Papers with Code 아카이브

Robot Navigation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 5
평가표: 4
보존된 평가 기록: 45

The fundamental objective of mobile Robot Navigation is to arrive at a goal position without collision. The mobile robot is supposed to be aware of obstacles and move freely in different working scenarios.

Source: Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding

코드

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

https://github.com/etendue/habitat-api

Habitat: A Platform for Embodied AI Research

https://github.com/fabhertz95/HATS

HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification

https://github.com/facebookresearch/fair_self_supervision_benchmark

Gibson Env: Real-World Perception for Embodied Agents

https://github.com/facebookresearch/habitat-api/tree/master/habitat_baselines/rl/ddppo

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/facebookresearch/habitat-challenge

Gibson Env: Real-World Perception for Embodied Agents

https://github.com/facebookresearch/nwm

Navigation World Models

https://github.com/ferdyandannes/Train-struct2depth

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

https://github.com/ferdyandannes/struct2depth_train

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

https://github.com/ferdyandannes/struct2depthv2

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

https://github.com/gnns4hri/sngnn1dv2

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

https://github.com/go125/TestCheckpointFromDFV

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

https://github.com/go125/depth_from_video_in_the_wild

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

https://github.com/google-research/google-research/tree/master/depth_from_video_in_the_wild

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

https://github.com/iit-pavis/look_around_and_learn

Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?

https://github.com/jacobkrantz/VLN-CE

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/jemaw/gym-safety

Lyapunov-based Safe Policy Optimization for Continuous Control

https://github.com/jiaxue1993/pytorch-material-classification

Differential Viewpoints for Ground Terrain Material Recognition

https://github.com/jsego/bfgp-pp

Representation and Synthesis of C++ Programs for Generalized Planning

https://github.com/kaushikb258/Habitat_RL

Habitat: A Platform for Embodied AI Research

https://github.com/kingoflolz/topotag

TopoTag: A Robust and Scalable Topological Fiducial Marker System

https://github.com/krishanrana/multiplicative_controller_fusion

Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer

https://github.com/lukaszhalbiniak95/Projekt_RA

Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

https://github.com/ly-zhu/ly-zhu.github.io

Beyond Visual Field of View: Perceiving 3D Environment with Echoes and Vision

https://github.com/neurobot-project/AvoidanceSimulation

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

https://github.com/nyu-wireless/mmwRobotNav

Learning to Explore using Active Neural SLAM

https://github.com/omron-sinicx/planning-datasets

Learning Heuristic Search via Imitation

https://github.com/ondrejba/action_attention_iclr_22

Factored World Models for Zero-Shot Generalization in Robotic Manipulation

https://github.com/onorabil/frameSegmentation

Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation

https://github.com/opendilab/DI-engine

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/oppo-us-research/riav-mvs

Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks

https://github.com/pEAceATLast53/Social-Navigation

Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning

https://github.com/palver7/CFLPytorch

Corners for Layout: End-to-End Layout Recovery from 360 Images

https://github.com/palver7/EquiConvPytorch

Corners for Layout: End-to-End Layout Recovery from 360 Images

https://github.com/pytorch/rl

Habitat: A Platform for Embodied AI Research

https://github.com/qizekun/SoFar

SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation

https://github.com/qxb-tyust/location

Habitat: A Platform for Embodied AI Research

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