This week’s Paper Club is all about robotics. Every year for the last decade, someone has promised that the era of robotics is just around the corner. But we’re still waiting. So we gathered a bunch of the top researchers working in AI and robotics to present the latest findings on where we are and what comes next.
We open with a discussion of the biggest roadblocks still in the way: the sim-to-real gap, action representation, the sensorimotor problem, and embodiment drift. Then we cover giving robot policies memory, teaching models what’s worth reasoning about, dexterous tool use learned entirely in simulation, why the next great robotics companies will start with teleoperation, and how to run world action models without two GB200s per robot.
Transcript: https://www.ycrootaccess.com/p/memory-simtoolreal-and-world-action
Chapters:
0:00 – Francois Chaubard: Ten years of “next year, robotics is solved”
7:59 – Marcel Torne: MEM – Multi-Scale Embodied Memory for Vision Language Action Models (arxiv.org/abs/2603.03596)
20:21 – Milan Ganai: Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning (arxiv.org/abs/2602.08167)
33:42 – Tyler Ga Wei Lum: SimToolReal – An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation (arxiv.org/abs/2602.16863)
51:21 – Niko West (Rerun.io): Why the next great robotics companies will start with teleoperation
1:08:30 – Bill Jiao & Guanming Wang (General Instinct): World action models and what comes after VLAs
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