Robotics

Creating animations for robotic characters is very challenging due to the constraints imposed by their physical nature. In particular, the combination of fast motions and unavoidable structural deformations leads to mechanical oscillations that negatively affect their performances. Our goal is to automatically transfer motions created using traditional animation software to robotic characters while avoiding such artifacts.

In this paper, we propose a trajectory-based reinforcement learning method named deep latent policy gradient (DLPG) for learning locomotion skills.

We propose a structural optimization that jointly optimizes an ornament’s strength-to-weight ratio and balance under self-weight, thermal, wind, and live loads.

This paper describes a system for autonomous spray painting using a UAV, suitable for industrial applications.

The paper describes a complete working pipeline to build a globally consistent map of a given ground-plane and subsequently to localize within this map at real-time.

We demonstrate a nested controller using temperature and position feedback to improve contraction speed, and investigate the cooling rates of various configurations that increase total force output.

We present the design and performance evaluation of a bipedal robot that utilizes the Hybrid Leg mechanism.

We demonstrate the effectiveness of our computational design method by automatically creating a variety of robotic manipulators and legged robots.

We present an automated learning environment for developing control policies directly on the hardware of a modular legged robot.

We present a novel computational approach to optimizing the morphological design of robots.