Reading the field in motion.
Long-form notes on world models, multi-agent systems, sim-to-real, and the weird intersections of foundation models with embodied AI.
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The Future of Foundation Models in Robotics
How large language models and vision-language models are reshaping the way robots understand and interact with the world.
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From Simulation to Reality: Bridging the Sim2Real Gap
A deep dive into techniques for transferring learned policies from simulation to real-world systems — domain randomization and reality-gap analysis.
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Diffusion Models: Not Just for Images Anymore
How diffusion models are being adapted for robotic manipulation, grasp synthesis, and motion planning with surprising results.
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Multi-Agent Systems: Lessons from Warehouse Automation
Real-world insights from deploying multi-agent coordination algorithms in warehouse robotics — challenges and what actually works.
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Visual SLAM in 2024: What's Changed?
Latest advances in visual SLAM, including the integration of deep learning and semantic understanding.
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Building Robust Robot Perception with Foundation Models
Leveraging models like SAM, CLIP, and DINO for robust object detection and scene understanding in challenging robotics scenarios.
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