FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation

Abstract

A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot’s real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: 1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human’s changing intent (motivation), availability, and capability; 2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human’s workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that induces cognitive load on humans to observe their dynamic behaviors, including their mistakes, and their changing characteristics such as their expertise. We conduct user studies and show that our framework effectively collaborates non-stop for hours and adapts to various changing human behaviors and characteristics in real-time. That increases the efficiency and naturalness of the collaboration with a higher perceived collaboration, positive teammate traits, and human trust. We believe that such an extended human-adaptation is a key to the long-term use of cobots.

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

“…Since humans and robots can work together as a team in HRC, they both have the authority to make decisions. Robots can make decisions by using algorithms based on models such as Markov decision processes (MDP) [11], partially observable Markov decision processes (POMDP) [12], Bayesian Decision Model (BDM) [13], Adaptive Bayesian policy selection (ABPS) [14], and others. Therefore, the performance of robot decision making relies on algorithms.…”

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

“…These can be used as components in models of high-level human decision behaviour to simulate non-deterministic but plausible human action in industrial settings [10], [261].…”

“…Recent models include behaviours such as inter-agent communication and leaving the workstation for a break [261], e.g. by relating transition probabilities in a Markov Decision Process to human fatigue [261], [262] or frustration [261] accumulated over time.…”

“…can be arbitrarily evaluated. It has been shown that workflow variations can also be generated automatically [10], [261], hence enabling large-scale experiments without needing to recruit human subjects and even without an experimenter's active supervision. These factors increase the ease of use compared to human-participant studies with physical or virtual prototypes.…”

“…VR moreover enables crowdsourcing, which can help to increase sample sizes and reduce the required time by moving user studies to participants' homes [235]. Virtual-HITL VC enables experiments ranging from tests of single, fixed workflows [203], [251] to large-scale evaluations of automatically varied human-robot co-working processes [10], [261] and structured situation coverage [289]; not building experiments around human subjects as a limited resource means in particular that team flexibility and the effects of dynamic task sharing can fully be covered. Yet modelling plausible human behaviour which reflects the indeterminism in human actions is still an open challenge [290].…”