Roku Explorer
Autonomous Exploratory Testing Platform
A production autonomous QA platform for firmware and Grand Central product areas. Combines PPO/LSTM reinforcement learning, ChromaDB multimodal RAG, dual validators, and MCP-based device control to navigate device UIs, surface potential issues, and flag visual regressions with minimal human oversight.
The Core Challenge
Engineering Solution & Implementation
Designed a multi-service Docker Compose stack (API, worker, UI, dual validators, scheduler, MCP) around an autonomous agent loop: PPO/LSTM policies plus multimodal vision reasoning explore device UIs, while change-driven planning targets high-risk paths and MCP commands physical test devices. UI states are indexed in ChromaDB for retrieval-augmented reasoning across firmware and Grand Central product areas.
Figure 1: High-level architectural dataflow of Roku Explorer's change-driven autonomous loop.
Verified Outcomes & Deliverables
In production for ~16 months with 249,868 device actions across 1,065 runs on 92 devices, serving 30+ users and surfacing 16,266 validation signals over a 1,993-node / 5,966-transition exploration graph.
Designed and still lead the platform — roughly 70% of commit history across a 17-engineer contributor base (~4,000 commits).
Closed the loop from code change → automated risk scenario generation → device exploration with dual validators and MCP device control.