In this letter, we present a novel bi-modal bi-copter robot called Skater, which is adaptable to air and various ground surfaces. Skater consists of a bi-copter moving along its longitudinal direction with two passive wheels on both sides. Using a longitudinally arranged bi-copter as the unified actuation system for both aerial and ground modes, this robot not only keeps a concise and lightweight mechanism but also possesses exceptional terrain traversing capability and strong steering capacity. Moreover, leveraging the vectored thrust characteristic of bi-copters, the Skater can actively generate the centripetal force needed for steering, enabling it to achieve stable movement even on slippery surfaces. Furthermore, we model the comprehensive dynamics of the Skater, analyze its differential flatness, and introduce a controller using nonlinear model predictive control for trajectory tracking. The outstanding performance of the system is verified by extensive real-world experiments and benchmark comparisons.

This paper introduces DogSurf - a newapproach of using quadruped robots to help visually impaired people navigate in real world. The presented method allows the quadruped robot to detect slippery surfaces, and to use audio and haptic feedback to inform the user when to stop. A state-of-the-art GRU-based neural network architecture with mean accuracy of 99.925% was proposed for the task of multiclass surface classification for quadruped robots. A dataset was collected on a Unitree Go1 Edu robot. The dataset and code have been posted to the public domain.

The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a difficult problem, especially for slippery terrains. To address this issue we propose a new approach that learns a surface-aware dynamics model by conditioning it on a latent variable vector storing surface information about the current location. A latent mapper is trained to update these latent variables during inference from multiple modalities on every traversal of the corresponding locations and stores them in a map. By training everything end-to-end with the loss of the dynamics model, we enforce the latent mapper to learn an update rule for the latent map that is useful for the subsequent dynamics model. We implement and evaluate our approach on a real miniature electric car. The results show that the latent map is updated to allow more accurate predictions of the dynamics model compared to a model without this information. We further show that by using this model, the driving performance can be improved on varying and challenging surfaces.

Despite extensive studies on motion stabilization of bipeds, they still suffer from the lack of disturbance coping capability on slippery surfaces. In this paper, a novel controller for stabilizing a bipedal motion in its sagittal plane is developed with regard to the surface friction limitations. By taking into account the physical limitation of the surface in the stabilization trend, a more advanced level of reliability is achieved that provides higher functionalities such as push recovery on low-friction surfaces and prevents the stabilizer from overreacting. The discrete event-based strategy consists of modifying the step length and time period at the beginning of each footstep in order to reestablish stability necessary conditions while taking into account the surface friction limitation as a constraint to prevent slippage. Adjusting footsteps to prevent slippage in confronting external disturbances is perceived as a novel strategy for keeping stability, quite similar to human reaction. The developed methodology consists of rough closed-form solutions utilizing elementary math operations for obtaining the control inputs, allowing to reach a balance between convergence and computational cost, which is quite suitable for real-time operations even with modest computational hardware. Several numerical simulations, including push recovery and switching between different gates on low-friction surfaces, are performed to demonstrate the effectiveness of the proposed controller. In correlation with human-gait experience, the results also reveal some physical aspects favoring stability and the fact of switching between gaits to reduce the risk of falling in confronting different conditions.

In this article, we are going to learn how to create and explore the Frozen Lake environment using the Gym library, an open source project created by OpenAI used for reinforcement learning experiments. The Gym library defines a uniform interface for environments what makes the integration between algorithms and environment easier for developers. Among many ready-to-use environments, the default installation includes a text-mode version of the Frozen Lake game, used as example in our last post. The first step to create the game is to import the Gym library and create the environment. The next line calls the method gym.make() to create the Frozen Lake environment and then we call the method env.reset() to put it on its initial state.

It seems like robots could learn from roaches. Researchers from the University of Cologne in Germany have discovered a change in roaches' gait that could help teach robots to walk. Animal's gait was previously only analyzed in fast mammals. Researchers have now found that arthropods that run quickly, like roaches, change their gait at mid-speed. Experts said the change in gait in roaches (Nauphoeta cinerea) is similar to the way horses switch from trop to gallop.

If you have already read part 1 and part 2 of our machine learning series, you are already well on your way to becoming a machine learning expert. If not, you should read them now. You can't do this to a toddler, though: Well, we suppose you could… But you probably shouldn't. If a learning machine behaves in this way, somebody messed up real bad. They have a limited set of data, in this case, pertaining to balance and remaining upright.