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.

Related Publications:

• 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

Related Publications:

• Representation-Free Model Predictive Control for Dynamic Motions in Quadrupeds

• qpSWIFT: A real-time sparse quadratic program solver for robotic applications

• Real-time model predictive control for versatile dynamic motions in quadrupedal robots

Our lab leverages recent advancements in Quasi-Direct-Drive (QDD) actuators to enhance humanoid robot performance. These high-bandwidth, compliant actuators have revolutionized legged locomotion, enabling more agile and adaptable movements. By designing and integrating custom hardware based on QDD technology, we aim to push the boundaries of humanoid robotics, focusing on improved mobility, stability, and overall functionality in diverse environments.

Related Publications:

• Design and development of the MIT humanoid: A dynamic and robust research platform

• Design and Experimental Implementation of a Quasi-Direct-Drive Leg for Optimized Jumping

• Hoppy: An open-source kit for education with dynamic legged robots

Related Publications:

• Hybrid Sampling/Optimization-based Planning for Agile Jumping Robots on Challenging Terrains

• Centroidal-momentum-based trajectory generation for legged locomotion

• Kinodynamic Motion Planning for Multi-Legged Robot Jumping via Mixed-Integer Convex Program

• Single Leg Dynamic Motion Planning with Mixed-Integer Convex Optimization