Media Summary: This video demonstrates the work presented in our paper "Learning Depth Vision-Based Abstract. This paper proposes NN-D* Lite, a learning-enhanced incremental path planner for mobile Spot autonomously navigates a specified route through an office and lab facility. Before the test, the

Personalized Robot Navigation From Dynamic - Detailed Analysis & Overview

This video demonstrates the work presented in our paper "Learning Depth Vision-Based Abstract. This paper proposes NN-D* Lite, a learning-enhanced incremental path planner for mobile Spot autonomously navigates a specified route through an office and lab facility. Before the test, the We introduce a novel task called zero-shot Ever wondered how self-driving cars avoid sudden obstacles or factory Human-friendly robot navigation in dynamic environments

This paper proposes a compositional framework of neural SDFs to solve

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Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality
Robot Navigation in Dynamic Social Environments
A Neural Heuristic-Enhanced Incremental Planner for Mobile Robot Navigation in Dynamic Environments
Onboard dynamic-object detection and tracking forautonomous robot navigation with RGB-D camera
Spot Autonomous Navigation
Think, Act and Ask: Open-World Interactive Personalized Robot Navigation (ICRA 2024 video material)
Think, Act and Ask: Open-World Interactive Personalized Robot Navigation
Adaptive Dynamics Planning for Robot Navigation
What Strategies Manage Dynamic Robot Navigation?
Human-friendly robot navigation in dynamic environments
LIMO ROS2 Robot Navigation with Dynamic Obstacles Using nav2
Navigation Among Movable Obstacles with Learned Dynamic Constraints
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Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality

Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality

This video demonstrates the work presented in our paper "Learning Depth Vision-Based

Robot Navigation in Dynamic Social Environments

Robot Navigation in Dynamic Social Environments

"Autonomous

A Neural Heuristic-Enhanced Incremental Planner for Mobile Robot Navigation in Dynamic Environments

A Neural Heuristic-Enhanced Incremental Planner for Mobile Robot Navigation in Dynamic Environments

Abstract. This paper proposes NN-D* Lite, a learning-enhanced incremental path planner for mobile

Onboard dynamic-object detection and tracking forautonomous robot navigation with RGB-D camera

Onboard dynamic-object detection and tracking forautonomous robot navigation with RGB-D camera

Pubilshed in IEEE

Spot Autonomous Navigation

Spot Autonomous Navigation

Spot autonomously navigates a specified route through an office and lab facility. Before the test, the

Think, Act and Ask: Open-World Interactive Personalized Robot Navigation (ICRA 2024 video material)

Think, Act and Ask: Open-World Interactive Personalized Robot Navigation (ICRA 2024 video material)

We introduce a novel task called zero-shot

Think, Act and Ask: Open-World Interactive Personalized Robot Navigation

Think, Act and Ask: Open-World Interactive Personalized Robot Navigation

We introduce a novel task called zero-shot

Adaptive Dynamics Planning for Robot Navigation

Adaptive Dynamics Planning for Robot Navigation

We propose Adaptive

What Strategies Manage Dynamic Robot Navigation?

What Strategies Manage Dynamic Robot Navigation?

Ever wondered how self-driving cars avoid sudden obstacles or factory

Human-friendly robot navigation in dynamic environments

Human-friendly robot navigation in dynamic environments

Human-friendly robot navigation in dynamic environments

LIMO ROS2 Robot Navigation with Dynamic Obstacles Using nav2

LIMO ROS2 Robot Navigation with Dynamic Obstacles Using nav2

We demonstrate

Navigation Among Movable Obstacles with Learned Dynamic Constraints

Navigation Among Movable Obstacles with Learned Dynamic Constraints

Navigation

Composite Neural Signed Distance Fields for Robot Navigation in Dynamic Indoor Environments

Composite Neural Signed Distance Fields for Robot Navigation in Dynamic Indoor Environments

This paper proposes a compositional framework of neural SDFs to solve