This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …

In a world designed for legs, quadrupeds, bipeds, and humanoids have the opportunity to
impact emerging robotics applications from logistics, to agriculture, to home assistance. The …

We present a single trajectory optimization formulation for legged locomotion that
automatically determines the gait sequence, step timings, footholds, swing-leg motions, and …

Collaborative robotics is a possible solution to the problem of musculoskeletal disorders
(MSDs) in industry, but efficiently designing such robots remains an issue because …

We present a contact invariant trajectory optimization formulation to synthesize motions for
legged robotic systems. The method is capable of finding optimal trajectories subject to …

Linear models for control and motion generation of humanoid robots have received
significant attention in the past years, not only due to their well known theoretical …

This article presents a planner to generate walking trajectories by using the centroidal
dynamics and the full kinematics of a humanoid robot. The interaction between the robot and …

Humanoid robots dynamically navigate an environment by interacting with it via contact
wrenches exerted at intermittent contact poses. Therefore, it is important to consider …

We present a novel MPC method for humanoid gait generation that is guaranteed to
produce stable CoM trajectories. This is obtained by using a dynamic extension of the LIP as …

This study introduces a robust planning framework that utilizes a model predictive control
(MPC) approach, enhanced by incorporating signal temporal logic (STL) specifications. This …