Research

Legged Robot Control

One of ARCAD lab's research focuses is humanoid robot control, where we leverage the unique ability of these machines to navigate human-designed environments. As interest in humanoid robotics grows, we explore advanced control techniques, particularly model predictive control (MPC). Our research aims to synthesize and stabilize dynamic motions on physical humanoid platforms, pushing the boundaries of their capabilities and real-world applications.

Orientation-aware model predictive control with footstep adaptation for dynamic humanoid walking
Dynamic walking with footstep adaptation on the mit humanoid via linear model predictive control
Humanoid Self-Collision Avoidance Using Whole-Body Control with Control Barrier Functions

Custom ROBOT Hardware

Our lab has the capability to design and fabricate custom robot hardware that fulfills a unique set of specifications of legged robots. Building upon the Quasi-Direct-Drive (QDD) actuator, we design and integrate hardware, ranging from custom actuators to novel mechanisms, to push the boundaries of robot actuation technology. We aim to enhance the mobility, efficiency, and overall functionality of legged robots in diverse environments.

dynamic motion planning

Long horizon planning for highly dynamic motion is key to pushing the boundary of capapble legged robots in unstructured environments. This line of research focuses on combining advanced optimization and sampling methods to synthesize parkour motions that navigates complex terrain while respecting dynamical constraints of the robot.