1,816 research outputs found
Task Planning and Execution for Human Robot Team Performing a Shared Task in a Shared Workspace
A cyber-physical system is developed to enable a human-robot team to perform a shared task in a shared workspace. The system setup is suitable for the implementation of a tabletop manipulation task, a common human-robot collaboration scenario. The system integrates elements that exist in the physical (real) and the virtual world. In this work, we report the insights we gathered throughout our exploration in understanding and implementing task planning and execution for human-robot team
"Guess what I'm doing": Extending legibility to sequential decision tasks
In this paper we investigate the notion of legibility in sequential decision
tasks under uncertainty. Previous works that extend legibility to scenarios
beyond robot motion either focus on deterministic settings or are
computationally too expensive. Our proposed approach, dubbed PoL-MDP, is able
to handle uncertainty while remaining computationally tractable. We establish
the advantages of our approach against state-of-the-art approaches in several
simulated scenarios of different complexity. We also showcase the use of our
legible policies as demonstrations for an inverse reinforcement learning agent,
establishing their superiority against the commonly used demonstrations based
on the optimal policy. Finally, we assess the legibility of our computed
policies through a user study where people are asked to infer the goal of a
mobile robot following a legible policy by observing its actions
SLOT-V: Supervised Learning of Observer Models for Legible Robot Motion Planning in Manipulation
We present SLOT-V, a novel supervised learning framework that learns observer
models (human preferences) from robot motion trajectories in a legibility
context. Legibility measures how easily a (human) observer can infer the
robot's goal from a robot motion trajectory. When generating such trajectories,
existing planners often rely on an observer model that estimates the quality of
trajectory candidates. These observer models are frequently hand-crafted or,
occasionally, learned from demonstrations. Here, we propose to learn them in a
supervised manner using the same data format that is frequently used during the
evaluation of aforementioned approaches. We then demonstrate the generality of
SLOT-V using a Franka Emika in a simulated manipulation environment. For this,
we show that it can learn to closely predict various hand-crafted observer
models, i.e., that SLOT-V's hypothesis space encompasses existing handcrafted
models. Next, we showcase SLOT-V's ability to generalize by showing that a
trained model continues to perform well in environments with unseen goal
configurations and/or goal counts. Finally, we benchmark SLOT-V's sample
efficiency (and performance) against an existing IRL approach and show that
SLOT-V learns better observer models with less data. Combined, these results
suggest that SLOT-V can learn viable observer models. Better observer models
imply more legible trajectories, which may - in turn - lead to better and more
transparent human-robot interaction
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