This article discusses four fundamental topics for future Distributed Computing Continuum
Systems: their representation, model, lifelong learning, and business model. Further, it …

Multi-task learning ideally allows embodied agents such as robots to acquire a diverse
repertoire of useful skills. However, many multi-task reinforcement learning efforts assume …

Training robots with reinforcement learning (RL) typically involves heavy interactions with
the environment, and the acquired skills are often sensitive to changes in task environments …

Lifelong seq2seq language generation models are trained with multiple domains in a
lifelong learning manner, with data from each domain being observed in an online fashion. It …

In learning an embodied agent executing daily tasks via language directives, the literature
largely assumes that the agent learns all training data at the beginning. We argue that such …

Robots will experience non-stationary environment dynamics throughout their lifetime: the
robot dynamics can change due to wear and tear, or its surroundings may change over time …

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient
multitask reinforcement learning (RL) method for industrial control applications that require …

Abstract Models based on the Transformer architecture have seen widespread application
across fields such as natural language processing (NLP), computer vision, and robotics, with …

Real-world deployment of machine learning models is challenging because data evolves
over time. While no model can work when data evolves in an arbitrary fashion, if there is …

Real-world environments require robots to continuously acquire new skills while retaining
previously learned abilities, all without the need for clearly defined task boundaries. Storing …