🤖 I teach robots to learn from their mistakes — just like the rest of us.
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I live in the messy middle ground between simulation and reality — training policies that work beautifully in sim, then spending weeks debugging why they fall apart on real hardware. That's where physics, control theory, and ML collide. That's where I do my best thinking.
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Learning Robots Teaching robots to learn from experience through RL — not just follow scripts |
Sim-to-Real Bridging the gap with domain randomization & transfer learning |
Soft Robotics Pneumatic systems for safer, more adaptive human-robot interaction |
Systems That Ship Production ML that works at scale — not just on a notebook |
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Physical Reservoir Computing
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Sim-to-Real Transfer
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Hand Tracking →
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Quadruped + UR5 Mobile Manipulation
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TurtleBot4 Autonomous Navigation
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Drift-Reduced Neural Navigation
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🏆 Best Hack for Social Good — DevHacks ASU
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🛰️ More — Jupiter AI Labs Production Systems (click to expand)
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Production infra: AWS · 99.7% uptime · 15,000+ users · CI/CD via GitHub Actions · Prometheus + Grafana
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PythonandC++are my daily drivers. Everything else is a tool I picked up when the problem demanded it.
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🔍 Interested In Sim-to-Real Transfer · Soft Robotics · Multi-Agent Systems · RL for Control |
💡 Open To Full-time roles · Research positions · Open-source robotics · Interesting RL problems |
📫 Reach Me [email protected] |
