Efficient Reinforcement Learning for 3D Jumping Monopods.

We consider a complex control problem: making a monopod accurately reach a target with a single jump. The monopod can jump in any direction at different elevations of the terrain. This is a paradigm for a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimization-based techniques.

Orientation Control System: Enhancing Aerial Maneuvers for Quadruped Robots.

For legged robots, aerial motions are the only option to overpass obstacles that cannot be circumvented with standard locomotion gaits. In these cases, the robot must perform a leap to either jump onto the obstacle or fly over it. However, these movements represent a challenge, because, during the flight phase, the Center of Mass (CoM) cannot be controlled, and there is limited controllability over the orientation of the robot.

A Simple Yet Effective Whole-Body Locomotion Framework for Quadruped Robots.

In the context of legged robotics, many criteria based on the control of the Center of Mass (CoM) have been developed to ensure a stable and safe robot locomotion. Defining a whole-body framework with the control of the CoM requires a planning strategy, often based on a specific type of gait and a reliable state-estimation. In a whole-body control approach, if the CoM task is not specified, the consequent redundancy can still be resolved by specifying a postural task that set references for all the joints.