Tata Consultancy Services Limited

Harshad Khadilkar

Hardik Meisheri

Venkat Raju Chintalapalli Patta

Rajesh Kumar Sinha

Aniruddha Singhal

Vinay Kulkarni

Souvik Barat

Vinita Baniwal

Prashant Kumar

Venkatesh Srinivas Prasad

Deepak Raina

Richa Verma

Shivam Thukral

Monika Gajrani

Omkar Dilip Shelke

Durgesh Kalwar

Pranavi Pathakota

The present disclosure provides a Reinforcement Learning (RL) based architecture to efficiently learn action embeddings in low dimensional space. In conventional methods, the embeddings are learnt with the sole objective of improving policy learning, and there are no specific requirements on the quality of the embeddings. Initially, the system receives a goal to be reached by a mobile robot and a current location of the mobile robot is obtained. Simultaneously current transition dynamics associated with the plurality of directional actuators are obtained using a Reinforcement Learning (RL) technique. Further, a plurality of embeddings is computed based on the current location of the mobile robot and the current transition dynamics using a trained Dual Channel Training (DCT) based autoencoder decoder model. Finally, a displacement vector for current navigation of the mobile robot is computed based on the computed plurality of embeddings using the RL technique.

Method and system for reinforcement learning and dual channel action embedding based robotic navigation

Generally, in complex systems such as business organizations and supply chain networks, decisions making and control are challenging due to dynamic environment thereof. The embodiments herein provide method and system that uses reinforcement learning (RL) for exploring policies and deciding control actions, and actor-based modelling and simulation for performing accurate long-term rollouts of the policies, in order to optimize operation of the complex systems. In an embodiment, a method for actor based simulation of complex system includes modeling, actors of the complex system using an actor abstraction, where actors includes RL agents embodied in each subsystem of the complex system, and executes micro-level interactions amongst the subsystems. An emergent macro-behavior of the complex system is simulated by the using the RL agents based on the micro-level interactions. Optimal decisions pertaining to actions of the RL agents are learnt based on the emergent macro-behavior of the complex subsystem.

System and method for actor based simulation of complex system using reinforcement learning

State of the art automated bin packing systems fail to handle dynamic scenarios in which information on dimensions of objects to be loaded is not available in advance. These systems also fail to consider capabilities of robots used for the automated packing of objects/bins. The disclosure herein generally relates to automated bin packing, and, more particularly, to a system and method for autonomous multi-bin parcel loading system. The system handles an online object packing in which information on dimensions of objects to be loaded is not available in advance. The system is also configured to consider capabilities of one or more robots used for loading objects to containers, while generating recommendations for object packing.

System and method for autonomous multi-bin parcel loading system

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Thane West, India

Harshad Khadilkar