Introduction: VLA Models in Robotics – The Shift to Multi-Modality
The robotics industry is amid a major paradigm shift, driven by the emergence of foundation models: large-scale, multi-modal AI systems trained to understand vision, language, and action in a unified framework. Models like Google’s RT-2, which translates web-scale vision-language knowledge into robotic actions, and PaLM-E, an embodied language model capable of reasoning across diverse sensor inputs and tasks, are setting new benchmarks for generalization and task versatility.
However, these models come with a significant trade-off: their size and compute demand make them impractical for real-time deployment on resource-constrained edge platforms & cost constrained robots. This opens an opportunity for models like CogACT, which strike a balance between multi-modal reasoning and architectural efficiency. In this blog, we dive into the CogACT architecture and about edge optimization of such VLA-style robot models.
CogAct: A General-Purpose Robotic Intelligence Stack
CogAct is a next-generation, large-scale multi-modal model designed to power general-purpose robotic autonomy. It’s built around three core modules Vision, Language, and Action working together to perceive, reason, and act in real-world environments. With a total of ~7.6 billion parameters, CogAct brings the scale and generalization of foundation models into robotics, without compromising on task-level execution.
How it works?
Vision Module
Built on high-capacity transformers like DINOv2 and SigLIP, this module processes raw images into perceptual tokens. Trained on large-scale datasets, it captures both spatial layouts and object-level semantics with high fidelity.
Language Module
Powered by a Large language model (LLM) LLaMA-2, this module blends visual context with language instructions to understand goals, reason through intent, and ground actions in the environment. It also enables flexible task execution by adapting to diverse natural language prompts, from simple object manipulation to more complex sequential tasks.
Action Module
To generate smooth, multi-step actions, CogAct uses a Diffusion Transformer. It translates cognitive features into temporally consistent motion commands, enabling complex, real-world tasks like grasping, placing, or navigating.
A Real-World Example
- Give CogAct an image of a cluttered tabletop and the instruction: “Move the Pepsi can near the orange”.
- It will recognize the objects, reason through the instruction, plan a collision-free path, and output a sequence of actions for a robotic arm to physically move the can next to the orange.
An overview of CogAct Model
CogAct has already been used in robotics scenarios like mobile manipulation, indoor navigation, warehouse automation, and multi-agent collaboration tasks that require high-level understanding and fine-grained action control. Its architecture enables robots to act with context, intent, and temporal coherence.
But with this level of intelligence comes significant computational overhead, making edge deployment a real challenge. That’s where our work at MulticoreWare comes in: transforming powerful but heavy models like CogAct into edge-ready systems without compromising their core capabilities.
Our Approach:
CogAct, with its 7.6B parameters and multi-stream architecture, presented a unique challenge. Using a combination of optimization techniques including quantization, pruning, and model graph tuning, we significantly reduced its inference time. As a result, we have achieved 1.3× faster performance, translating to around 26% reduction in latency, all while preserving the model’s original accuracy and behaviour.
Results of the Original CogAct Model
Results of the MulticoreWare Optimized
CogAct Model (1.3x faster)
We successfully deployed the optimized model on real-world edge platforms proving that even foundation-scale robotics models like CogAct can be made efficient and practical for on-device execution.
Applications of VLA Models: Why Optimization Matters
VLA models like CogAct are ushering in a new era of robotic intelligence by enabling machines to understand and act upon complex, high-level instructions. Their potential applications span a wide array of real-world domains:
Warehouse Automation
Robots can understand flexible commands like “Stack all the red boxes near the loading bay,” and figure out object types, spatial relationships, and task sequences on the fly.
Healthcare Robotics
In hospitals or elder care settings, robots powered by VLA models can safely follow spoken instructions, navigate through crowded spaces, and assist with simple fetch-and-carry tasks.
Household Assistance
Whether it’s tidying up or following multi-step instructions like “Put the dishes in the sink and wipe the counter,” VLA-based robots make it easier for humans to interact with machines in a natural way.
Multi-Agent Collaboration
In environments where several robots need to work together, like coordinating drone fleets or warehouse bots shared understanding of language and vision helps improve coordination, efficiency, and safety.
But while these models promise general-purpose autonomy, deploying them in the field, especially on low-power, mobile, or real-time systems requires overcoming steep computational challenges. That’s why optimization is not just beneficial, but essential. Edge optimization ensures that:
- Fast, real-time responses to dynamic environments.
- Energy efficiency for mobile or battery-powered robots.
- Compliance with strict memory and compute limits on embedded platforms.
- Reliable performance for safety-critical tasks.
By optimizing VLA models like CogAct, we bridge the gap between foundational intelligence and deployable autonomy, bringing sophisticated reasoning to practical robotics applications, from warehouses to wheels to underwater exploration.
Our Expertise in AI powered Edge Solutions
- Proficiency Across 150+ SOTA AI Models: Custom optimization for CPUs, GPUs, DSPs, NPUs, and low-power edge AI SoCs across modalities.
- Edge-First BEV (Bird’s Eye View) Algorithms for Diverse Mobility Systems: Tailored BEV pipelines for micro-mobility, two-wheeled, and four-wheeled & AMR / AGV based mobile robotics platforms.
- End-to-End Robotics Perception Stack Development: Experience building modular perception systems including object detection, depth estimation, semantic segmentation, and sensor fusion tailored for robotics use cases.
- Expertise in BEV & Vision Transformers: Optimized models like BEVFormer, BEV-SegFormer, and Lift-Splat-Shoot (LSS) etc for automotive AI accelerators.
- Advanced Quantization of Fusion Models: INT8 quantization of camera+LiDAR models like DeepFusion, BEV-Det, and DeepInteraction without sacrificing accuracy.
- SLAM, Mapping & Navigation Algorithms: In-house expertise in visual-inertial SLAM, 3D mapping, and real-time navigation for autonomous robotic systems in GPS-denied and dynamic environments.
Conclusion: From Intelligence to On-Device Autonomy
Our method is optimized for hardware efficiency, prioritizes low latency, and emphasizes high accuracy-designed to enable real-world deployment in industries such as autonomous vehicles, warehouse robotics, last-mile delivery, smart infrastructure, and more. At MulticoreWare, we leverage our specialized expertise to enhance and accelerate AI solutions, tailored to the specific demands of your unique use cases. To learn more about how we are building efficient AI solutions, write to us at info@multicorewareinc.com
