TILOS-SDSU Seminar: A Modular AgenticAI Architecture for Commercially Scalable and Compliant Robotics
Sahil Rajesh Dhayalkar, Brain Corporation
Abstract: Autonomous navigation in dynamic environments faces immense challenges. Traditional rigid, rules-based systems often fail due to a lack of semantic understanding needed to adapt to continuous environmental shifts. Conversely, emerging end-to-end Vision-Language-Action (VLA) models introduce a critical “black box” dilemma; they inherently lack the explicit application context, deterministic guardrails, and data efficiency required for rigorous enterprise safety and compliance (e.g., SOC2). To address this, Brain Corp, in collaboration with UCSD, proposes a robust hybrid architecture underpinning the BrainOS platform. In this framework, visual inputs (via VLMs) and task commands (via LLMs) feed directly into a distinct Perception block anchored by a Contextual Grounding Layer with Semantic Mapping. This rich, grounded perception then informs a hybrid Action block, where the reasoning capabilities of VLA models operate safely alongside proven deterministic controls such as deep learning, reinforcement learning, model predictive control, etc. Crucially, an underlying Directed Safety Layer and strict Enterprise Infrastructure wrap this entire process. By isolating adaptable AI reasoning from hard-coded physical controls, this architecture provides a framework designed to securely manage the unpredictable realities of varied environments. Ultimately, this approach addresses the compliance bottleneck, laying the foundation to scale safely across diverse commercial applications and power the continuous, real-world data engine necessary to fuel next-generation physical AI.
Sahil Rajesh Dhayalkar is a Staff Autonomy Engineer and Perception Team Lead at Brain Corporation. He specializes in architecting real-time perception pipelines across LiDAR, RGB, and depth sensors, with his work currently deployed on production robots in dynamic commercial environments. During his tenure, he has pioneered the real-time computer vision pipeline for on-robot object detection at the edge, spearheaded “Localize From Anywhere,” a global localization system utilizing Vision-Language Models and RGB images, and auto-calibration, a targetless calibration of ranging sensors on robots. He holds a Master’s degree in Computer Science from Arizona State University. His research interests include robotic perception, large language models, deep learning, neuro-symbolic reasoning, and optimizations.