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Smartphone users often find it difficult to navigate myriad menus to perform common tasks such as "How to block calls from unknown numbers?". Currently, help documents with step-by-step instructions are manually written to aid the user. The user experience can be further enhanced by grounding the instructions in the help document to the UI and overlaying a tutorial on the phone UI. To build such tutorials, several natural language processing components including retrieval, parsing, and grounding are necessary, but there isn't any relevant dataset for such a task. Thus, we introduce UGIF-DataSet, a multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone containing 4,184 tasks across 8 languages. As an initial approach to this problem, we propose retrieving the relevant instruction steps based on the user's query and parsing the steps using Large Language Models (LLMs) to generate macros that can be executed on-device. The instruction steps are often available only in English, so the challenge includes cross-modal, cross-lingual retrieval of English how-to pages from user queries in many languages and mapping English instruction steps to UI in a potentially different language. We compare the performance of different LLMs including PaLM and GPT-3 and find that the end-to-end task completion rate is 48% for English UI but the performance drops to 32% for other languages. We analyze the common failure modes of existing models on this task and point out areas for improvement.