Modeling Supervisor Safe Sets for Improving Collaboration in Human-Robot Teams

Abstract

When a human supervisor collaborates with a team of robots, the human's attention is divided, and cognitive resources are at a premium. We aim to optimize the distribution of these resources and the flow of attention. To this end, we propose the model of an idealized supervisor to describe human behavior. Such a supervisor employs a potentially inaccurate internal model of the the robots' dynamics to judge safety. We represent these safety judgements by constructing a safe set from this internal model using reachability theory. When a robot leaves this safe set, the idealized supervisor will intervene to assist, regardless of whether or not the robot remains objectively safe. False positives, where a human supervisor incorrectly judges a robot to be in danger, needlessly consume supervisor attention. In this work, we propose a method that decreases false positives by learning the supervisor's safe set and using that information to govern robot behavior. We prove that robots behaving according to our approach will reduce the occurrence of false positives for our idealized supervisor model. Furthermore, we empirically validate our approach with a user study that demonstrates a significant (p = 0.0328) reduction in false positives for our method compared to a baseline safety controller.

Symbiotic human-robot collaborative assembly

“…Similar to an assembly environment, the potential map using diffusion equation is employed to control the behaviour of the manipulator to avoid collision between the manipulator and an obstacle or a user. In addition, other researchers focused on combining different sensing techniques to track humans and robots on shop floors [46,207,135,212,157,259] which used both ultrasonic and infrared proximity sensors to establish a collision-free robotic environment. Among commercial systems of safety protection solutions, SafetyEYE  [200] of Pilz is a popular choice.…”

Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning

“…One approach to avoid this trivial solution is to use domain knowledge of the modeled system to restrict C to a library of plausible or common constraints. The authors of [16] construct such a library by using reachability theory to calculate a family of likely unsafe sets.…”

“…Recent work has attempted to address these pitfalls by using demonstrations to learn a rich class of possible specifications [12]. Others have focused specifically on learning constraints, that is, behaviors that are expressly forbidden or infeasible [13][14][15][16][17]. It is towards this problem of constraint inference that we turn our attention.…”

Predicting Stochastic Human Forward Reachable Sets Based on Learned Human Behavior

“…In [15], the authors develop the notion of a human safe set for a human supervisor, to model when the supervisor would start to intervene with robot teams operating on their own in order to avoid static obstacles. However, this does not provide information on a mapping from any joint configuration of the human and robot to the human's action when we aim to also model how the human would avoid, for example, predicting the direction of avoidance, and the entire avoidance process.…”