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Luisa Mao, Garrett Warnell, Peter Stone, and Joydeep Biswas. PACER: Preference-conditioned All-terrain Costmap Generation. Robotics and Automation Letters, 2025.
In autonomous robot navigation, terrain cost assignment is typically performed using a semantics-based paradigm in which terrain is first labeled using a pre-trained semantic classifier and costs are then assigned according to a user-defined mapping between label and cost. While this approach is rapidly adaptable to changing user preferences, only preferences over the types of terrain that are already known by the semantic classifier can be expressed. In this paper, we hypothesize that a machine-learning-based alternative to the semantics-based paradigm above will allow for rapid cost assignment adaptation to preferences expressed over new terrains at deployment time without the need for additional training. To investigate this hypothesis, we introduce and study PACER, a novel approach to costmap generation that accepts as input a single birds-eye view (BEV) image of the surrounding area along with a user-specified preference context and generates a corresponding BEV costmap that aligns with the preference context. Using a staged training procedure leveraging real and synthetic data, we find that PACER is able to adapt to new user preferences at deployment time while also exhibiting better generalization to novel terrains compared to both semantics-based and representation-learning approaches. We release our code and dataset at http://github.com/ut-amrl/PACER_RAL_2025.git
@Article{mao_PACER_RAL2025,
author = {Luisa Mao and Garrett Warnell and Peter Stone and Joydeep Biswas},
title = {PACER: Preference-conditioned All-terrain Costmap Generation},
journal = {Robotics and Automation Letters},
year = {2025},
abstract = {In autonomous robot navigation, terrain cost assignment is typically performed
using a semantics-based paradigm in which terrain is first labeled using a
pre-trained semantic classifier and costs are then assigned according to a
user-defined mapping between label and cost. While this approach is rapidly
adaptable to changing user preferences, only preferences over the types of
terrain that are already known by the semantic classifier can be expressed. In
this paper, we hypothesize that a machine-learning-based alternative to the
semantics-based paradigm above will allow for rapid cost assignment adaptation to
preferences expressed over new terrains at deployment time without the need for
additional training. To investigate this hypothesis, we introduce and study
PACER, a novel approach to costmap generation that accepts as input a single
birds-eye view (BEV) image of the surrounding area along with a user-specified
preference context and generates a corresponding BEV costmap that aligns with the
preference context. Using a staged training procedure leveraging real and
synthetic data, we find that PACER is able to adapt to new user preferences at
deployment time while also exhibiting better generalization to novel terrains
compared to both semantics-based and representation-learning approaches. We
release our code and dataset at http://github.com/ut-amrl/PACER_RAL_2025.git
},
}
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