We support different padding strategies and alphabet sizes, but by default we choose an alphabet size of 96, where the last character is used for padding.

We support different padding strategies and alphabet sizes, but by default we choose an alphabet size of 96, where the last character is used for padding.

Abstract--An accurate odometry is essential for legged-wheel robots operating in unstructured terrains such as bumpy roads and staircases. Existing methods often suffer from pose drift due to their ignorance of terrain geometry. We propose a terrain-awared LiDAR-Inertial odometry (LIO) framework that approximates the terrain using Radial Basis Functions (RBF) whose centers are adaptively selected and weights are recursively updated. The resulting smooth terrain manifold enables "soft constraints" that regularize the odometry optimization and mitigates the z-axis pose drift under abrupt elevation changes during robot's maneuver. To ensure the LIO's real-time performance, we further evaluate the RBF-related terms and calculate the inverse of the sparse kernel matrix with GPU parallelization. Experiments on unstructured terrains demonstrate that our method achieves higher localization accuracy than the state-of-the-art baselines, especially in the scenarios that have continuous height changes or sparse features when abrupt height changes occur. EGGED-WHEEL robots combine the speed advantage of wheeled robots with the terrain adaptability advantage of legged robots. Thus, they are well-suited for traversing complex and uneven environments such as bumpy roads, staircases, etc. However, the uneven surface in these environments will cause impulsive velocity variations during the robot's maneuver.

Abstract-- For robots to operate autonomously in densely cluttered environments, they must reason about and potentially physically interact with obstacles to clear a path. Safely clearing a path on challenging terrain, such as a cluttered staircase, requires controlled interaction. For example, a quadrupedal robot that pushes objects out of the way with one leg while maintaining a stable stance with its three other legs. However, tightly coupled physical actions, such as one-legged pushing, create new constraints on the system that can be difficult to predict at design time. In this work, we present a new method that addresses one such constraint, wherein the object being pushed by a quadrupedal robot with one of its legs becomes occluded from the robot's sensors during manipulation. T o address this challenge, we present a tightly coupled perception-action framework that enables the robot to perceive clutter, reason about feasible push paths, and execute the clearing maneuver . Our core contribution is an interaction-aware state estimation loop that uses proprioceptive feedback regarding foot contact and leg position to predict an object's displacement during the occlusion. This prediction guides the perception system to robustly re-detect the object after the interaction, closing the loop between action and sensing to enable accurate tracking even after partial pushes. Using this feedback allows the robot to learn from physical outcomes, reclassifying an object as immovable if a push fails due to it being too heavy. We present results of implementing our approach on a Boston Dynamics Spot robot that show our interaction-aware approach achieves higher task success rates and tracking accuracy in pushing objects on stairs compared to open-loop baselines.

-- Staircase perception is critical for humanoid robots and mobility -impaired individuals, yet existing methods have low accuracy, lighting sensitivity, and texture dependency. To address this, we propose a novel polarization-visual fusion framework that achieves robust staircase detection and high -precision the three-dimensional (3D) reconstruction, establishing a paradigm of S taircase recognition -- Heterogeneous sensor calibration (monocular and TOF camera) -- Polarization 3D reconstruction . First, the staircase recognition algorithm based on YOLOv11 integrated with polarization-intensity contrast enhancement algorithm and point cloud segmentation is improved, reaching recognition accuracy of 98.7% 0.10% by suppressing reflections and correcting by r edundant information of point cloud. Then, an improved gray wolf optimizer with Levy flight and d ynamic weights enable s accurate heterogeneous sensor calibration ( 0.33 0.04 mm error) between heterogeneous-resolution cameras is employed . Finally, a method of fusing polarized binocular and TOF depth information to realize the 3D reconstruction of the staircase is proposed . Considering the ambiguity in polarization reconstruction and the data holes in binocular reconstruction, b inocular vision is used to correct polarization azimuth ambiguity, TOF is used to fill data holes from stereo matching. Experiments show our method achieves <0.2% reconstruction error at 0.5m - significantly outperforming binocular (surface distortion) and polarization-based (normal vector ambiguity) approaches. This technology provides accurate terrain adaptation for robot ic foothold planning. INTRODUCTION A s a general scene, the staircase interferes with the traversal of h umanoid robots, legged robots, lower limb disabilities, or visually impaired individuals due to its special physical structure. Accurate staircase recognition technology is a prerequisite for navigation and control, and staircase recognition technology has attracted the attention of man y scholars [1],[2],[3] . Staircase recognition is of great significance for the mode switching and foothold position calculation of robots, which can improve the overall performance of robots in stair case scenes. As a common terrain, stairs are very difficult for humanoid robots and people with lower limb disabilities or visual impairments. Therefore, it is of great significance to design a staircase scene perception algorithm. At present, the staircase recognition is mainly applied in the fields of rehabilitation medicine and humanoid robots [ 4 ].

