Mobile robots are widely used in the surveillance industry, for military and industrial applications. In order to carry out surveillance tasks like urban search and rescue operation, the ability to traverse stairs is of immense significance. This paper presents a deep learning-based approach for semantic segmentation of stairs, behavioral cloning for stair alignment, and a novel mechanical design for an autonomous stair climbing robot. The main objective is to solve the problem of locomotion over staircases with the proposed implementation. Alignment of a robot with stairs in an image is a traditional problem, and the most recent approaches are centered around hand-crafted texture-based Gabor filters and stair detection techniques. However, we could arrive at a more scalable and robust pipeline for alignment schemes. The proposed deep learning technique eliminates the need for manual tuning of parameters of the edge detector, the Hough accumulator and PID constants. The empirical results and architecture of stair alignment pipeline are demonstrated in this paper.

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We address the problem of goal-directed cloth manipulation, a challenging task due to the deformability of cloth. Our insight is that optical flow, a technique normally used for motion estimation in video, can also provide an effective representation for corresponding cloth poses across observation and goal images. We introduce FabricFlowNet (FFN), a cloth manipulation policy that leverages flow as both an input and as an action representation to improve performance. FabricFlowNet also elegantly switches between dual-arm and singlearm actions based on the desired goal. We show that FabricFlowNet significantly outperforms state-of-the-art model-free and model-based cloth manipulation policies. We also present real-world experiments on a bimanual system, demonstrating effective sim-to-real transfer. Finally, we show that our method generalizes when trained on a single square cloth to other cloth shapes, such as T-shirts and rectangular cloths. Video and other supplementary materials are available at: https://sites.google.com/view/fabricflownet.