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2. Aim My Robot: Precision Local Navigation to Any Object
 
 # Aim My Robot: Precision Local Navigation to Any Object

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 Existing navigation systems mostly consider “success” when the robot reaches within 1 m radius to a goal. This precision is insufficient for emerging applications where a robot needs to be positioned precisely relative to an object for downstream tasks, such as docking, inspection, and manipulation. To this end, we design and implement Aim-My-Robot (AMR), a local navigation system that enables a robot to reach any object in its vicinity at the desired relative pose, with centimeter-level accuracy. AMR achieves high accuracy and robustness by leveraging multi-modal sensors, precise action prediction, and is trained on large-scale photorealistic data generated in simulation. AMR shows strong sim2real transfer and can adapt to different robot kinematics and unseen objects with little to no fine-tuning.



 ## Authors



Xiangyun Meng (Applied Research Scientist at Field AI)

[Xuning Yang](/person/xuning-yang)

Sanghun Jung (University of Washington)

[Fabio Ramos](/person/fabio-ramos)

Sri Sadhan Jujjavarapu (Accenture)

Sanjoy Paul (Accenture)

Dieter Fox (University of Washington)

 

 

 ## Publication Date



Tuesday, April 1, 2025

 

 ## Published in



[IEEE Robotics and Automation Letters (R-AL)](https://ieeexplore.ieee.org/document/10887329)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Robotics](/research-area/robotics)

 

 

 ## External Links



[website](https://sites.google.com/view/aimmyrobot)

[paper](https://ieeexplore.ieee.org/document/10887329)

 

 

 ## Uploaded Files



[Aim\_My\_Robot\_Precision\_Local\_Navigation\_to\_Any\_Object.pdf](https://d1qx31qr3h6wln.cloudfront.net/publications/Aim_My_Robot_Precision_Local_Navigation_to_Any_Object.pdf "Open file in new window")8.16 MB