Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …

Conceptual abstraction and analogy‐making are key abilities underlying humans' abilities to
learn, reason, and robustly adapt their knowledge to new domains. Despite a long history of …

Expert problem-solving is driven by powerful languages for thinking about problems and
their solutions. Acquiring expertise means learning these languages—systems of concepts …

We aim to endow a robot with the ability to learn manipulation concepts that link natural
language instructions to motor skills. Our goal is to learn a single multi-task policy that takes …

Cognitive maps are mental representations of spatial and conceptual relationships in an
environment, and are critical for flexible behavior. To form these abstract maps, the …

This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing
library functions that capture common functionality from a corpus of programs in a domain …

The ever-changing nature of human environments presents great challenges to robot
manipulation. Objects that robots must manipulate vary in shape, weight, and configuration …

Recently, deep reinforcement learning (DRL) methods have achieved impressive
performance on tasks in a variety of domains. However, neural network policies produced …

Today's applications are using machine learning algorithms to analyze the data collected
from a swarm of devices on the Internet of Things (IoT). However, most existing learning …

Library learning compresses a given corpus of programs by extracting common structure
from the corpus into reusable library functions. Prior work on library learning suffers from two …