Human-robot cross-training: computational formulation, modeling and evaluation of a human team training strategy
Stefanos Nikolaidis,Julie A. Shah +1 more
- pp 33-40
TLDR
The hypothesis that effective and fluent human-robot teaming may be best achieved by modeling effective practices for human teamwork is supported.Abstract:
We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task. We first present a computational formulation of the robot's interrole knowledge and show that it is quantitatively comparable to the human mental model. Based on this encoding, we formulate human-robot cross-training and evaluate it in human subject experiments (n = 36). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training provides statistically significant improvements in quantitative team performance measures. Additionally, significant differences emerge in the perceived robot performance and human trust. These results support the hypothesis that effective and fluent human-robot teaming may be best achieved by modeling effective practices for human teamwork.read more
Citations
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Journal ArticleDOI
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response
TL;DR: This work represents each possible future using an anticipatory temporal conditional random field (ATCRF) that models the rich spatial-temporal relations through object affordances and represents each ATCRF as a particle and represents the distribution over the potential futures using a set of particles.
Journal ArticleDOI
Evaluating Fluency in Human–Robot Collaboration
TL;DR: In this paper, the authors developed a number of metrics to evaluate the level of fluency in human-robot shared-location teamwork, and provided an analytical model for four objective metrics, and assessed their dynamics in a turn-taking framework.
Proceedings ArticleDOI
Improving Robot Controller Transparency Through Autonomous Policy Explanation
Bradley Hayes,Julie A. Shah +1 more
TL;DR: This work presents a series of algorithms and an accompanying system that enables robots to autonomously synthesize policy descriptions and respond to both general and targeted queries by human collaborators, demonstrating applicability to a variety of robot controller types.
Proceedings ArticleDOI
Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks
TL;DR: In this article, a framework for automatically learning human user models from joint-action demonstrations that enables a robot to compute a robust policy for a collaborative task with a human is presented. But, it is not shown that the learned model can support effective teaming in human-robot collaborative tasks.
Journal ArticleDOI
Decision-making authority, team efficiency and human worker satisfaction in mixed human---robot teams
Matthew C. Gombolay,Reymundo A. Gutierrez,Shanelle G. Clarke,Giancarlo F. Sturla,Julie A. Shah +4 more
TL;DR: It is found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation, and that people preferred to cede their control authority to the robot than to human teammates only.
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