Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward. While this model has been successful in a variety of robotics domains, its roots lie in econometrics, and in modeling decisions among different discrete options, each with its own utility or reward. In contrast, human trajectories lie in a continuous space, with continuous-valued features that influence the reward function. We propose that it is time to rethink the Boltzmann model, and design it from the ground up to operate over such trajectory spaces. We introduce a model that explicitly accounts for distances between trajectories, rather than only their rewards. Rather than each trajectory affecting the decision independently, similar trajectories now affect the decision together. We start by showing that our model better explains human behavior in a user study. We then analyze the implications this has for robot inference, first in toy environments where we have ground truth and find more accurate inference, and finally for a 7DOF robot arm learning from user demonstrations. KEYWORDShuman decision modeling, robot inference and prediction ACM Reference Format:
We investigate experimentally the characteristics of the flow field that develops at low Reynolds numbers (Re 1) around a sharp 90 • corner bounded by channel walls. Two-dimensional planar velocity fields are obtained using particle image velocimetry (PIV) conducted in a towing tank filled with a silicone oil of high viscosity. We find that, in the vicinity of the corner, the steady-state flow patterns bear the signature of a three-dimensional secondary flow, characterized by counter-rotating pairs of streamwise vortical structures and identified by the presence of non-vanishing transverse velocities (u z ). These results are compared to numerical solutions of the incompressible flow as well as to predictions obtained, for a similar geometry, from an asymptotic expansion solution (Guglielmini et al., J. Fluid Mech., vol. 668, 2011, pp. 33-57). Furthermore, we discuss the influence of both Reynolds number and aspect ratio of the channel cross-section on the resulting secondary flows. This work represents, to the best of our knowledge, the first experimental characterization of the three-dimensional flow features arising in a pressure-driven flow near a corner at low Reynolds number.
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.
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