360 research outputs found
Active Classification: Theory and Application to Underwater Inspection
We discuss the problem in which an autonomous vehicle must classify an object
based on multiple views. We focus on the active classification setting, where
the vehicle controls which views to select to best perform the classification.
The problem is formulated as an extension to Bayesian active learning, and we
show connections to recent theoretical guarantees in this area. We formally
analyze the benefit of acting adaptively as new information becomes available.
The analysis leads to a probabilistic algorithm for determining the best views
to observe based on information theoretic costs. We validate our approach in
two ways, both related to underwater inspection: 3D polyhedra recognition in
synthetic depth maps and ship hull inspection with imaging sonar. These tasks
encompass both the planning and recognition aspects of the active
classification problem. The results demonstrate that actively planning for
informative views can reduce the number of necessary views by up to 80% when
compared to passive methods.Comment: 16 page
A Discrete Geometric Optimal Control Framework for Systems with Symmetries
This paper studies the optimal motion control of
mechanical systems through a discrete geometric approach. At
the core of our formulation is a discrete Lagrange-d’Alembert-
Pontryagin variational principle, from which are derived discrete
equations of motion that serve as constraints in our optimization
framework. We apply this discrete mechanical approach to
holonomic systems with symmetries and, as a result, geometric
structure and motion invariants are preserved. We illustrate our
method by computing optimal trajectories for a simple model of
an air vehicle flying through a digital terrain elevation map, and
point out some of the numerical benefits that ensue
Downwash-Aware Trajectory Planning for Large Quadrotor Teams
We describe a method for formation-change trajectory planning for large
quadrotor teams in obstacle-rich environments. Our method decomposes the
planning problem into two stages: a discrete planner operating on a graph
representation of the workspace, and a continuous refinement that converts the
non-smooth graph plan into a set of C^k-continuous trajectories, locally
optimizing an integral-squared-derivative cost. We account for the downwash
effect, allowing safe flight in dense formations. We demonstrate the
computational efficiency in simulation with up to 200 robots and the physical
plausibility with an experiment with 32 nano-quadrotors. Our approach can
compute safe and smooth trajectories for hundreds of quadrotors in dense
environments with obstacles in a few minutes.Comment: 8 page
CppFlow: Generative Inverse Kinematics for Efficient and Robust Cartesian Path Planning
In this work we present CppFlow - a novel and performant planner for the
Cartesian Path Planning problem, which finds valid trajectories up to 129x
faster than current methods, while also succeeding on more difficult problems
where others fail. At the core of the proposed algorithm is the use of a
learned, generative Inverse Kinematics solver, which is able to efficiently
produce promising entire candidate solution trajectories on the GPU. Precise,
valid solutions are then found through classical approaches such as
differentiable programming, global search, and optimization. In combining
approaches from these two paradigms we get the best of both worlds - efficient
approximate solutions from generative AI which are made exact using the
guarantees of traditional planning and optimization. We evaluate our system
against other state of the art methods on a set of established baselines as
well as new ones introduced in this work and find that our method significantly
outperforms others in terms of the time to find a valid solution and planning
success rate, and performs comparably in terms of trajectory length over time.
The work is made open source and available for use upon acceptance
HyperPPO: A scalable method for finding small policies for robotic control
Models with fewer parameters are necessary for the neural control of
memory-limited, performant robots. Finding these smaller neural network
architectures can be time-consuming. We propose HyperPPO, an on-policy
reinforcement learning algorithm that utilizes graph hypernetworks to estimate
the weights of multiple neural architectures simultaneously. Our method
estimates weights for networks that are much smaller than those in common-use
networks yet encode highly performant policies. We obtain multiple trained
policies at the same time while maintaining sample efficiency and provide the
user the choice of picking a network architecture that satisfies their
computational constraints. We show that our method scales well - more training
resources produce faster convergence to higher-performing architectures. We
demonstrate that the neural policies estimated by HyperPPO are capable of
decentralized control of a Crazyflie2.1 quadrotor. Website:
https://sites.google.com/usc.edu/hyperppoComment: Website: https://sites.google.com/usc.edu/hyperpp
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