Finn Busch @fnnBsch
PhD Student in Robotics/Computer Science at KTH Royal Institute of Technology Joined October 2021-
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Reach out if you want to chat and are at #ICRA2026, or if you want to see a live demo of our method with the @Bitcraze_se drones :)
Teaching a robot to navigate should be as simple as recording a video with your phone. In LoTIS, we study how an RGB video can guide a robot without camera calibration, poses, or robot-specific training. Page, code, demo: finnbusch.com/lotis/ #RSS2026 #ICRA2026 1/9 🧵
@giffmana The hard part might be getting the ghost registered in the right place in the view, Reminds me of some work we did recently on localizing a previous recording directly in the current view, maybe there’s some overlap: x.com/i/status/20592…
Teaching a robot to navigate should be as simple as recording a video with your phone. In LoTIS, we study how an RGB video can guide a robot without camera calibration, poses, or robot-specific training. Page, code, demo: finnbusch.com/lotis/ #RSS2026 #ICRA2026 1/9 🧵
@TobiasFischer11 Thanks for the insights :) might try to apply this to a work of ours that is related (x.com/i/status/20592…). Was it easy to get this to train stably?
Teaching a robot to navigate should be as simple as recording a video with your phone. In LoTIS, we study how an RGB video can guide a robot without camera calibration, poses, or robot-specific training. Page, code, demo: finnbusch.com/lotis/ #RSS2026 #ICRA2026 1/9 🧵
@TobiasFischer11 Interesting! Given that, and the findings in tab 4 which suggest that weight sharing gives better perf, do you think training your model with a larger encoder would get even better performance? I.e. same compute as prior methods?
Try the model yourself on your own data at huggingface.co/spaces/fnnBsch… and let us know what you think :) Find us at #RSS2026 in Sydney in July or the MM-SpatialAI workshop @ #ICRA2026 in Vienna next week! Thanks to my amazing colleagues for making this project possible :) 9/9
Results The largest gains appear when the setting becomes less like “follow the video as recorded”: off-traj starts, camera mismatch, and especially backward traversal. In sim and real-world experiments, LoTIS substantially outperforms prior baselines. 8/9
Teaching a robot to navigate should be as simple as recording a video with your phone. In LoTIS, we study how an RGB video can guide a robot without camera calibration, poses, or robot-specific training. Page, code, demo: finnbusch.com/lotis/ #RSS2026 #ICRA2026 1/9 🧵
@iamgrigorev Same here for vision tasks. Notice a small hit on training throughput, would you share the kernel?
@maharshii Interesting, this is related to your earlier tweet right? Does this also mean no matter if it is dynamic=True or False, the int/float Inputs will not be compiled into the kernel and you can change them at runtime?
@maharshii Please let me know if you figure this out :)
@CrichaelMawshaw @gowerrobert Interesting, we've been running the moonshot MUON + AdamW setup for vision tasks with shared LR. This seemed better than plan AdamW. Would be interesting to into varying coeffs for our application
@gowerrobert @CrichaelMawshaw I think so, see p.4 in the report ("Muon can directly reuse the learning rate and weight decay tuned for AdamW") and Appendix A. It seems that the RMS relationship is quite straightforward. Chances are the LR setup you found to work best is in the same ballpark?
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