Aim My Robot: Precision Local Navigation to Any Object
Xiangyun Meng, Xuning Yang, Sanghun Jung, Fabio Ramos, Srid Sadhan, Jujjavarapu, Sanjoy Paul, Dieter Fox

TL;DR
Aim-My-Robot (AMR) is a local navigation system that enables robots to reach objects with centimeter-level accuracy for downstream tasks, using multi-modal perception and simulation-trained models.
Contribution
The paper introduces AMR, a novel local navigation system that achieves high-precision object positioning with robust sim2real transfer and adaptability to various robots and objects.
Findings
Achieves centimeter-level positioning accuracy.
Demonstrates strong sim2real transfer capabilities.
Adapts to different robot kinematics and unseen objects.
Abstract
Existing navigation systems mostly consider "success" when the robot reaches within 1m radius to a goal. This precision is insufficient for emerging applications where the 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 precision. AMR achieves high precision and robustness by leveraging multi-modal perception, 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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotic Path Planning Algorithms
