We present a large-scale study of imitating human demonstrations on tasks that require a
virtual robot to search for objects in new environments-(1) ObjectGoal Navigation (eg'find & …

Abstract We study ObjectGoal Navigation--where a virtual robot situated in a new
environment is asked to navigate to an object. Prior work has shown that imitation learning …

Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

We approach the problem of learning by watching humans in the wild. While traditional
approaches in Imitation and Reinforcement Learning are promising for learning in the real …

Reward function design and exploration time are arguably the biggest obstacles to the
deployment of reinforcement learning (RL) agents in the real world. In many real-world …

Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex
behaviors through experience. However, realizing this promise for long-horizon tasks in the …

Vision-based teleoperation offers the possibility to endow robots with human-level
intelligence to physically interact with the environment, while only requiring low-cost camera …

Experience precedes understanding. Humans constantly explore and learn about their
environment out of curiosity, gather information, and update their models of the world. On 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 …