https://github.com/airi-institute/learn-to-follow
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
Papers with Code 아카이브
Collision Avoidance
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alexfrom0815/Online-3D-BPP-PCT
Learning Practically Feasible Policies for Online 3D Bin Packing
https://github.com/amiryanj/crowdGAN
Data-Driven Crowd Simulation with Generative Adversarial Networks
https://github.com/amiryanj/socialways
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/bborja/mods_evaluation
MODS -- A USV-oriented object detection and obstacle segmentation benchmark
https://github.com/cmubig/SPEC
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/denkiwakame/python-ervo
L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation
https://github.com/eth-sri/eran
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
https://github.com/heshameraqi/GA-NN-Car
Reactive Collision Avoidance using Evolutionary Neural Networks
https://github.com/huang-xx/STGAT
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/hypro/hypro
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
https://github.com/john-tornblom/vote
Scaling up Memory-Efficient Formal Verification Tools for Tree Ensembles
https://github.com/kesslerlib/kessler
Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
https://github.com/lojzezust/WaSR
MODS -- A USV-oriented object detection and obstacle segmentation benchmark
https://github.com/m-hasan-n/pooling
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/miaoruonan/MACA_test
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/mirkozaff/aa-sgan
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/mit-acl/cadrl_ros
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/mit-acl/rl_collision_avoidance
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/pauls658/ReluDiff-ICSE2020-Artifact
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
https://github.com/rm2pt/veriprune
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
https://github.com/rohanchandra30/TrackNPred
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/romi514/TLSGAN
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/sen-uni-kn/specrepair
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
https://github.com/stanleybak/nnenum
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
https://github.com/stevezhang1990/dma_rl
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/tcwangshiqi-columbia/Neurify
Formal Security Analysis of Neural Networks using Symbolic Intervals
https://github.com/tcwangshiqi-columbia/ReluVal
Formal Security Analysis of Neural Networks using Symbolic Intervals
https://github.com/thorschoepe/collisionavoidance_NRP
Finding the Gap: Neuromorphic Motion Vision in Cluttered Environments
https://github.com/umautobots/bidireaction-trajectory-prediction
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
https://github.com/vehicle-lang/vehicle
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
https://github.com/vita-epfl/s-attack
Are socially-aware trajectory prediction models really socially-aware?
https://github.com/vita-epfl/semdisc
Are socially-aware trajectory prediction models really socially-aware?
https://github.com/zcc31415926/FFNet
Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.