Manufacturing’s toughest challenge lies in automating varied, routine operations like bin picking and managing different components. Conventional robotic solutions often prove overly complicated, require costly external specialists, and face lengthy deployment timelines because of continuous reprogramming demands. Enter Physical AI—it can transform robots into smart decision-makers that can detect and pick diverse objects by using only CAD file information. First introduced at NVIDIA GTC 2025, Vention’s AI Operator solution is successfully showcasing the potential of what practical application of physical AI could mean for manufacturing. Vention’s AI experts recently shared an in-depth overview of the solution in a live webinar with NVIDIA.
Here are some of the most important questions asked by the manufacturing and automation community during the event.
Adaptability and Flexibility
How do you see physical AI adapting to all kinds of factory environments?
Physical AI is already taking traditional picking into uncharted territories with an ability to detect parts placed in an unstructured way. As it evolves, there are three key areas where it can vastly improve adaptability for different factory environments:
Automated grasp planning will replace manual programming by using CAD models and simulation to generate optimal grip strategies autonomously.
Physics-based digital twins provide accurate predictive models that reduce implementation risk and enable confident deployment.
Advanced dexterous manipulation algorithms address complex spatial challenges, including collision avoidance during bin picking of densely packed components.
Together, these technologies mark a shift from traditional project-based automation, which requires extensive customization, toward scalable product-based automation accessible to enterprises of all sizes and technical expertise levels.
Can this be used for low-light conditions?
Yes, the AI Operator is designed to be adaptable to diverse environments, including low lighting conditions. Its ability to operate efficiently with lights on or off enables true 24/7 manufacturing operations, maximizing throughput and facility utilization.
Can the AI Operator pick soft or delicate objects?
Yes, the AI Operator adapts to many sizes, shapes, colors, and part types —such as packaged pouches, fragile plastic plumbing parts, precision metal components for the automotive industry, and more.. This adaptability makes it suitable for a wide range of industries and manufacturing processes.
Ease of Use and Setup
How easy is it to teach new parts? What does the workflow to teach a new part look like?
Teaching new parts in Vention’s system, which combines NVIDIA’s Isaac technologies with MachineMotion AI (MMAI), is designed to be simple and fast compared to traditional industrial automation methods.
The system uses pre-trained AI foundation models like NVIDIA’s FoundationPose, which eliminates the need for task-specific training or extensive implementation. Unlike conventional methods that require days of development and significant compute resources to train models for each specific part, this approach provides out-of-the-box performance with minimal setup.
Workflow for Teaching a New Part:
- Upload CAD Model - Provide a 3D CAD model of the part. FoundationPose can immediately detect the part based on this model.
- Define Pick Points - Specify the grasp locations on the part. Currently, some grasps require manual programming, though automatic grasp generation based on CAD models will simplify this process in the future.
- Deploy for Bin Picking - The system handles the challenging bin picking task, extracting parts from unstructured environments and positioning them in a known, structured location.
- Integration Handoff - Once the robot places the part in a structured position, additional actions can be programmed based on the application requirements. This downstream programming can be accomplished through traditional coding methods or Vention’s no-code MachineLogic platform.
How long does it take to set up new recipes?
In real-world applications, it can take a few hours up to a day to onboard new recipes. Part of this rapid onboarding comes from the ability to simply upload the CAD model of the part and program the grasps. The inherent capability of the AI Operator to adapt to size, shape, color, and environmental conditions makes onboarding simpler.
Do you require the 3D model for bin picking?
Yes, onboarding a 3D CAD model of a part is the most straightforward and efficient approach. The AI Operator leverages NVIDIA’s FoundationPose model, eliminating the need for system training on new parts. By simply providing a new CAD model, the system can instantly detect the part, significantly reducing downtime between changeovers and development time compared to traditional methods. Soon it might be possible to scan the object to generate CAD models as 3D scanning technologies improve.
Technical Details and Performance
Is the model processing raw depth data directly, or using something more refined?
The system utilizes RGB and depth signals. FoundationStereo directly estimates depth from stereo images, transforming inexpensive, commodity stereo cameras (~$500) into high-quality depth sensors with results approaching LiDAR performance.
From there, the pipeline branches into specialized NVIDIA modules:
- FoundationPose uses the RGB images together with the generated depth to estimate 6-DoF object poses in 3D space
- nvblox converts the depth into a voxel-based collision model of the environment
- cuMotion then uses this collision model to plan smooth, collision-free trajectories in parallel on the GPU
This pipeline enables real-time, GPU-accelerated stereo sensing, perception, and motion planning—turning affordable stereo cameras into powerful robotic vision systems.
What speeds/throughputs can be achieved with the AI Operator?
For the standard AI Operator demos, Vention has consistently showcased 15-20 second cycle times. However, this can be faster in a real-world environment after optimizing the application.
How fast is image processing in the AI Operator?
The AI Operator leverages Vention’s MachineMotion AI controller with an integrated NVIDIA Jetson module for image processing. This configuration delivers exceptional processing speeds for both visual and perception data through its advanced AI computing architecture.
Reliability and Risk Management
In the event of a failure or collision during the pick-and-place process in manufacturing, how does the system detect and interpret the issue?
Vention minimizes the risk of collision or system failure to pick the object through several safeguards.
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Collision-free path planning: The FoundationStereo model generates depth data which nvblox and the cuMotion library use to plan safe trajectories.
- Redundant Grasp Planning: The AI Operator automatically selects the best candidate to avoid collision from a large pool of grasp options. The system also verifies alignment after each pick. If accurate placement isn’t possible, the robot puts it back in the bin.
- Risk-Minization Strategies: A re-grip station allows the robot arm to switch positions and pick the object again at an optimized angle. Shaking the bin helps reposition the objects in case the initial picks fail.
- Future Improvements: A force feedback mechanism is planned to prevent excessive pressure from the robot to protect delicate parts.
Does the AI Operator need only a wrist camera, or is there another one overhead?
The AI Operator currently only needs the commodity stereo camera located on the wrist of the robot arm to capture stereo images. After running the images through the FoundationStereo model, the system is able to achieve very high-quality depth data.
Does the gripper in the AI Operator have a pressure sensor?
Yes. The AI Operator supports a range of grippers through Vention’s ecosystem of compatible parts, including options with built-in pressure sensors. Depending on the application, manufacturers can choose the most suitable gripper—for example, a finger gripper for standard objects or a vacuum gripper for pouches.
How far away is automated grasp planning—5/10/15 years?
Vention’s AI Operator can already do this to a certain extent. Each object can be picked up in a variety of ways, and the system can automatically select a collision-free grasp. While we can’t specify exact timelines, fully automated grasp planning represents a key development priority to improve the success rate of the grasp, and to choose grasps that lead to fast execution speed.
Physical AI represents a fundamental shift in manufacturing automation, transforming complex robotic programming into simple CAD-based workflows. These questions highlight the growing enthusiasm about the practical application of this technology. With Vention’s AI Operator, manufacturers have a realistic pathway to automate tasks once thought out of scope. Whether it’s random bin picking or delicate handling of specialized parts, the AI Operator is set to fully tap into the potential of AI in manufacturing.
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