Waddle Labs
Waddle Labs is an applied AI lab building LLM agents that control robots through code-as-policy, where an agent decomposes high-level instructions into subtasks and writes executable control programs. The system works across different robot arms, grippers, and camera setups without fine-tuning or new data collection, and supports long-horizon planning, multi-agent coordination, and conversational iteration. The platform can also autonomously generate training data, such as collecting roughly a thousand pick-and-place demonstrations overnight and training an ACT policy from scratch.
- Robotics
- Artificial Intelligence
- AI Agents
- Industrial Automation
- Manufacturing / Industry 4.0
- Software Only
- Developer Tools
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Robot learning currently relies on training end-to-end models, such as vision-language-action models (VLAs) or world action models, that map camera frames and instructions directly to motor commands. This approach requires enormous amounts of robot data, produces models that are difficult to steer, and fails to generalize across robot embodiments and environments, making deployment to new hardware or tasks a slow, data-intensive process.
Solution
Waddle Labs provides an LLM-agent framework that controls robots through code-as-policy, where an agent decomposes a high-level instruction into subtasks and completes each by viewing camera feeds, writing executable control programs, and calling action models like VLAs as tools. Because the agent is an LLM agent, it inherits generalist capabilities, working with any robot arm, gripper, or camera setup without needing new data collection or fine-tuning. The agent handles long-horizon planning by reasoning through stages, verifying intermediate results, and re-planning on failure, while a master agent can spawn subagents to coordinate multiple robots concurrently. Users interact with the agent conversationally, iterating on generated programs until the robot performs the desired task.
Target Audience
Primary users are robotics researchers, automation engineers, and companies deploying industrial manipulators who need flexible, generalizable robot control without per-task data collection or model retraining.
Features
- Code-as-policy architecture that writes executable robot programs from natural-language instructions
- Long-horizon planning via reasoning model that decomposes tasks, verifies intermediate outcomes, and re-plans on failure
- Multi-agent coordination where a master agent spawns subagents to control multiple robots concurrently
- Generalist operation across different robot arms, grippers, and camera setups without fine-tuning or new data collection
- Conversational interface for iterating on robot programs in real time
- Autonomous data generation capability, such as collecting roughly a thousand pick-and-place demonstrations overnight and training an ACT policy from scratch