A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

We describe a framework for research and evaluation in Embodied AI. Our proposal is
based on a canonical task: Rearrangement. A standard task can focus the development of …

Humans universally dislike the task of cleaning up a messy room. If machines were to help
us with this task, they must understand human criteria for regular arrangements, such as …

There has been a significant recent progress in the field of Embodied AI with researchers
developing models and algorithms enabling embodied agents to navigate and interact …

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the
robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level …

We present a novel method for learning and predicting the affordances of an object based
on its physical and visual attributes. Affordance prediction is a key task in autonomous robot …

In a realistic mobile push-manipulation scenario, it becomes non-trivial and infeasible to
build analytical models that will capture the complexity of the interactions between the …

The ability to predict how objects behave during manipulation is an important problem.
Models informed by mechanics are powerful, but are hard to tune. An alternative is to learn a …

Data efficiency in robotic skill acquisition is crucial for operating robots in varied small-batch
assembly settings. To operate in such environments, robots must have robust obstacle …

In this work, we present an anytime planner for creating open-loop trajectories that solve
rearrangement planning problems under uncertainty using nonprehensile manipulation. We …