ICC 2025 RAS Workshop

About the Workshop

The rapid advancement of artificial intelligence (AI) is reshaping the landscape of control and estimation in complex dynamic systems especially in robotics and cyber-physical systems. This workshop aims to bring together researcher and practitioners at the intersection of control theory, artificial intelligence and robotics to explore generalized frameworks for control and estimation that are robust, scalable and adaptable to uncertain, high-dimensional and data-rich environment. Robotic systems with their nonlinear, high DOF dynamics and real-time interaction with unstructured environments provide a compelling platform for advancing and testing new-age methodologies. At the same time similar challenges arise across a wide range of complex systems - from unmanned autonomous vehicle and power system to biological networks and industrial processes.

This workshop will cover recent developments in both model based and data-driven approaches, including reinforcement learning, adaptive and robust control, observer design, distributed estimation, and hybris techniques that blend classical control with AI driven methods. Through invited talks, contributed presentations and panel discussions, the workshop will foster dialogue on open challenges, foundational principles, and future direction for control and estimation in the age of AI.

What to Expect

Invited Keynote Talks

Leading researchers from academia and industry

Contributed Research Presentations

Peer-reviewed extended abstracts to be presented as short talks and posters

Panel Discussion

Advances in AI-Driven Control and Estimation for Robotics

Objectives

To present cutting-edge research on ML/AI-driven control of robots and autonomous systems
To discuss frameworks for continual learning and adaptation in robotics
To explore learning-based locomotion and navigation in dynamic and uncertain environments
To create a dialogue between control theorists, machine learning experts, and roboticists on unifying principles of generalization and adaptability
To identify benchmarks, datasets, and open problems in the field

Topics of Interest

The workshop will include a blend of invited talks, contributed papers, and interactive sessions:

Learning-based manipulation with reinforcement learning and imitation learning
Generalization of controllers across tasks, robots and autonomous systems
Sim-to-real transfer for manipulation and dexterous control
Continual and adaptive learning frameworks for autonomous systems
Data-driven locomotion control for bipedal, quadrupedal, and wheeled robots
Multimodal learning with vision, language, and sensor fusion for autonomous navigation
Robots in social collaboration and care giving

Target Audience

Academic researchers in robotics, AI, and control theory
Graduate students and postdoctoral fellows working in learning-based robotics
Industry professionals in autonomous systems, manufacturing, and service robotics
Practitioners interested in lifelong learning and adaptive AI for robotics

Indian Control Conference 2025 RAS Workshop

Advances in AI-Driven Control and Estimation for Robotics and Autonomous Systems