Abstract: Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive … [Read more...] about AINav
Understanding and respecting personal space preferences is essential for socially assistive robots designed for older adult users. This work introduces and evaluates a novel personalized context-aware method for modeling users’ proxemics preferences during human-robot interactions. Using an interactive augmented reality interface, we collected a set of user-preferred distances … [Read more...] about An Interactive Augmented Reality Interface for Personalized Proxemics Modeling: Comfort and Human–Robot Interactions
Humans can autonomously learn the relationship between sensation and motion in their own bodies, estimate and control their own body states, and move while continuously adapting to the current environment. On the other hand, current robots control their bodies by learning the network structure described by humans from their experiences, making certain assumptions on the … [Read more...] about GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning
When a robot executes a task, it is necessary to model the relationship among its body, target objects, tools, and environment, and to control its body to realize the target state. However, it is difficult to model them using classical methods if the relationship is complex. In addition, when the relationship changes with time, it is necessary to deal with the temporal changes … [Read more...] about Deep Predictive Model Learning With Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
This article addresses the problem of online adaptation of control parameters, dedicated to a path tracking problem in off-road conditions. Two approaches are offered to modify the tuning gain of a previously developed adaptive and predictive control law. The first approach is a deterministic method based on dynamic equations of the system, allowing the adaptation of the … [Read more...] about Online Tuning of Control Parameters for Off-Road Mobile Robots
