Embodied Intelligence
Center for Embodied Intelligent Systems

This Indo-US Knowledge R&D Networked Joint Center for Embodied Intelligent Systems was established with the main goal to enable Indian and American scientists to carry out joint research activity by leveraging existing infrastructure and funding available with the partners at both sides.


TheCenter aims to encourage joint projects in focal  research areas thus, paving way to sustainable interactions by promoting excellence and developing long term relationship based on synergy of activities. The Networked Center also provides opportunities for integrating research with education.


Goals of the Center

This Center is focused on the design and development of computational systems that learn operate and communicate in a changing, unpredictable environment by developing high-level representation and motor abilities. The objective is to design biologically inspired, self-organizing, motivated learning and problem-solving tools in order to build powerful systems capable of formulating and achieving complex goals and of communicating and interacting with their environments using limited computational resources. 

The Center will advance understanding of the role of interaction with environment for perception, cognition and intelligence. Specific objectives are to develop an integrated computational model of basal ganglia that explain the multifaceted function of this brain circuit, design learning-memory-anticipation framework, and build biologically inspired network models of sensory-motor functions.

Research will focus on theoretical aspects of motivated intelligent systems development and their applications to robots and virtual agents.  Specific application areas will include machine learning, learning algorithms, learning architectures, natural language understanding, text-to-speech systems, blind-source separation, mobile robotics, audio processing and image processing, intelligent control, visual motor coordination, sentiment analysis, machine translation, case based reasoning, intrinsically motivated RL algorithms and developmental models of RL. This Center aims at advancing machine learning and cognition and improving learning agentsí capabilities.