31 research outputs found
Does Unpredictability Influence Driving Behavior?
In this paper we investigate the effect of the unpredictability of
surrounding cars on an ego-car performing a driving maneuver. We use Maximum
Entropy Inverse Reinforcement Learning to model reward functions for an ego-car
conducting a lane change in a highway setting. We define a new feature based on
the unpredictability of surrounding cars and use it in the reward function. We
learn two reward functions from human data: a baseline and one that
incorporates our defined unpredictability feature, then compare their
performance with a quantitative and qualitative evaluation. Our evaluation
demonstrates that incorporating the unpredictability feature leads to a better
fit of human-generated test data. These results encourage further investigation
of the effect of unpredictability on driving behavior.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS) 202
Policy-Guided Lazy Search with Feedback for Task and Motion Planning
PDDLStream solvers have recently emerged as viable solutions for Task and
Motion Planning (TAMP) problems, extending PDDL to problems with continuous
action spaces. Prior work has shown how PDDLStream problems can be reduced to a
sequence of PDDL planning problems, which can then be solved using
off-the-shelf planners. However, this approach can suffer from long runtimes.
In this paper we propose LAZY, a solver for PDDLStream problems that maintains
a single integrated search over action skeletons, which gets progressively more
geometrically informed as samples of possible motions are lazily drawn during
motion planning. We explore how learned models of goal-directed policies and
current motion sampling data can be incorporated in LAZY to adaptively guide
the task planner. We show that this leads to significant speed-ups in the
search for a feasible solution evaluated over unseen test environments of
varying numbers of objects, goals, and initial conditions. We evaluate our TAMP
approach by comparing to existing solvers for PDDLStream problems on a range of
simulated 7DoF rearrangement/manipulation problems
Preserving Linear Separability in Continual Learning by Backward Feature Projection
Catastrophic forgetting has been a major challenge in continual learning,
where the model needs to learn new tasks with limited or no access to data from
previously seen tasks. To tackle this challenge, methods based on knowledge
distillation in feature space have been proposed and shown to reduce
forgetting. However, most feature distillation methods directly constrain the
new features to match the old ones, overlooking the need for plasticity. To
achieve a better stability-plasticity trade-off, we propose Backward Feature
Projection (BFP), a method for continual learning that allows the new features
to change up to a learnable linear transformation of the old features. BFP
preserves the linear separability of the old classes while allowing the
emergence of new feature directions to accommodate new classes. BFP can be
integrated with existing experience replay methods and boost performance by a
significant margin. We also demonstrate that BFP helps learn a better
representation space, in which linear separability is well preserved during
continual learning and linear probing achieves high classification accuracy.
The code can be found at https://github.com/rvl-lab-utoronto/BFPComment: CVPR 2023. The code can be found at
https://github.com/rvl-lab-utoronto/BF
Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images
We introduce a multi-sensor navigation system for autonomous surface vessels
(ASV) intended for water-quality monitoring in freshwater lakes. Our mission
planner uses satellite imagery as a prior map, formulating offline a
mission-level policy for global navigation of the ASV and enabling autonomous
online execution via local perception and local planning modules. A significant
challenge is posed by the inconsistencies in traversability estimation between
satellite images and real lakes, due to environmental effects such as wind,
aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we
specifically modelled these traversability uncertainties as stochastic edges in
a graph and optimized for a mission-level policy that minimizes the expected
total travel distance. To execute the policy, we propose a modern local planner
architecture that processes sensor inputs and plans paths to execute the
high-level policy under uncertain traversability conditions. Our system was
tested on three km-scale missions on a Northern Ontario lake, demonstrating
that our GPS-, vision-, and sonar-enabled ASV system can effectively execute
the mission-level policy and disambiguate the traversability of stochastic
edges. Finally, we provide insights gained from practical field experience and
offer several future directions to enhance the overall reliability of ASV
navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv
admin note: text overlap with arXiv:2209.1186