68 research outputs found
Cost Functions for Robot Motion Style
We focus on autonomously generating robot motion for day to day physical
tasks that is expressive of a certain style or emotion. Because we seek
generalization across task instances and task types, we propose to capture
style via cost functions that the robot can use to augment its nominal task
cost and task constraints in a trajectory optimization process. We compare two
approaches to representing such cost functions: a weighted linear combination
of hand-designed features, and a neural network parameterization operating on
raw trajectory input. For each cost type, we learn weights for each style from
user feedback. We contrast these approaches to a nominal motion across
different tasks and for different styles in a user study, and find that they
both perform on par with each other, and significantly outperform the baseline.
Each approach has its advantages: featurized costs require learning fewer
parameters and can perform better on some styles, but neural network
representations do not require expert knowledge to design features and could
even learn more complex, nuanced costs than an expert can easily design
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
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