Labor shortages are still fueling growth at automation firms like GrayMatter
How are labor shortages influencing venture funding in robotics?
Labor shortages are a significant factor driving venture funding in robotics, particularly in warehouse and manufacturing processes3. Analyst firm Garner predicts that by 2028, half of large enterprises will employ robots in these areas1. Companies like GrayMatter Robotics, which provides "physics-based" AI-powered systems, are benefiting from this trend, receiving millions in funding to address labor shortages and enhance productivity in manufacturing.
How does GrayMatter handle constraints in its AI-powered systems?
GrayMatter handles constraints in its AI-powered systems by using more complex representations and associated solution generation methods. This approach ensures that the systems can handle constraints and produce acceptable computational performance, rather than relying on a simple neural network trained with observed input and output data.
What distinguishes a “physics-based” robotics system from data-driven systems?
A "physics-based" robotics system differs from data-driven systems by incorporating physical principles and constraints into its models, ensuring that the system's behavior aligns with real-world expectations2. This approach contrasts with purely data-driven methods that rely solely on input-output data, which may not guarantee constraint preservation or require more complex representations for handling constraints.