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The wheel-legged hybrid robot (WLHR) is capable of adapting height and wheelbase configuration to traverse obstacles or rolling in confined space. Compared with legged and wheeled machines, it can be applied for more challenging mobile robotic exercises using the enhanced environment adapting performance. To make full use of the deformability and traversability of WHLR with parallel Stewart mechanism, this paper presents an optimization-driven planning framework for WHLR with parallel Stewart mechanism by abstracting the robot as a deformable bounding box. It will improve the obstacle negotiation ability of the high degree-of-freedoms robot, resulting in a shorter path through adjusting wheelbase of support polygon or trunk height instead of using a fixed configuration for wheeled robots. In the planning framework, we firstly proposed a pre-calculated signed distance field (SDF) mapping method based on point cloud data collected from a lidar sensor and a KD -tree-based point cloud fusion approach. Then, a covariant gradient optimization method is presented, which generates smooth, deformable-configuration, as well as collision-free trajectories in confined narrow spaces. Finally, with the user-defined driving velocity and position as motion inputs, obstacle-avoidancing actions including expanding or shrinking foothold polygon and lifting trunk were effectively testified in realistic conditions, demonstrating the practicability of our methodology. We analyzed the success rate of proposed framework in four different terrain scenarios through deforming configuration rather than bypassing obstacles.

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.

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