Multi-scopic neuro-cognitive adaptation for legged locomotion robots

Abstract

Dynamic locomotion is realized through a simultaneous integration of adaptability and optimality. This article proposes a neuro-cognitive model for a multi-legged locomotion robot that can seamlessly integrate multi-modal sensing, ecological perception, and cognition through the coordination of interoceptive and exteroceptive sensory information. Importantly, cognitive models can be discussed as micro-, meso-, and macro-scopic; these concepts correspond to sensing, perception, and cognition; and short-, medium-, and long-term adaptation (in terms of ecological psychology). The proposed neuro-cognitive model integrates these intelligent functions from a multi-scopic point of view. Macroscopic-level presents an attention mechanism with short-term adaptive locomotion control conducted by a lower-level sensorimotor coordination-based model. Macrosopic-level serves environmental cognitive map featuring higher-level behavior planning. Mesoscopic level shows integration between the microscopic and macroscopic approaches, enabling the model to reconstruct a map and conduct localization using bottom-up facial environmental information and top-down map information, generating intention towards the ultimate goal at the macroscopic level. The experiments demonstrated that adaptability and optimality of multi-legged locomotion could be achieved using the proposed multi-scale neuro-cognitive model, from short to long-term adaptation, with efficient computational usage. Future research directions can be implemented not only in robotics contexts but also in the context of interdisciplinary studies incorporating cognitive science and ecological psychology.

Multiscopic CPSS for Independent Block-Design Test Based on Hand–Object Interaction Recognition With Visual Attention

“…As in our previous work on the chopsticks manipulation test (CMT), we conducted a multiscopic approach to examine the importance of combining finger joint angle estimation and visual attention measurement in hand rehabilitation [34]. This study further supported the use of multiscopic methods to address dynamic locomotion in legged robots [35], cognitive memory systems for continuous gesture learning [36], and the application of CPSS for activity daily living (ADL) [37]. Building upon these insights, we propose a multiscopic approach to develop a CPSS for HOI recognition based on visual attention specifically for the BDT.…”

Distributed Batch Learning of Growing Neural Gas for Quick and Efficient Clustering

“…SOINN has high tolerance for noise and clustering capability. In particular, Load-Balancing SOINN (LB-SOINN) significantly improves learning stability and clustering performance [25] by using a load that means the learning time of each node. However, all these methods require input signals one by one, and therefore will require more time to process and grow if used in large-scale datasets.…”

“…A topological map is a simplified diagram that retains only important information and removes unnecessary details, which is one of the main purposes of GNG. This topological map is important for the robot to make path decisions [25,32]. We plot the DBL-GNG topological map for each dataset in Figure 8, including initialization, when reaching the maximum node, after an additional 10 epochs of training, and after the edge cutting method.…”

Hand–object interaction recognition based on visual attention using multiscopic cyber-physical-social system

“…With a case study on the Chopsticks Manipulation Test, we examined the significance of combining finger joint angle estimation and a visual attention measurement in hand rehabilitation [24]. Our previous work used a multiscopic method to address dynamic locomotion in a legged robot [25] and simulation for human-robot interactions [26]. We propose a multiscopic approach for developing a CPSS for HOI recognition based on visual attention based on this experience.…”