Papers with Code 아카이브

Motion Planning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 1
보존된 평가 기록: 1

<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/dongjing3309/minisam

공식 코드

miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework

https://github.com/dongliangch/leastar

공식 코드

LEA*: An A* Variant Algorithm with Improved Edge Efficiency for Robot Motion Planning

https://github.com/dsx0511/3dmotformer

공식 코드

3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking

https://github.com/eleurent/highway-env

공식 코드

Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios

https://github.com/embedded-software-laboratory/p-dmpc

공식 코드

Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning

https://github.com/ericcsr/diffsrl

공식 코드

DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator

https://github.com/fionaz3696/cafht

공식 코드

Conformalized Adaptive Forecasting of Heterogeneous Trajectories

https://github.com/fudan-zvg/bridgead

공식 코드

Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning

https://github.com/getterupper/PreWorld

공식 코드

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

https://github.com/goncamateus/Planning-the-path-with-rl

공식 코드

Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments

https://github.com/google-research/planet

공식 코드

Learning Latent Dynamics for Planning from Pixels

https://github.com/gsutanto/smp_manifold_learning

공식 코드

Learning Equality Constraints for Motion Planning on Manifolds

https://github.com/guthriejd1/mink-obca

공식 코드

Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance

https://github.com/haosulab/ManiSkill

공식 코드

ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

https://github.com/henryhcliu/codrivevlm

공식 코드

CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand Systems

https://github.com/himanshugupta1009/extended_space_navigation_pomdp

공식 코드

Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning

https://github.com/hithmh/Actor-critic-with-stability-guarantee

공식 코드

Actor-Critic Reinforcement Learning for Control with Stability Guarantee

https://github.com/hongsukchoi/handnerf_release

공식 코드

HandNeRF: Learning to Reconstruct Hand-Object Interaction Scene from a Single RGB Image

https://github.com/horizonrobotics/gump

공식 코드

Solving Motion Planning Tasks with a Scalable Generative Model

https://github.com/huawei-noah/SMARTS

공식 코드

Getting SMARTER for Motion Planning in Autonomous Driving Systems

https://github.com/husseinalijaafar/WPN

공식 코드

Waypoint Planning Networks

https://github.com/hyan-yao/lilodriver

공식 코드

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios

https://github.com/hzheng40/objective_space_cfr

공식 코드

Game-theoretic Objective Space Planning

https://github.com/ignc-research/ir-drl

공식 코드

Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

https://github.com/imsquared/prime-the-search

공식 코드

Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search

https://github.com/intelligent-vehicles/bevdriver

공식 코드

BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

https://github.com/jacobieee/st-mr

공식 코드

Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction

https://github.com/jangerritha/CycleIK

공식 코드

Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents

https://github.com/jianzhou1212/interaction-safety-aware-motion-planning

공식 코드

Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions

https://github.com/jingtianyan/mass-aaai

공식 코드

Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search

https://github.com/jlinki/ggns

공식 코드

Grounding Graph Network Simulators using Physical Sensor Observations

https://github.com/jlubars/RL-MPC-LaneMerging

공식 코드

Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging

https://github.com/joemwatson/monte-carlo-posterior-policy-iteration

공식 코드

Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes

https://github.com/jsalfity/task_decomposition

공식 코드

Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks

https://github.com/jtorde/planning_worlds_gazebo

공식 코드

FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments

https://github.com/junchaoli001/model-free_drl_lstm_on_pomdp_with_ldgba

공식 코드

Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments

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