# NRN Robotics Roadmap NRN’s approach to robotic sports is grounded in progressive, real-world experimentation. Our roadmap is structured into phases, each designed to validate and evolve the capabilities of our Sim-to-Real reinforcement learning (RL) pipeline. From robotic arms to humanoids and racing drones, we aim to demonstrate the full range of embodied AI in competitive settings. ### **Phase 1: Concept Validation & Pipeline Robustness** In this initial phase, we focus on showcasing the integrity of our RL-powered continuous learning pipeline using a robotic arm named **RME-1** (pronounced “Arm-y 1”). This stage is centered around proving core capabilities like data collection and real-time learning. Demonstrations will include: * Object pickup and manipulation tasks * Stacking and fine motor control * Mini-putt challenges to showcase an understanding of environmental physics * Dynamic sparring drills to highlight reaction time and motion prediction These tests serve as the foundation for more complex embodied AI behavior and validate our client-side data collection tools and training infrastructure in real-world conditions. ### **Phase 2: Diversifying Sport Primitives** ### **Robotic Combat** Building on the success of RME-1, we will apply the full NRN RL pipeline to humanoid agents. * Begin with miniature humanoid robots to test RL agent performance in bipedal combat * Launch a full robotic combat competition campaign featuring humanoid tournaments * Scale toward larger, more complex humanoids with expanded mobility and dexterity * Long-term milestone: Develop and deploy full-sized humanoid competitors trained via continual learning, capable of dynamic physical interaction in competitive matches ### **Robotic Racing & Athletics** In addition to robotic combat, we will expand the NRN platform to other categories of robotic competition, each emphasizing different skill domains and control systems: * Robot Dog Racing & Challenges: Quadrupedal agents showcasing agility and terrain adaptation * Robot Kart Racing: Fast-paced, agent-controlled vehicles navigating real tracks * Drone Racing: Aerial agents trained on trajectory prediction and reactive flight control * Robot Athletics: Obstacle courses, climbing, jumps—pushing physical versatility and sim-to-real transfer --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://whitepaper.nrnagents.ai/nrn-robotics/nrn-robotics-roadmap.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.