Recent research shows how diffusion models can unconditionally generate tile-based game levels, but use of diffusion models for text-to-level generation is underexplored. There are practical considerations for creating a usable model: caption/level pairs are needed, as is a text embedding model, and a way of generating entire playable levels, rather than individual scenes. We present strategies to automatically assign descriptive captions to an existing dataset, and train diffusion models using both pretrained text encoders and simple transformer models trained from scratch. Captions are automatically assigned to generated scenes so that the degree of overlap between input and output captions can be compared. We also assess the diversity and playability of the resulting level scenes. Results are compared with an unconditional diffusion model and a generative adversarial network, as well as the text-to-level approaches Five-Dollar Model and MarioGPT. Notably, the best diffusion model uses a simple transformer model for text embedding, and takes less time to train than diffusion models employing more complex text encoders, indicating that reliance on larger language models is not necessary. We also present a GUI allowing designers to construct long levels from model-generated scenes.

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-- Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty introduced by robot movement. For example, a robot climbing a cluttered staircase can misinterpret clutter as a step, misrepresenting the state and compromising safety. This requires robust state estimation methods capable of inferring the underlying structure of the environment even from incomplete sensor data. In this paper, we introduce a novel method for robust state estimation of staircases. T o address the challenge of perceiving occluded staircases extending beyond the robot's field-of-view, our approach combines an infinite-width staircase representation with a finite endpoint state to capture the overall staircase structure. This representation is integrated into a Bayesian inference framework to fuse noisy measurements enabling accurate estimation of staircase location even with partial observations and occlusions. Additionally, we present a segmentation algorithm that works in conjunction with the staircase estimation pipeline to accurately identify clutter-free regions on a staircase. Our method is extensively evaluated on real robot across diverse staircases, demonstrating significant improvements in estimation accuracy and segmentation performance compared to baseline approaches. Staircases, an ubiquitous feature of human-built environments throughout history, have enabled access to different levels within structures.

V ersatile Locomotion Skills for Hexapod Robots Tomson Qu 1, Dichen Li 1, Avideh Zakhor 1, Wenhao Y u 2, Tingnan Zhang 2 Abstract -- Hexapod robots are potentially suitable for carrying out tasks in cluttered environments since they are stable, compact, and light weight. They also have multi-joint legs and variable height bodies that make them good candidates for tasks such as stairs climbing and squeezing under objects in a typical home environment or an attic. Expanding on our previous work on joist climbing in attics, we train a legged hexapod equipped with a depth camera and visual inertial odometry (VIO) to perform three tasks: climbing stairs, avoiding obstacles, and squeezing under obstacles such as a table. Our policies are trained with simulation data only and can be deployed on low-cost hardware not requiring real-time joint state feedback. We train our model in a teacher-student model with 2 phases: In phase 1, we use reinforcement learning with access to privileged information such as height maps and joint feedback. In phase 2, we use supervised learning to distill the model into one with access to only onboard observations, consisting of egocentric depth images and robot pose captured by a tracking VIO camera. By manipulating available privileged information, constructing simulation terrains, and refining reward functions during phase 1 training, we are able to train the robots with skills that are robust in non-ideal physical environments. We demonstrate successful sim-to-real transfer and achieve high success rates across all three tasks in physical experiments.

We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities. Experiment videos can be found at \url{https://yxyang.github.io/jumping\_cod/}.