5 research outputs found
Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems
Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework to quantitatively measure regret is proposed in this thesis. We embed quantitative regret analysis into Bayesian sequential decision-making (BSD) algorithms for HRC shared vision tasks in both domain search and assembly tasks. The BSD method has been used for robot decision-making tasks, which however is proved to be very different from human decision-making patterns. Instead, regret theory qualitatively models human\u27s rational decision-making behaviors under uncertainty. Moreover, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. Trust plays a critical role in determining the level of a human\u27s acceptance and hence utilization of a robot. A dynamic network based trust model combing the time series trust model is first implemented in a multi-robot motion planning task with a human-in-the-loop. However, in this model, the trust estimates for each robot is independent, which fails to model the correlative trust in multi-robot collaboration. To address this issue, the above model is extended to interdependent multi-robot Dynamic Bayesian Networks
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata