Deepak Pathak

AI Summary

→ WHAT IT COVERS Skild AI co-founders Deepak Pathak and Abhinav Gupta explain their OmniBrain platform — a single universal model designed to control any robot form factor across any task, using a three-source data strategy and deployment-first approach to scale physical AI across industrial and consumer environments. → KEY INSIGHTS - **Three-Source Data Architecture:** Skild trains OmniBrain using video data (billions of examples, high diversity, low precision), simulation data (scalable, measurable forces, but sim-to-real gap exists), and real-world teleoperation data (richest quality, hardest to scale). Each source compensates for the others' weaknesses, mirroring the pre-training and fine-tuning separation used in large language models. - **Deployment as a Technical Problem:** Unlike software products where users self-onboard, robotics deployment requires active engineering effort. Skild's strategy is to get new robot systems operational within days using minimal fine-tuning data, then scale across scenarios. This rapid specialization from a general base model is treated as a core technical challenge, not an afterthought. - **Data Flywheel Across Verticals:** Corner cases in one industry become standard training cases for another. Structured factory deployments generate data that enables semi-structured environments like hospitals and hotels, which in turn bootstrap unstructured home robotics. This cross-vertical data accumulation is the primary mechanism Skild uses to improve OmniBrain over time. - **Three-Stage Deployment Testing Protocol:** Before any deployment, Skild runs task-specific KPIs (accuracy and cycle time), generalization stress tests (altered lighting, unexpected objects, hardware failures), and safety guardrail validation. If a camera feed is severed, for example, the robot must halt or stay within predefined boundaries rather than continue operating blind. - **NVIDIA Stack Integration:** Skild uses Isaac physics simulation for scenario generation, Cosmos generative models for data augmentation to create variations from single data points, and NVIDIA edge compute hardware for on-device inference. On-device processing is necessary because robots cannot tolerate server round-trip latency during physical actions like catching or stabilizing. → NOTABLE MOMENT The founders use Roger Federer as a concrete illustration of video data's limits: watching millions of hours of professional tennis footage does not produce a professional tennis player. Observation alone cannot transfer physical skill — robots, like humans, require practice repetitions to build reliable motor behavior. 💼 SPONSORS None detected 🏷️ Physical AI, Robot Foundation Models, Sim-to-Real Transfer, Industrial Automation, Edge Inference