Generalizable manipulation skills, which can be composed to tackle long-horizon and
complex daily chores, are one of the cornerstones of Embodied AI. However, existing …

In this work, we propose a unified framework, called Visual Reasoning with Differ-entiable
Physics (VRDP), that can jointly learn visual concepts and infer physics models of objects …

The empirical success of reinforcement learning (RL) in contact-rich manipulation leaves
much to be understood from a model-based perspective, where the key difficulties are often …

Modeling and manipulating elasto-plastic objects are essential capabilities for robots to
perform complex industrial and household interaction tasks (eg, stuffing dumplings, rolling …

Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use:
bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded …

Deep reinforcement learning can generate complex control policies, but requires large
amounts of training data to work effectively. Recent work has attempted to address this issue …

The current dominant paradigm for robotic manipulation involves two separate stages:
manipulator design and control. Because the robot's morphology and how it can be …

We propose a hybrid neural network (NN) and PDE approach for learning generalizable
PDE dynamics from motion observations. Many NN approaches learn an end-to-end model …

Existing approaches to system identification (estimating the physical parameters of an
object) from videos assume known object geometries. This precludes their applicability in a …

Understanding and manipulating deformable objects (eg, ropes and fabrics) is an essential
yet challenging task with broad applications. Difficulties come from complex states and …