Papers with Code 아카이브

Motion Planning

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<span style="color:grey; opacity: 0.6">( Image credit: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/rajarshi008/scarlet

공식 코드

Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic

https://github.com/reso1/stgcs

공식 코드

Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning

https://github.com/river-lab/mobiman

공식 코드

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning

https://github.com/robinwangsd/diffusion-with-multi-modal-priors

공식 코드

Controllable Motion Generation via Diffusion Modal Coupling

https://github.com/robot-motion/mpb

공식 코드

Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots

https://github.com/rose-stl-lab/pecco

공식 코드

Probabilistic Symmetry for Multi-Agent Dynamics

https://github.com/rossgreer/doscenes

공식 코드

doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation

https://github.com/rstrudel/rlbc

공식 코드

Learning to combine primitive skills: A step towards versatile robotic manipulation

https://github.com/rui1223/confined-space-rearrangement

공식 코드

Lazy Rearrangement Planning in Confined Spaces

https://github.com/rui1223/confined_space_rearrangement

공식 코드

Lazy Rearrangement Planning in Confined Spaces

https://github.com/rui1223/uniform_object_rearrangement

공식 코드

Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

https://github.com/ruiqini/ntfields

공식 코드

NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning

https://github.com/ruiqini/ntrl-demo

공식 코드

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning

https://github.com/ruiqini/p-ntfields

공식 코드

Progressive Learning for Physics-informed Neural Motion Planning

https://github.com/saferoboticslab/dual_control_hri

공식 코드

Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach

https://github.com/saferoboticslab/reach-avoid-games

공식 코드

Back to the Future: Efficient, Time-Consistent Solutions in Reach-Avoid Games

https://github.com/saferoboticslab/sharp

공식 코드

SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction

https://github.com/sailorbrandon/gt-bmpc

공식 코드

Automated Lane Merging via Game Theory and Branch Model Predictive Control

https://github.com/sajjadmzf/trajpred

공식 코드

Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios

https://github.com/scenediffuser/Scene-Diffuser

공식 코드

Diffusion-based Generation, Optimization, and Planning in 3D Scenes

https://github.com/shn66/lampos

공식 코드

Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates

https://github.com/shreya-bhatt27/NeuralAstar-ported

공식 코드

[Reproducibility Report] Path Planning using Neural A* Search

https://github.com/shuijing725/crowdnav_sim2real_turtlebot

공식 코드

Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph

https://github.com/shuishida/calvin

공식 코드

Towards real-world navigation with deep differentiable planners

https://github.com/sisl/EvidentialSparsification

공식 코드

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

https://github.com/sunnyhelen/jperceiver

공식 코드

JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes

https://github.com/symforce-org/symforce

공식 코드

SymForce: Symbolic Computation and Code Generation for Robotics

https://github.com/tarashakhurana/4d-occ-forecasting

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Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting

https://github.com/tarashakhurana/emergent-occ-forecasting

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Differentiable Raycasting for Self-supervised Occupancy Forecasting

https://github.com/teguhSL/learning_distribution_gan

공식 코드

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

https://github.com/tomjur/ModelBasedDDPG

공식 코드

Harnessing Reinforcement Learning for Neural Motion Planning

https://github.com/travelbureau/f0_icml_code

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FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis

https://github.com/tsinghua-mars-lab/statetransformer

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Large Trajectory Models are Scalable Motion Predictors and Planners

https://github.com/tsummerslab/risk_bounded_nonlinear_robot_motion_planning

공식 코드

Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control

https://github.com/ttianyuren/eTAMP

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Extended Tree Search for Robot Task and Motion Planning

https://github.com/tulerfeng/PlanKD

공식 코드

On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.