Creating autonomous robots that can actively explore the environment, acquire knowledge
and learn skills continuously is the ultimate achievement envisioned in cognitive and …

Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …

Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …

With the recent development of deep learning technology comes the wide use of artificial
intelligence (AI) models in various domains. AI shows good performance for definite …

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

This paper studies a model learning and online planning approach towards building flexible
and general robots. Specifically, we investigate how to exploit the locality and sparsity …

We provide a framework to accelerate reinforcement learning (RL) algorithms by heuristics
that are constructed by domain knowledge or offline data. Tabula rasa RL algorithms require …

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …

Efficient planning in continuous state and action spaces is fundamentally hard, even when
the transition model is deterministic and known. One way to alleviate this challenge is to …

An effective approach to solving long-horizon tasks in robotics domains with continuous
state and action spaces is bilevel planning, wherein a high-level search over an abstraction …