147 research outputs found
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
Planning under uncertainty is critical for robust robot performance in
uncertain, dynamic environments, but it incurs high computational cost.
State-of-the-art online search algorithms, such as DESPOT, have vastly improved
the computational efficiency of planning under uncertainty and made it a
valuable tool for robotics in practice. This work takes one step further by
leveraging both CPU and GPU parallelization in order to achieve near real-time
online planning performance for complex tasks with large state, action, and
observation spaces. Specifically, we propose Hybrid Parallel DESPOT
(HyP-DESPOT), a massively parallel online planning algorithm that integrates
CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT
tree search by simultaneously traversing multiple independent paths using
multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes
of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds
up online planning by up to several hundred times, compared with the original
DESPOT algorithm, in several challenging robotic tasks in simulation
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a
composable architecture for robot learning systems. A DAN is composed of neural
network modules, each encoding a differentiable robot algorithm and an
associated model; and it is trained end-to-end from data. DAN combines the
strengths of model-driven modular system design and data-driven end-to-end
learning. The algorithms and models act as structural assumptions to reduce the
data requirements for learning; end-to-end learning allows the modules to adapt
to one another and compensate for imperfect models and algorithms, in order to
achieve the best overall system performance. We illustrate the DAN methodology
through a case study on a simulated robot system, which learns to navigate in
complex 3-D environments with only local visual observations and an image of a
partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at
https://youtu.be/4jcYlTSJF4
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
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