AMD x Liquid AI

Advancing Physical AI with AMD and Liquid AI

Robotec.ai partnered with AMD and Liquid AI to develop an Edge AI Hardware-in-the-Loop (HiL) simulation combining agentic reasoning, perception, and manipulation for a mobile manipulator operating in a warehouse environment. The project aimed to fine-tune multimodal AI models and deliver embodied, closed-loop autonomy running efficiently on AMD Ryzen AI platforms.

Goals

Build a high-fidelity warehouse simulation for navigation, perception, and manipulation benchmarks
Integrate and optimize Agentic AI (RAI + VLMs) with ROS, MoveIt, and robotic control stacks
Achieve embodied autonomy capable of task sequencing, inspection, manipulation, and reporting
Deploy real-time, on-device AI inference on AMD Ryzen AI hardware

Benefits

First demonstration of fully on-board, multi-agent embodied autonomy
Significant VLM performance improvements through synthetic data fine-tuning
Explainable agentic control enabling language-driven robotic tasks
Real-time Edge AI execution on AMD Ryzen AI platforms

Case study: Advancing Physical AI with AMD and Liquid AI

Partners: AMD, Liquid AI

Challenge

AMD asked us to develop a novel Edge AI Hardware-in-the-Loop (HiL) simulation with Agentic AI to perform autonomous robotic tasks in a warehouse using a mobile manipulator (Robotnik). Liquid AI engaged with us to apply and fine-tune their Edge AI models for robotics and specific domain. The challenge focuses on achieving Embodied AI combining perception, reasoning, and manipulation while ensuring hardware-efficient real-time execution on AMD Ryzen AI platforms.

High-fidelity simulation: Build a dynamic warehouse environment with diverse assets to benchmark AI-driven, flexible navigation, perception, and manipulation, and provide a fine-tuning platform for VLM.
Agentic AI integration: Apply and optimize the agentic AI framework RAI to combine VLMs with robotic control stacks (ROS, MoveIt), enabling adaptive task planning and introspection.
Embodied intelligence: Implement fully on-board, closed-loop autonomy capable of task sequencing, navigation, inspection, manipulation, and reporting driven by multimodal reasoning.

Solution

High-fidelity simulation & benchmarks: A simulated warehouse world with safety and compliance violations (e. g. spills, blocked exits, violations).
Agentic AI stack (RAI + Liquid LFM2-VL): Robotec applies the RAI multi-agent framework with Liquid AI’s LFM2-VL and open-source LLM to couple perception and reasoning with ROS 2 packages (MoveIt, Nav). The agent toolbox covers navigation, vision, manipulation, reporting, and state introspection.
Hardware-in-the-Loop (edge deployment): The full stack is deployed on a Mini-PC with AMD Ryzen AI, running ROS nodes, ONNX-runtime models, and targeted RAI for real-time inference and diagnostics.

Results

The first successful case of multi-agent embodiment fully on-board, proving huge potential of Agentic AI for robotics.
Huge VLM performance gain with synthetic data fine-tuning We delivered high-quality datasets that improved VLM performance in challenging inspection tasks drastically to 95%.
Flexible AI with fully explainable control The integrated Agentic AI system successfully executed language-to-action tasks, including handling of returns based on package damage, housekeeping tasks and inspection for safety and compliance, driven through OSHA rules.
A springboard for modern agentic AI for robotics Demonstrated on-device execution with AMD Ryzen AI achieving real-time reasoning and perception for agentic robotics at the edge. Public showcase at ROSCon 2025 in Singapore with great reception.