Aim My Robot: Precision Local Navigation to Any Object
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Nov 2024.
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Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidancecited by 46
- 2025Mobi-π: Mobilizing Your Robot Learning Policycited by 9
- 2025MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learningcited by 3
- 2026PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representationcited by 2
- 2026NavOL: Navigation Policy with Online Imitation Learningcited by 2
- 2026NavOL: Navigation Policy with Online Imitation Learning12 May 2026
- 2026PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation4 Mar 2026
- 2025MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning3 Oct 2025
- 2025Mobi-π: Mobilizing Your Robot Learning Policy29 May 2025
- 2025NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance13 May 2025