Chris A. C. Parker

A human-inspired object handover controller

In this paper, we present a novel controller for safe, efficient, and intuitive robot-to-human object handovers and a set of experiments to evaluate user responses to the robot’s handover behavior. The controller enables a robot to mimic human behavior by actively regulating the applied grip force according to the measured load force during a handover. We provide an implementation of the controller on a Willow Garage PR2 robot, demonstrating the feasibility of realizing our design on robots with basic sensor/actuator capabilities. A user study comparing four variations of our controller shows that our design yields both human-like and human-preferred object handovers.

Grip forces and load forces in handovers: implications for designing human-robot handover controllers

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.

Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem

When a complex mission must be undertaken, it often can be simplified by dividing it into a sequence of smaller subtasks, which are then completed in order. This strategy implicitly requires a system to recognize the completion of each subtask and make the decision to begin work on the next one. Decentralized multiple-robot systems can tackle many tasks, but their behavior is typified by continuous responses to stimuli. Task sequencing, however, demands a controlled, self-induced phase change in collective behavior—working on one task one moment and then on a different task the next—which is nontrivial for an emergent system. The main contribution of this study is a collective decision-making framework for decentralized multiple-robot systems that enables such a system to cooperatively decide that a current task has been completed and thus focus its attention on the next one in a sequence using only anonymous local communication. Central to the framework is the use of consensus, whereby task sequencing is delayed until a prespecified proportion of a system’s robots agree that the current task is complete, reducing the likelihood of premature decisions. Two low-cost consensus estimation strategies are presented, both of which are practical for the extremely simple robots that are expected to compose large decentralized systems. Experiments in simulation and with real robots demonstrate that the proposed decision-making framework performs as predicted. Although the specific application of collective decision-making in this work is the cooperative task-sequencing problem, the proposed decision-making framework potentially has many additional applications.

Did you see it hesitate? - Empirically grounded design of hesitation trajectories for collaborative robots

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 surveys 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.

Biologically inspired collective comparisons by robotic swarms

Intelligent entities must often make decisions by comparing several candidate alternatives and selecting the best one. This is just as true for autonomous swarms as it is for solitary robots, but to date there has been little work to propose efficient comparison behaviors for autonomous robotic swarms that are not tied to specific environments. In this work, we examine an elegant collective comparison strategy that is used by at least three different species of social insect and adapt it for artificial systems. The behavior is particularly attractive for robotic implementations because it relies only on short range explicit peer-to-peer communication, eliminating the need for chemical trails or other forms of stigmergy. The proposed comparison strategy is proven to converge, and a series of experiments using real robots with noisy sensors is presented that validates our theoretical analysis. Using the proposed behavior, a robotic swarm is able to compare alternatives collectively more accurately than its member robots would be able to individually.

Design and impact of hesitation gestures during human-robot resource conflicts

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. Compared to traditional abrupt stopping behaviours, we did not find sufficient evidence that the AHP-based robot responses improve human perception of the robot or human-robot task completion time. However, results from our in situ experiment suggest that subjects can recognize AHP-based robot responses as hesitations and distinguish them to be different from abrupt stopping behaviours.

Experimental investigation of human-robot cooperative carrying

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.

Measuring intent in human-robot cooperative manipulation

To effectively interact with people in a physically assistive role, robots will need to be able to cooperatively manipulate objects with a human partner. For example, it can be very difficult for an individual to manipulate a long or heavy object. An assistant can help to share the load, and improve the maneuverability of the object. Each partner can communicate objectives (e.g., move around an obstacle or put the object down) via non-verbal cues (e.g., moving the end of the object in a particular direction, changing speed, or tugging). Herein, non-verbal communication in a human-robot coordinated manipulation task is addressed using a small articulated robot arm equipped with a 6-axis wrist mounted force/torque sensor and joint angle encoders. The robot controller uses a Jacobian Transpose velocity PD control scheme with gravity compensation. To aid collaborative manipulation we implement a uniform impedance controller at the robot end-effector with an attractive force to a virtual path in the style of a cobot. Unlike a cobot, this path is recomputed online as a function of user input. In our present research, we utilize force/torque sensor measurements to identify intentional user communications specifying a change in the task direction. We consider the impact of path recomputation and the resulting robot haptic feedback on user physiological response.

Active versus passive expression of preference in the control of multiple-robot decision-making

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.

A Practical Implementation of Random Peer-to-Peer Communication for a Multiple-Robot System

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.