About
Waddle Labs builds AI agents for robotics control: users connect its API to a robot and describe tasks, after which the agents write executable control programs and decompose goals into subtasks. It targets robotics developers and builders, differentiating itself by combining the generalization of vision-language-action models with the speed, reliability, and explicit control of classical robotics approaches.
Market
Waddle Labs competes in physical-AI and robotics-control software, positioning itself as a developer-facing agent layer—essentially “Claude Code for robotics”—that turns natural-language goals into executable robot programs. Its differentiation is the combination of general-purpose LLM agents, camera-based task execution, VLA-model calls, long-horizon decomposition, and rapid policy generation across robots and environments, rather than selling only a single robot foundation model.
Waddle Labs is best suited to robotics developers, research labs, robot integrators, and industrial-automation teams—from small technical startups to larger engineering organizations—that need to create and iterate robot policies quickly. The likely buyers are robotics engineers, autonomy researchers, and automation leads working across heterogeneous robots and physical environments.
At a Glance
Problem
Waddle Labs addresses the deployment bottleneck in robot learning. The prevailing approach relies on huge robot datasets and end-to-end models that are difficult to steer and do not reliably generalize across robots, environments, and tasks. As a result, adapting a robot to a new workspace or task typically requires fresh data collection and fine-tuning, creating substantial engineering time and deployment cost, although Waddle has not published a dollar estimate for that burden. The killer use case is quickly creating manipulation policies for real hardware—for example, placing microswitches into slots or packaging a box—rather than spending weeks building and tuning task-specific robot software.
Product / Service
Waddle provides an agent-based robotics-control API. A user connects a robot, describes a task in natural language, and the agents decompose the goal into subtasks, inspect camera feeds, write control code, and call specialist action models such as vision-language-action models. The output is an executable program that can be run, tested, and iterated through further conversation, with the company’s YC profile claiming a working policy can be produced in roughly 20 minutes.
The product is intended to combine the generalization of foundation models with the speed and reliability of programmed control. Waddle’s agents continuously create and refine reusable skills—such as grasping, aligning, and folding—so each solved task can expand a shared skill library. Beyond policy creation, the company highlights autonomous data generation and overnight robotics experimentation as important uses, allowing robots to run repeated trials and train or improve policies with less hands-on intervention.
Market
Waddle competes in agentic robotics-control software and the broader physical-AI or robot-foundation-model market. Its approach is differentiated from a model that maps an instruction directly to motor commands: Waddle uses code as the policy while retaining the ability to call action models as tools. The nearest visible alternatives are adjacent robotics-intelligence platforms such as Intrinsic, which describes itself as a software and AI robotics company building intelligent-automation infrastructure, and Physical Intelligence, whose π models are general-purpose or steerable robotic foundation models. The available sources do not identify a named direct competitor with an identical agent-and-API approach.
The company is at an early commercialization stage. Waddle Labs was founded in 2025 by Yiding Song and Hanming Ye, is listed by Y Combinator in its Summer 2026 batch as an active Boston company with two employees, and invites users to try a preview API. Its publicly described traction consists of internal demonstrations—creating policies, generating training data, and facilitating auto-research—rather than disclosed customer, revenue, or scale metrics, so it is best characterized as pre-revenue or at least pre-scale based on the available evidence.
Founders & Leadership
Funding History
Y Combinator
Recent News
Waddle Labs launched its robotics-control product through Y Combinator. Its API lets users prompt robots in natural language, while agents decompose tasks, inspect camera feeds, write control code, and produce an executable program in about 20 minutes.
RuntimeWire reported that Harvard founders Hanming Ye and Yiding Song launched a YC-backed API that converts natural-language prompts and camera feeds into editable robot programs.
Y Combinator partner Ankit Gupta introduced Waddle Labs as a robotics equivalent of Claude Code. The system uses camera input, task decomposition, generated code, specialist models, and reusable skills to control robots.
Waddle Labs published a research post describing an agent-based robotics stack in which agents write and refine control programs, call vision-language-action models, build reusable skills, and coordinate multiple robots.
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Get notified when they postBusiness Model
Waddle appears to be commercializing a developer/API software product for robotics users: customers connect its API to robots and use agents to generate and iterate control programs. The company offers a preview API, but the available evidence does not disclose pricing, subscription terms, usage fees, or other revenue streams.