One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

Continual learning aims to emulate the human ability to continually accumulate knowledge
over sequential tasks. The main challenge is to maintain performance on previously learned …

Machine learning is a crucial tool for both academic and real-world applications.
Classification problems are often used as the preferred showcase in this space, which has …

Sim2Real transfer has gained popularity because it helps transfer from inexpensive
simulators to real world. This paper presents a novel system that fuses components in a …

In the ever-evolving field of technologies, the emergence of Artificial General Intelligence
(AGI), often referred as strong artificial intelligence (AI), stands as a breakthrough in the …

As machine learning moves towards edge deployment, lifelong learning becomes crucial
due to evolving data distributions and new tasks. Yet, applying traditional methods to learn …

Imitation learning has gained immense popularity because of its high sample-efficiency.
However, in real-world scenarios, where the trajectory distribution of most of the tasks …

In this paper, we try to improve exploration in Blackbox methods, particularly Evolution
strategies (ES), when applied to Reinforcement Learning (RL) problems where intermediate …

Deep networks are frequently tuned to novel tasks and continue learning from ongoing data
streams. Such sequential training requires consolidation of new and past information, a …

Artificial Intelligence (AI) systems are increasingly deployed in real-time applications that
require optimal performance and rapid adaptability. To address this need, we introduce a …