Learning to fall: Designing low damage fall sequences for humanoid soccer robots

Javier Ruiz-del-Solar, Rodrigo Palma Amestoy, Román Marchant, Isao Parra-Tsunekawa, P. Zegers

Abstract: A methodology for the analysis and design of fall sequences of robots that minimize joint/articulation injuries, and the damage of valuable body parts is proposed. These fall sequences can be activated/triggered by the robot in case of a detected unintentional fall or an intentional fall, which are common events in humanoid soccer environments. The methodology is human-based and requires the use of a realistic simulator as development tool. The obtained results show that fall sequences designed using the proposed method produce less damage than standard, uncontrolled falls.