Soft Robotics, a Bedford, Mass., startup, is using NVIDIA Isaac Sim to help close the ‘sim to real gap’ for robotic gripping applications. One area is perfecting gripping for pick and placement of foods for packaging. Unlike other industries that have adopted robotics, the $8 trillion food market has been slow to develop robots to handle variable items in unstructured environments, says Soft Robotics.
Soft Robotics develops models for gripping applications, each requiring specific datasets. And picking from piles of wet, slippery chicken and other foods can be a tricky challenge. Utilizing Omniverse and Isaac Sim, the company can create 3D renderings of chicken parts with different backgrounds, like on conveyor belts or in bins, and with different lighting scenarios.
The company taps into Isaac Replicator to develop synthetic data, generating hundreds of thousands of images per model and distributing that among an array of instances in the cloud. Isaac Replicator is a set of tools, APIs and workflows for generating synthetic data using Isaac Sim. It also runs pose estimation models to help its gripping system see the angle of the item to pick.
NVIDIA A100 Tensor Core GPUs enable Soft Robotics to run split-second inference with the models for each application in these food-processing facilities. Meanwhile, simulation and training in Isaac Sim offers access to NVIDIA A100 GPUs for scaling up workloads.
The company, founded in 2013, recently landed $26 million in Series C funding from Tyson Ventures, Marel and Johnsonville Ventures. Companies such as Tyson Foods and Johnsonville are betting on adoption of robotic automation to help improve safety and increase production in their facilities. Both companies rely on Soft Robotics technologies.
Soft Robotics is a member of the NVIDIA Inception program, which provides companies with GPU support and AI platforms guidance.
