No abstract

This paper introduces SCALER, a quadrupedal robot that demonstrates climbing on bouldering walls, overhangs, ceilings and trotting on the ground. SCALER is one of the first high-degrees of freedom four-limbed robots that can free-climb under the Earth's gravity and one of the most mechanically efficient quadrupeds on the ground. Where other state-of-the-art climbers specialize in climbing, SCALER promises practical free-climbing with payload and ground locomotion, which realizes true versatile mobility. A new climbing gait, SKATE gait, increases the payload by utilizing the SCALER body linkage mechanism. SCALER achieves a maximum normalized locomotion speed of 1.87 /s, or 0.56 m/s on the ground and 1.0 /min, or 0.35 m/min in bouldering wall climbing. Payload capacity reaches 233 % of the SCALER weight on the ground and 35 % on the vertical wall. Our GOAT gripper, a mechanically adaptable underactuated two-finger gripper, successfully grasps convex and non-convex objects and supports SCALER.

Bird's-Eye View (BEV) Perception has received increasing attention in recent years as it provides a concise and unified spatial representation across views and benefits a diverse set of downstream driving applications. While the focus has been placed on discriminative tasks such as BEV segmentation, the dual generative task of creating streetview images from a BEV layout has rarely been explored. The ability to generate realistic street-view images that align with a given HD map and traffic layout is critical for visualizing complex traffic scenarios and developing robust perception models for autonomous driving. In this paper, we propose BEVGen, a conditional generative model that synthesizes a set of realistic and spatially consistent surrounding images that match the BEV layout of a traffic scenario. BEVGen incorporates a novel cross-view transformation and spatial attention design which learn the relationship between cameras and map views to ensure their consistency. Our model can accurately render road and lane lines, as well as generate traffic scenes under different weather conditions and times of day. The code will be made publicly available.