Terrain Adaptive Quadrupedal Jumping for Rigid and Articulated Soft Robots using Example Guided Reinforcement Learning
G. Apostolides (TU Delft - Mechanical Engineering)
Cosimo Della Santina – Mentor (Mechanical, Maritime and Materials Engineering)
Jiatao Ding – Mentor (TU Delft - Learning & Autonomous Control)
Jens Kober – Graduation committee member (TU Delft - Learning & Autonomous Control)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The challenge of navigating uneven terrain is a critical obstacle in the advancement of robotic
locomotion. Traditional quadrupedal locomotion methods, such as walking, are often
insufficient for dynamic and complex environments. Agile skills like jumping are necessary and
must be adaptable over uneven terrain. This study addresses this issue by developing a policy
for executing jumps over uneven terrain using a single demonstration. Initially, the system
learns to imitate a forward jump based on a single demonstration from a SLIP trajectory
planner. It then generalizes its jumping abilities to various distances in both forward and lateral
directions. The study compares the performance of systems with and without parallel elasticity,
demonstrating the energetic benefits of using elastic actuation for quadrupedal jumping.
Results show that the system with parallel elastic actuation is 15.20% more energy-efficient
and experiences a 15.79% reduction in peak power compared to the system without parallel
elasticity. A policy trained using the proposed methodology successfully performs jumps of
variable distances over uneven terrain with height perturbations of +/-4 cm using only
proprioceptive information.