Diligently gathered human demonstrations serve as the unsung heroes empowering the
progression of robot learning. Today, demonstrations are collected by training people to use …

We present a novel learning and control framework that combines artificial neural networks
with online trajectory optimization to learn dexterous manipulation skills from human …

We introduce the Universal Manipulation Policy Network (UMPNet)–a single image-based
policy network that infers closed-loop action sequences for manipulating articulated objects …

Generating human-like behavior on robots is a great challenge especially in dexterous
manipulation tasks with robotic hands. Scripting policies from scratch is intractable due to …

We present iGibson 1.0, a novel simulation environment to develop robotic solutions for
interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interactive …

Robot learning has emerged as a promising tool for taming the complexity and diversity of
the real world. Methods based on high-capacity models, such as deep networks, hold the …

Imitation learning from a large set of human demonstrations has proved to be an effective
paradigm for building capable robot agents. However, the demonstrations can be extremely …

Reinforcement learning (RL) algorithms face significant challenges when dealing with long-
horizon robot manipulation tasks in real-world environments due to sample inefficiency and …

Complex object manipulation tasks often span over long sequences of operations. Task
planning over long-time horizons is a challenging and open problem in robotics, and its …

Developing personal robots that can perform a diverse range of manipulation tasks in
unstructured environments necessitates solving several challenges for robotic grasping …