Scaling automation remains one of the industry’s most persistent challenges. In the 2025 State of the Market survey by IndustryWeek and Vention, only 37 percent of manufacturers report having scaled automation successfully. The underlying issue is the need to reprogram robots repeatedly as conditions change, limiting their ability to scale across applications and facilities.
Recent advances in Physical AI change this equation. Generalized intelligence now allows robots to perceive their environment and adapt actions in real time without retraining models or reprogramming cells for each new scenario. Unstructured tasks once considered impractical to automate, such as bin picking in high-mix environments, are becoming viable. A new class of production-ready AI is here.
Introducing Generalized Physical AI Pipeline for Manufacturing Automation (GRIIPTM)
GRIIP is a generalized Physical AI pipeline that enables robots to operate autonomously in real-world manufacturing environments. Rather than requiring each task to be reprogrammed from scratch, GRIIP provides a reusable foundation that can be deployed across applications and scaled across locations. By abstracting perception, grasp intelligence, pose estimation, and motion planning into a production-ready pipeline, it helps robot cells adapt to real world conditions without manual configuration, reducing deployment effort and the need for specialized expertise. Running on Vention’s MachineMotion AI controller leveraging the NVIDIA Jetson module-on-compute platform, GRIIP can convert existing traditionally programmed robotic applications into autonomous operations.
Under the Hood: How it Works
GRIIP leverages state-of-the-art foundation models from industry leaders such as NVIDIA, alongside Vention’s proprietary models in an integrated pipeline from perception to motion. This unique physical AI pipeline allows robots to operate reliably in unstructured manufacturing environments across a wide range of common tasks. Here are steps involved in creating a unified pipeline from perception to motion, designed to evolve continuously.
Scene Digitization & Calibration
Calibration is a foundational capability within GRIIP. It helps build and maintain the digital representation of the robot’s environment for accurate spatial reasoning. GRIIP ensures 24/7 operations through reliable scene contextualization across changing conditions including low, uneven or no light.
GRIIP supports:
- Hand-eye calibration to align camera and robot coordinate systems
- Intrinsic camera calibration for accurate image interpretation
- Stereo calibration for depth estimation
- TCP calibration to ensure precise tool positioning
- Scene calibration to establish a 3D model of the environment
Perception and Segmentation
Real-time perception is one of the biggest problems in robot vision today, as a wide range of tasks cannot be completed without minimizing the segmentation errors. GRIIP enables robots to detect and segment objects based on image capture, even in cluttered environments. Objects are ranked to prioritize viable picks, allowing the system to reason about what can be handled at any given moment.
Pose Estimation
GRIIP estimates object orientation and position with sub-millimeter accuracy throughout the manipulation lifecycle, from pre-grasp to in-hand. It provides 6DOF pose estimation for grasp point calculation and tracks object pose during manipulation for stable handling. It also adapts in real-time to object movements and shifts.
Grasping and Manipulation
GRIIP identifies and evaluates hundreds of pick candidates in real time, selecting the most reliable option based on the current scene and object state. Rather than relying on a single predefined grasp, the system maintains multiple viable options per cycle, allowing it to adapt to variation in part orientation, surface properties, and presentation.This approach enables the system to extend beyond rigid motion logic while remaining practical for production environments.
Collision-Free Path Planning
GRIIP plans robot motion with a continuous understanding of the 3D scene. Paths are filtered through inverse kinematics feasibility to ensure they can be executed by the robot, and collision-free trajectories are generated based on real-time scene awareness. This combination allows robots to move safely and predictably within dynamic environments, even as parts, fixtures, or surrounding conditions change during operation.
Benefits of a Generalized Intelligence Layer
Engineered for real-world manufacturing conditions, GRIIP delivers adaptability without any tradeoffs. It handles production variability while maintaining the speed, reliability and consistency required on the factory floor.
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Generalizes across variability
Operates reliably across changing SKUs, part geometries, surface conditions, and lighting without custom logic for each scenario. -
Expands the scope of automation
Unlocks new automation opportunities by extending the scope of automation to unstructured tasks previously considered unviable. -
Scales across applications and cells
Replaces the one-robot-one-task model with a shared AI pipeline that supports multiple applications and standardized deployment. -
Delivers production-ready performance
Designed for continuous operation with consistent pick success rates and predictable cycle times. -
Enables faster changeovers with CAD to pick workflow
Users can simply upload the CAD file to onboard a new part in a few minutes, without any training!
One AI Pipeline for Multiple Applications, Powered by GRIIP
Traditional robotics requires custom engineering for every task. GRIIP applies a single AI foundation across multiple manufacturing workflows, enabling one system to support many applications without task-specific programming:
- Deep bin picking: Reliably picks randomly oriented parts from deep bins. Handles mixed SKUs, variable geometries, and cluttered scenes without task-specific programming.
- Machine tending: Loads and unloads parts for CNC machines, presses, and other equipment. Adapts to part variation and orientation changes while maintaining stable cycle times.
- Conveyor pick and place: Tracks and picks moving parts directly from conveyors. Adapts to variable positioning and changing speeds without recalibration.
- Depalletizing: Unloads parts from pallets with varying stack patterns and orientations. Handles mixed loads and adjusts to different pallet configurations automatically.
- Kitting: Builds kits by picking and placing multiple part types into trays. Automatically adapts to different components, quantities, and configurations.
- Sanding with AI vision: Executes precise surface finishing on complex geometries. Adapts tool paths to actual part pose and surface variation to ensure consistent quality.
All of these are powered by the same generalized intelligence pipeline, allowing manufacturers to reuse capabilities across workflows.
A Foundation for Autonomous Manufacturing
As manufacturing environments become more variable and automation demands grow more complex, intelligence must become reusable, scalable, and production-ready. GRIIP represents a shift from rigid automation logic to generalized Physical AI that can evolve with the factory, turning robots into autonomous systems rather than scripted machines.
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