Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Perceiving and handling deformable objects is an integral part of everyday life for humans.
Automating tasks such as food handling, garment sorting, or assistive dressing requires …

Long-horizon tasks, usually characterized by complex subtask dependencies, present a
significant challenge in manipulation planning. Skill chaining is a practical approach to …

Deformable object manipulation (DOM) is an emerging research problem in robotics. The
ability to manipulate deformable objects endows robots with higher autonomy and promises …

Develo** controllers for physics-based character simulation and control is one of the core
challenges of computer animation. In recent years, techniques that combine deep …

Many anatomical factors, such as bone geometry and muscle condition, interact to affect
human movements. This work aims to build a comprehensive musculoskeletal model and its …

Many real-world manipulation tasks consist of a series of subtasks that are significantly
different from one another. Such long-horizon, complex tasks highlight the potential of …

Human characters with a broad range of natural looking and physically realistic behaviors
will enable the construction of compelling interactive experiences. In this paper, we develop …

Human character animation is often critical in entertainment content production, including
video games, virtual reality or fiction films. To this end, deep neural networks drive most …

We demonstrate model-based, visual robot manipulation of deformable linear objects. Our
approach is based on a state-space representation of the physical system that the robot …