Obstacle-negotiation performance on challenging terrain for a parallel leg-wheeled robot

Abstract

Reinforcement Learning (RL) has the potential to enable extreme off-road mobility by circumventing complex kinodynamic modeling, planning, and control by simulated endto-end trial-and-error learning experiences. However, most RL methods are sample-inefficient when training in a large amount of manually designed simulation environments and struggle at generalizing to the real world. To address these issues, we introduce Verti-Selector (VS), an automatic curriculum learning framework designed to enhance learning efficiency and generalization by selectively sampling training terrain. VS prioritizes vertically challenging terrain with higher Temporal Difference (TD) errors when revisited, thereby allowing robots to learn at the edge of their evolving capabilities. By dynamically adjusting the sampling focus, VS significantly boosts sample efficiency and generalization within the VW-Chrono simulator built on the Chrono multi-physics engine. Furthermore, we provide simulation and physical results using VS on a Verti-4-Wheeler platform. These results demonstrate that VS can achieve 23.08% improvement in terms of success rate by efficiently sampling during training and robustly generalizing to the real world.

Bionic Design of Multi-Toe Quadruped Robot for Planetary Surface Exploration

“…For conducting these tasks, a series of planetary surface rovers have to be utilized. Because the lunar and Martian surfaces are covered with granular materials, and the surface gravities are significantly lower than for Earth [7], researchers mainly proposed four types of rover structures to be suited to such environments, namely, wheeled [4,8], crawler [9], legged [10,11], and leg-wheeled [12,13].…”