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2404.01596
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PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction
in Off-road Driving

PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving

IEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2024

2 April 2024

ArXiv (abs)PDF HTML Github (22★)

Papers citing "PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving"

6 / 6 papers shown

Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI

Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI

Terry Jingchen Zhang

...

460

1

0

06 Oct 2025

Four Principles for Physically Interpretable World Models

Four Principles for Physically Interpretable World Models

522

7

0

04 Mar 2025

FusionForce: End-to-end Differentiable Neural-Symbolic Layer for Trajectory Prediction

FusionForce: End-to-end Differentiable Neural-Symbolic Layer for Trajectory Prediction

Karel Zimmermann

692

0

0

14 Feb 2025

Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy

Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot AutonomyThe international journal of robotics research (IJRR), 2024

...

819

28

0

28 Jan 2025

AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation

AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation

Souma Chowdhury

Chen Wang

424

5

0

22 Jan 2025

iKap: Kinematics-aware Planning with Imperative Learning

iKap: Kinematics-aware Planning with Imperative LearningIEEE International Conference on Robotics and Automation (ICRA), 2024

616

5

0

12 Dec 2024

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