Significance of the compliance of the joints on the dynamic slip resistance of a bioinspired hoof

Robust mechanisms for slip resistance are an open challenge in legged locomotion. Animals such as goats show impressive ability to resist slippage on cliffs. It is not fully known what attributes in their body determine this ability. Studying the slip resistance dynamics of the goat may offer insight toward the biologically inspired design of robotic hooves. This article tests how the embodiment of the hoof contributes to solving the problem of slip resistance. We ran numerical simulations and experiments using a passive robotic goat hoof for different compliance levels of its three joints. We established that compliant yaw and pitch and stiff roll can increase the energy required to slide the hoof by ≈ 20% compared to the baseline (stiff hoof). Compliant roll and pitch allow the robotic hoof to adapt to the irregularities of the terrain. This produces an antilock braking system-like behavior of the robotic hoof for slip resistance. Therefore, the pastern and coffin joints have a substantial effect on the slip resistance of the robotic hoof, while the fetlock joint has the lowest contribution. These shed insights into how robotic hooves can be used to autonomously improve slip resistance

Human Behavioral Metrics of a Predictive Model Emerging During Robot Assisted Following Without Visual Feedback

Robot assisted guiding is gaining increased interest due to many applications involving moving in noisy and low visibility environments. In such cases, haptic feedback is the most effective medium to communicate. In this paper, we focus on perturbation based haptic feedback due to applications like guide dogs for visually impaired people and potential robotic counterparts providing haptic feedback via reins to assist indoor firefighting in thick smoke. Since proprioceptive sensors like spindles and tendons are part of the muscles involved in the perturbation, haptic perception becomes a coupled phenomenon with spontaneous reflex muscle activity. The nature of this interplay and how the model based sensory-motor integration evolves during haptic based guiding is not well understood yet. In this study, we asked human followers to hold the handle of a hard rein attached to a 1-DoF robotic arm that gave perturbations to the hand to correct an angle error of the follower. We found that human followers start with a 2nd order reactive autoregressive following model and changes it to a predictive model with training. The post-perturbation Electromyography (EMG) activity exhibited a reduction in co-contraction of muscles with training. This was accompanied by a reduction in the leftward/rightward asymmetry of a set of followers behavioural metrics. These results show that the model based prediction accounts for the internal coupling between proprioception and muscle activity during perturbation responses. Furthermore, the results provide a firm foundation and measurement metrics to design and evaluate robot assisted haptic guiding of humans in low visibility environments

Multi-segmented Adaptive Feet for Versatile Legged Locomotion in Natural Terrain

Most legged robots are built with leg structures from serially mounted links and actuators and are controlled through complex controllers and sensor feedback. In comparison, animals developed multi-segment legs, mechanical coupling between joints, and multi-segmented feet. They run agile over all terrains, arguably with simpler locomotion control. Here we focus on developing foot mechanisms that resist slipping and sinking also in natural terrain. We present first results of multi-segment feet mounted to a bird-inspired robot leg with multi-joint mechanical tendon coupling. Our one- and two-segment, mechanically adaptive feet show increased viable horizontal forces on multiple soft and hard substrates before starting to slip. We also observe that segmented feet reduce sinking on soft substrates compared to ball-feet and cylinder-feet. We report how multi-segmented feet provide a large range of viable centre of pressure points well suited for bipedal robots, but also for quadruped robots on slopes and natural terrain. Our results also offer a functional understanding of segmented feet in animals like ratite birds

Investigating foot morphology in rock climbing mammals: inspiration for biomimetic climbing shoes

The sporting goods sector can serve as a proving ground for new technologies. We propose that climbing shoes are an excellent case study for showcasing a systematic approach to bio-inspired design. Foot adaptations to climbing have been described before in some animals and have even been incorporated into bio-inspired products. However, there has not yet been a systematic description of climbing adaptations in mammals, and especially in rock climbing species. We present a description of foot morphology in mammals and compare rock climbing species to those with other locomotion types. Our results show that rock climbing species in our sample had fewer digits and larger anterior pads than arboreal species. Rock climbing species often had hooves or, if they had foot pads, these were relatively smooth. These examples look a bit like current climbing shoe designs, perhaps suggesting convergent evolution. However, there was also variation, with rock climbing species having pads varying in shape, placement and texture. Much of this variation is likely to be dependent on the relatedness of species, with those that are more related having more similar feet. We suggest that incorporation of novel textures and compliant pads might be an interesting focus for future climbing shoe designs

A method to guide local physical adaptations in a robot based on phase portraits

In this paper, we propose a method that shows how phase portraits rendered by a controller can inform the development of a physical adaptation at a single degree of freedom (DoF) for a given control task. This approach has the advantage of having physical adaptations sharing the responsibility of control to accomplish a task. We use an inverted pendulum which is reminiscent of the trunk of a biped walker to conduct numerical simulations and hardware experiments to show how our method can innovate a physical adaptation at the pivot joint to reduce the control effort. Our method discovered that a torsional spring at the pivot joint would lead to a lower input effort by the regulator type feedback controller. The method can tune the spring to minimize the total cost of control up to about 32.81%. This physical adaptation framework allows multiple degrees of freedom robotic system to suggest local physical adaptations to accomplish a given control objective

Learning discrete word embeddings to achieve better interpretability and processing efficiency

L’omniprésente utilisation des plongements de mot dans le traitement des langues naturellesest la preuve de leur utilité et de leur capacité d’adaptation a une multitude de tâches. Ce-pendant, leur nature continue est une importante limite en terme de calculs, de stockage enmémoire et d’interprétation. Dans ce travail de recherche, nous proposons une méthode pourapprendre directement des plongements de mot discrets. Notre modèle est une adaptationd’une nouvelle méthode de recherche pour base de données avec des techniques dernier crien traitement des langues naturelles comme les Transformers et les LSTM. En plus d’obtenirdes plongements nécessitant une fraction des ressources informatiques nécéssaire à leur sto-ckage et leur traitement, nos expérimentations suggèrent fortement que nos représentationsapprennent des unités de bases pour le sens dans l’espace latent qui sont analogues à desmorphèmes. Nous appelons ces unités dessememes, qui, de l’anglaissemantic morphemes,veut dire morphèmes sémantiques. Nous montrons que notre modèle a un grand potentielde généralisation et qu’il produit des représentations latentes montrant de fortes relationssémantiques et conceptuelles entre les mots apparentés.The ubiquitous use of word embeddings in Natural Language Processing is proof of theirusefulness and adaptivity to a multitude of tasks. However, their continuous nature is pro-hibitive in terms of computation, storage and interpretation. In this work, we propose amethod of learning discrete word embeddings directly. The model is an adaptation of anovel database searching method using state of the art natural language processing tech-niques like Transformers and LSTM. On top of obtaining embeddings requiring a fractionof the resources to store and process, our experiments strongly suggest that our representa-tions learn basic units of meaning in latent space akin to lexical morphemes. We call theseunitssememes, i.e., semantic morphemes. We demonstrate that our model has a greatgeneralization potential and outputs representation showing strong semantic and conceptualrelations between related words