Qualcomm made its India debut for its complete robotics technology platform at the AI Impact Summit 2026 in New Delhi, unveiling an end-to-end stack spanning hardware, software, and artificial intelligence designed to accelerate physical AI deployments across industries.
At the centre of the showcase was Qualcomm’s first dedicated robotics processor, the Dragonwing IQ-10, built specifically for full-size humanoid robots and advanced autonomous mobile robots. The chip is engineered to handle complex robotics workloads with high performance while minimising energy consumption — a key challenge in untethered robotic platforms that depend on battery power.
The platform first appeared at the Consumer Electronics Show in January before its India premier. The company positions the architecture as deployment-ready, supported by an ecosystem of partners and developer tools designed to accelerate industry-wide adoption.
Rather than pursuing general-purpose robotics immediately, Qualcomm has taken a task-based approach — concentrating development on 10 priority real-world tasks across logistics, manufacturing, and retail automation. This focused strategy allows deeper optimisation for specific use cases, making robots practically useful in commercial settings rather than technically impressive but operationally limited.
The platform integrates heterogeneous edge computing, mixed-criticality systems, machine learning operations, software frameworks, and an AI data flywheel — a feedback loop where real-world operational data continuously improves model performance. The modular design enables robots to adapt across different environments using integrated vision, audio, and motion capabilities.
Qualcomm also advocated a hybrid AI deployment model for India’s sovereign AI ambitions — combining on-device intelligence with cloud computing to ensure scalability and affordability at national scale. The company highlighted that 7 to 10 billion parameter models can already run fully on-device today, along with automatic speech recognition and text-to-speech capabilities, reducing dependence on cloud infrastructure for real-time operations.
This architecture is particularly relevant for India, where connectivity remains variable across geographies and low-latency, cost-effective AI deployment matters enormously for last-mile applications.
