Collision Avoidance
109 papers with code • 0 benchmarks • 1 datasets
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Libraries
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HARPER
Most implemented papers
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems.
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
agrimgupta92/sgan
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CVPR 2018
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
mfe7/cadrl_ros
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This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules.
Formal Security Analysis of Neural Networks using Symbolic Intervals
In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers.
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
mit-acl/gym-collision-avoidance
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Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians.
Learning Sampling Distributions for Robot Motion Planning
This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling.
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Acmece/rl-collision-avoidance
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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.
Efficient Formal Safety Analysis of Neural Networks
Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques.
Provable Repair of Deep Neural Networks
This has motivated a large number of techniques for finding unsafe behavior in DNNs.
MODS -- A USV-oriented object detection and obstacle segmentation benchmark
lojzezust/WaSR
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We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation.
