Traversing 3-D complex environments has always been a significant challenge for legged locomotion. Existing methods typically rely on external sensors such as vision and lidar to preemptively react to obstacles by acquiring environmental information. However, in scenarios like nighttime or dense forests, external sensors often fail to function properly, necessitating robots to rely on proprioceptive sensors to perceive diverse obstacles in the environment and respond promptly. This task is undeniably challenging. Our research finds that methods based on collision detection can enhance a robot’s perception of environmental obstacles. In this work, we propose an end-to-end learning-based quadruped robot motion controller that relies solely on proprioceptive sensing. This controller can accurately detect, localize, and agilely respond to collisions in unknown and complex 3D environments, thereby improving the robot’s traversability in complex environments. We demonstrate in both simulation and real-world experiments that our method enables quadruped robots to successfully traverse challenging obstacles in various complex environments. The videos and appendix can be found at Quad-Traverse-Go2.github.io

To realize the exquisite interaction and precise manipulation for the robot, in this article, we propose a multispectral multimodal visuotactile sensor named M$^{3}$Tac, which combines visible, near-infrared, and mid-infrared imaging technologies for the first time and can exceed the sensing ability of human skin in terms of resolution (719 pixels/cm$^{2}$), temperature sensing range (−20–130$^\text{o}$C), etc. The M$^{3}$Tac cannot only realize high-quality sensing of deformation, texture, force, stickiness, and temperature comparable to human skin but also can realize proximity sensing that is lacking for human skin. To achieve this, we not only design a multispectral imaging system with an elastic film whose light penetrability can be regulated by the brightness of the light, but also develop corresponding algorithms, including the pixel-level force sensing with finite element method (accuracy:$\pm$0.023N), the proximity perception (accuracy:$\pm$3.8 mm), the 3-D reconstruction (accuracy: 0.33 mm), the super-resolution temperature sensing (accuracy:$\pm 0.3^\text{o}$C), the multimodal fusion classification (accuracy: 98%), and the stickiness recognition (accuracy: 98%). Finally, we conduct experiments to verify the effectiveness and application potential of our research.