2,693 research outputs found
Enabling Robots to Communicate their Objectives
The overarching goal of this work is to efficiently enable end-users to
correctly anticipate a robot's behavior in novel situations. Since a robot's
behavior is often a direct result of its underlying objective function, our
insight is that end-users need to have an accurate mental model of this
objective function in order to understand and predict what the robot will do.
While people naturally develop such a mental model over time through observing
the robot act, this familiarization process may be lengthy. Our approach
reduces this time by having the robot model how people infer objectives from
observed behavior, and then it selects those behaviors that are maximally
informative. The problem of computing a posterior over objectives from observed
behavior is known as Inverse Reinforcement Learning (IRL), and has been applied
to robots learning human objectives. We consider the problem where the roles of
human and robot are swapped. Our main contribution is to recognize that unlike
robots, humans will not be exact in their IRL inference. We thus introduce two
factors to define candidate approximate-inference models for human learning in
this setting, and analyze them in a user study in the autonomous driving
domain. We show that certain approximate-inference models lead to the robot
generating example behaviors that better enable users to anticipate what it
will do in novel situations. Our results also suggest, however, that additional
research is needed in modeling how humans extrapolate from examples of robot
behavior.Comment: RSS 201
On inferring intentions in shared tasks for industrial collaborative robots
Inferring human operators' actions in shared collaborative tasks, plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space but also forces and the execution of a task. In this article, we present a robotic system which is able to identify different human's intentions and to adapt its behavior consequently, only by means of force data. In order to accomplish this aim, three major contributions are presented: (a) force-based operator's intent recognition, (b) force-based dataset of physical human-robot interaction and (c) validation of the whole system in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human-robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.Peer ReviewedPostprint (published version
Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression
We design a new approach that allows robot learning of new activities from
unlabeled human example videos. Given videos of humans executing the same
activity from a human's viewpoint (i.e., first-person videos), our objective is
to make the robot learn the temporal structure of the activity as its future
regression network, and learn to transfer such model for its own motor
execution. We present a new deep learning model: We extend the state-of-the-art
convolutional object detection network for the representation/estimation of
human hands in training videos, and newly introduce the concept of using a
fully convolutional network to regress (i.e., predict) the intermediate scene
representation corresponding to the future frame (e.g., 1-2 seconds later).
Combining these allows direct prediction of future locations of human hands and
objects, which enables the robot to infer the motor control plan using our
manipulation network. We experimentally confirm that our approach makes
learning of robot activities from unlabeled human interaction videos possible,
and demonstrate that our robot is able to execute the learned collaborative
activities in real-time directly based on its camera input
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