An official website of the United States government
Supervisory control of a humanoid robot in a manipulation task requires coordination of remote perception with robot action, which becomes more demanding with multiple moving cameras available for task supervision. We explore the use of autonomous camera control and selection to reduce operator workload and improve task performance in a supervisory control task. We design a novel approach to autonomous camera selection and control, and evaluate the approach in a user study which revealed that autonomous camera control does improve task performance and operator experience, but autonomous camera selection requires further investigation to benefit the operator’s confidence and maintain trust in the robot autonomy.
more »
« less
A Diagnostic Human Workload Assessment Algorithm for Human-Robot Teams
High-stress environments, such as a NASA Control Room, require optimal task performance, as a single mistake may cause monetary loss or the loss of human life. Robots can partner with humans in a collaborative or supervisory paradigm. Such teaming paradigms require the robot to appropriately interact with the human without decreasing either»s task performance. Workload is directly correlated with task performance; thus, a robot may use a human»s workload state to modify its interactions with the human. A diagnostic workload assessment algorithm that accurately estimates workload using results from two evaluations, one peer-based and one supervisory-based, is presented.
more »
« less
- Award ID(s):
- 1659746
- PAR ID:
- 10055056
- Date Published:
- Journal Name:
- Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
- Page Range / eLocation ID:
- 123-124
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Soft robotics has seen substantial success as a method to produce robust robot bodies which are safer for human users to interact with. However, while this means soft robots may be better suited to tasks requiring human interaction and teleoperation, little thought has been paid to how human users learn to control and leverage these soft robots. In pursuit of this goal, we conduct a pilot study investigating novice users interacting with a directly “operated” soft continuum robot in order to complete static and dynamic tasks in a range of difficulty levels. We study the effect of two features of the learning process: the teaching method (selfdirected versus structured) and the order of task presentation (easy to hard or hard to easy). Task performance and learning were assessed as measured by task completion time and success rate while user workload was directly measured through selfreport surveys and indirectly through heart rate tracking. We find that starting with harder tasks involving dynamic or multidegree of freedom motions better prepared users to transfer those skills to static pointing tasks. Similar benefits were partially achieved by structuring the teaching to highlight many of the robot’s features. These preliminary results highlight how soft robotic devices may require distinct teaching methods to avoid confusion or frustration by users, but that users are able to effectively explore the control space when given a sufficiently challenging context.more » « less
-
Human teams are able to easily perform collaborative manipulation tasks. However, simultaneously manipulating a large extended object for a robot and human is a difficult task due to the inherent ambiguity in the desired motion. Our approach in this paper is to leverage data from human-human dyad experiments to determine motion intent for a physical human-robot co-manipulation task. We do this by showing that the human-human dyad data exhibits distinct torque triggers for a lateral movement. As an alternative intent estimation method, we also develop a deep neural network based on motion data from human-human trials to predict future trajectories based on past object motion. We then show how force and motion data can be used to determine robot control in a human-robot dyad. Finally, we compare human-human dyad performance to the performance of two controllers that we developed for human-robot co-manipulation. We evaluate these controllers in three-degree-of-freedom planar motion where determining if the task involves rotation or translation is ambiguous.more » « less
-
Using the context of human-supervised object collection tasks, we explore policies for a robot to seek assistance from a human supervisor and avoid loss of human trust in the robot. We consider a human-robot interaction scenario in which a mobile manipulator chooses to collect objects either autonomously or through human assistance; while the human supervisor monitors the robot’s operation, assists when asked, or intervenes if the human perceives that the robot may not accomplish its goal. We design an optimal assistance-seeking policy for the robot using a Partially Observable Markov Decision Process (POMDP) setting in which human trust is a hidden state and the objective is to maximize collaborative performance. We conduct two sets of human-robot interaction experiments. The data from the first set of experiments is used to estimate POMDP parameters, which are used to compute an optimal assistance-seeking policy that is used in the second experiment. For most participants, the estimated POMDP reveals that humans are more likely to intervene when their trust is low and the robot is performing a high-complexity task; and that the robot asking for assistance in high-complexity tasks can increase human trust in the robot. Our experimental results show that the proposed trust-aware policy yields superior performance compared with an optimal trust-agnostic policy.more » « less
-
Team member inclusion is vital in collaborative teams. In this work, we explore two strategies to increase the inclusion of human team members in a human-robot team: 1) giving a person in the group a specialized role (the 'robot liaison') and 2) having the robot verbally support human team members. In a human subjects experiment (N = 26 teams, 78 participants), groups of three participants completed two rounds of a collaborative task. In round one, two participants (ingroup) completed a task with a robot in one room, and one participant (outgroup) completed the same task with a robot in a different room. In round two, all three participants and one robot completed a second task in the same room, where one participant was designated as the robot liaison. During round two, the robot verbally supported each participant 6 times on average. Results show that participants with the robot liaison role had a lower perceived group inclusion than the other group members. Additionally, when outgroup members were the robot liaison, the group was less likely to incorporate their ideas into the group's final decision. In response to the robot's supportive utterances, outgroup members, and not ingroup members, showed an increase in the proportion of time they spent talking to the group. Our results suggest that specialized roles may hinder human team member inclusion, whereas supportive robot utterances show promise in encouraging contributions from individuals who feel excluded.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- The DOI is not currently available.
