The advancement of physical and embodied AI is facilitating a shift away from traditional repeatable-motion automation toward robots that can perceive, make decisions, and act in unpredictable real-world conditions. This will enable highly dexterous manipulation that exceeds conventional pick-and-place automation. As learning-based policies, foundation-model methods, and multimodal sensing capabilities (vision, force/torque, and tactile feedback) are refined, robots will increasingly be able to manage contact-rich interactions, detect and recover from errors, and adapt to variations in parts, clutter, lighting conditions, and tolerances.

Over the next three to five years, progress in this realm will be characterized less by isolated demonstrations and more by quantifiable advancements in robustness, recovery capability, and economic reliability, ultimately reducing the need for human intervention and broadening the range of feasible applications in high-mix manufacturing, logistics, and hazardous operations.

• What are the technological developments shaping dexterous robotic manipulation and influencing their growth, commercialization, and adoption across industrial environments?
• Which growth avenues are emerging due to transformative megatrends and next-gen robotics technology domains like physical AI, hybrid robots, and humanoids?
• How can you optimize growth strategies based on system architectures and the evolution from classical control to learning-based manipulation and foundation-model policies?