robots-4

Collision Avoidance

3260 papers • 126 benchmarks • 313 datasets

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Use these libraries to find collision-avoidance-4 models and implementations

95616ARG/SyReNN

2 papers 38

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Most implemented papers

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Mykel J. Kochenderfer, Kyle D. Julian, Guy Katz, Clark W. Barrett, D. Dill•Thu Feb 02 2017

Results show that the novel, scalable, and efficient technique presented can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.

2019

Content

lojzezust/WaSR

2 papers 27

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Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

Justin Johnson, S. Savarese, Li Fei-Fei, Alexandre Alahi, Agrim Gupta•Wed Mar 28 2018

A recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people, and outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.

2241 0

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Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

J. How, Michael Everett, Yu Fan Chen•Thu May 03 2018

This work extends the 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, and introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size.

583 0

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Formal Security Analysis of Neural Networks using Symbolic Intervals

Kexin Pei, Junfeng Yang, S. Jana, Shiqi Wang, J. Whitehouse•Fri Apr 27 2018

This paper designs, implements, and evaluates a new direction for formally checking security properties of DNNs without using SMT solvers, and leverages interval arithmetic to compute rigorous bounds on the DNN outputs, which is easily parallelizable.

512 0

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Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning

J. How, Michael Everett, Yu Fan Chen•Wed Oct 23 2019

This work develops an algorithm that learns collision avoidance among a variety of heterogeneous, non-communicating, dynamic agents without assuming they follow any particular behavior rules and extends the previous work by introducing a strategy using Long Short-Term Memory (LSTM) that enables the algorithm to use observations of an arbitrary number of other agents.

214 0

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Learning Sampling Distributions for Robot Motion Planning

M. Pavone, Brian Ichter, James Harrison•Fri Sep 15 2017

This paper proposes a methodology for nonuniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling, resulting in an order of magnitude improvement in terms of success rate and convergence to the optimal cost.

389 0

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Efficient Formal Safety Analysis of Neural Networks

Kexin Pei, Junfeng Yang, S. Jana, Shiqi Wang, J. Whitehouse•Tue Sep 18 2018

This paper presents a new efficient approach for rigorously checking different safety properties of neural networks that significantly outperforms existing approaches by multiple orders of magnitude and believes that this approach to estimating tight output bounds of a network for a given input range can also help improve the explainability of Neural networks and guide the training process of more robust neural networks.

433 0

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Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

H. Zhang, Wenxi Liu, Pinxin Long, Tingxiang Fan, X. Liao, Jia Pan•Wed Sep 27 2017

This work presents 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 and demonstrates that the learned policy can be well generalized to new scenarios that do not appear in the entire training period.

507 0

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Provable repair of deep neural networks

Matthew Sotoudeh, Aditya V. Thakur•Thu Apr 08 2021

The Provable Repair problem is introduced, which is the problem of repairing a network N to construct a new network N′ that satisfies a given specification, and the introduction of a Decoupled DNN architecture, which allows for provable repair to a linear programming problem.

82 0

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