task-sequencing behavior is found to be efficient with respect to time and energy requirements, and could be applied to many existing decentralized multiple-robot systems.

In this paper, we present a physical implementation of random peer-to-peer (RP2P) communication for use in a multiple-robot system and analyze its performance. Traditionally, multiple-robot systems have either broadcast all of their inter-robot communication or have avoided explicit communication altogether. RP2P communication, on the other hand, allows efficient system-level communication while retaining the error-correction capabilities of peer-to-peer connections. We demonstrate that RP2P communication can be implemented with off-the-shelf components. MRS as large as ten robots are investigated and it is demonstrated that message rates as high as 50 messages/second are easily achievable using TCP connections and 802.11B wireless network interfaces.

Inter-robot communication is essential if general purpose intelligent decentralized multiple-robot systems are to become a reality. Traditionally, explicit communication amongst the robots of a MRS has been broadcasted or eliminated altogether. The limits of these two approaches prevent the widespread deployment of useful decentralized MRS. In this paper, we present and analyze, both analytically and empirically, a communication protocol that we call random peer-to-peer or RP2P. Using RP2P, a decentralized MRS can share state in logarithmic time with respect to system population. Additionally, the load placed upon the individual robots by RP2P is independent of system population size. Potential applications of random peer-to-peer communication also are provided, including state sharing, decision-making and task allocation.

Just like their solitary counterparts, multiple-robot systems must be able to make decisions in response to their environment. However, with a multiple-robot system, one must take care to ensure that the individual robots that compose a system make their decisions in concert with each other. We desire decisions to be made at the system level. In this paper, we investigate four different mechanisms to allow individual robots within a system to express their preference for a particular solution to a system-level problem. All four mechanisms consistently produced unanimous decisions, but had varying ability to produce unanimous decisions of good quality. An approach that we refer to as passive expression of preference performed the best, but had to be tuned to the particular problem being solved. A mechanism that we refer to as active expression of preference exhibited very good performance and required no problem specific tuning, which makes it more universally applicable to the multiple-robot decision-making problem.

Practical collective robotic systems likely will be confronted with problems which have more than one unique solution. When deciding on which of a set of candidate solutions to a problem to pursue, a collective system should ensure that its members reach a unanimous decision regarding which solution to implement so that the system itself does not split apart with different members pursuing different solutions. If such a split were to occur, much of the collective system's functionality could be lost. In this paper, we present a unique approach to collective decision making that is based on an algorithm employed by a particular species of ant when it chooses a new nest site. We expand the ants' algorithm into a general purpose decision making scheme and apply it to the collective relocation problem. A detailed study of the performance of our decision making algorithm was carried out in simulation using the collective relocation task as a test bed. Consistent system performance was observed across three robot populations. It was found that one particular system variable, the decision quorum threshold played a large role in determining the system's behaviour and that system behaviour was maximized when this variable was set to 50% of the system's population.

In this paper, we present a collective, or swarm construction algorithm to control robotic bulldozers in the creation of a work site. Predictions about robotic missions to the planet Mars have described such site preparation as essential to the success of later mission objectives, such as the construction of solar arrays, etc. This algorithm was based on a behaviour observed in a particular species of ant called "blind bulldozing". We developed a mathematical model of blind bulldozing using a unique approach based on Markov chains. Robot bulldozers were developed and used to test the algorithms in our laboratory. The team of robots was found to be successful at clearing an open area out of field of rocks. Our robots' behaviour also agreed with the predictions of our model. This work is significant because it demonstrated the viability of blind bulldozing and represents the first time, to our knowledge, that a multiple robot system has carried out a form of the general construction task outside of simulation.