Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased
datasets. While curated datasets can help, challenges remain in generalizability and real …

With the fast improvement of machine learning, reinforcement learning (RL) has been used
to automate human tasks in different areas. However, training such agents is difficult and …

The ability to apply a previously-learned skill (eg, pushing) to a new task (context or object)
is an important requirement for new-age robots. An attempt is made to solve this problem in …

Conventional works that learn grasping affordance from demonstrations need to explicitly
predict grasping configurations, such as gripper approaching angles or grasping preshapes …

Vision-based imitation learning has shown promising capabilities of endowing robots with
various motion skills given visual observation. However, current visuomotor policies fail to …

The ability to make educated predictions about their surroundings, and associate them with
certain confidence, is important for intelligent systems, like autonomous vehicles and robots …

This paper presents a framework for learning visual representations from unlabeled video
demonstrations captured from multiple viewpoints. We show that these representations are …

This paper presents a framework for learning visual representations from unlabeled video
demonstrations captured from multiple viewpoints. We show that these representations are …

Learning to accomplish complex tasks may require a tight coupling among different levels of
cognitive functions or components, like perception, acting, planning, and self-explaining …

Abstract In Activities of Daily Living (ADLs), humans perform thousands of arm and hand
object manipulation tasks, such as picking, pouring and drinking a drink. Interpreting such …