Learning from Demonstration (LfD) based on Movement Primitives (MPs) constitutes a robust paradigm for transferring human skills to robots. However, many existing approaches often rely on homoscedastic assumptions or parametric constraints, limiting the robot’s ability to modulate stiffness according to specific task variations. To address this, we present Gaussian Movement Primitives (GMP), a non-parametric framework that explicitly learns a time-varying heteroscedastic prior from demonstrations. By interpreting the inverse covariance as a dynamic stiffness matrix, our method introduces a precision-weighted fusion mechanism. This formulation ensures geometric fidelity at via-points (handling them as infinite-precision constraints) and enables the safe composition of conflicting skills by automatically prioritizing high-precision behaviors. The framework is further enhanced by a temporal alignment preprocessing stage and a local obstacle avoidance module. To validate its effectiveness, the proposed method was evaluated in simulation and on real robotic manipulators, demonstrating significant improvements in reproduction accuracy and adaptability when compared against various LfD algorithms based on Movement Primitives.

Robotic manipulation can involves multiple manipulators to complete a task. In those cases, the complexity of performing the task in a coordinated manner increases, requiring coordinated planning while avoiding collisions between robots and environmental elements. For these challenges, we propose a robotic arm control algorithm based on Learning from Demonstration to independently learn the tasks of each arm, followed by a graph-based communication method using Gaussian Belief Propagation. Our method enables the resolution of decoupled dual-arm tasks learned independently with-out requiring coordinated planning. The algorithm generates smooth, collision-free solutions between arms and environmental obstacles while ensuring efficient movements without the need for constant replanning. Its efficiency has been validated through experiments and comparisons against another multi-robot control method in simulation using PyBullet with two opposing IIWA robots, as well as a mobile robot with two UR3 arms, which has also been used for real-world testing. Code provided in https://adrianprados.github.io/GaussianBeliefPropagationDualArm/

In the field of robotics, the demand for adaptable skills capable of effectively handling diverse situations has surpassed the reliance on repetitive tasks. To enhance the generalization of motion policies, task-parameterized learning has emerged as a valuable approach, as it encodes pertinent contextual information in task parameters, facilitating the flexible execution of tasks. This process requires the collection of multiple demonstrations in various situations. Generate a set of diverse situations covering all possible cases is a complex task. Due to this, the search for training with fewer demonstrations becomes highly desirable. In this article, we present a novel algorithm focused on generating synthetic information to facilitate the generalization of parameterized tasks. This algorithm makes use of Kinesthetic Fast Marching Learning, a Learning from Demonstration (LfD) algorithm that allows obtaining optimal movement paths based on velocity fields. The algorithm enables autonomous data generation, producing demonstrations that are equal to or more optimal than those generated by users themselves. Evaluation is done through a metric based on Wasserstein distance that takes into account the probabilistic data of the generated paths. The algorithm has been evaluated through tests in simulated environments, comparing its efficiency against two widely used algorithms (where it has shown greater efficiency in generalization and the generation of more optimal paths) and with real-world tests in a task-oriented environment (sweeping) carried out by the ADAM robot.

Robots require a detailed understanding of the 3D structure of the environment for autonomous navigation and path planning. A popular approach is to represent the environment using metric, dense 3D maps such as 3D occupancy grids. However, in large environments the computational power required for most state-of-the-art 3D dense mapping systems is compromising precision and real-time capability. In this work, we propose a novel mapping method that is able to build and maintain 3D dense representations for large indoor environments using standard CPUs. Topological global representations and 3D dense submaps are maintained as hybrid global map. Submaps are generated for every new visited place. A place (room) is identified as an isolated part of the environment connected to other parts through transit areas (doors). This semantic partitioning of the environment allows for a more efficient mapping and path-planning. We also propose a method for autonomous exploration that directly builds the hybrid representation in real time.We validate the real-time performance of our hybrid system on simulated and real environments regarding mapping and path-planning. The improvement in execution time and memory requirements upholds the contribution of the proposed work.

The ability to search for objects is a precondition for various robotic tasks. In this paper, we address the problem of finding objects in partially known indoor environments. Using the knowledge of the floor plan and the mapped objects, we consider object-object and object-room co-occurrences as prior information for identifying promising locations where an unmapped object can be present. We propose an efficient search strategy that determines the best pose of the robot based on the analysis of the candidate locations. We optimize the probability of finding the target object and the distance travelled through a cost function.To evaluate our method, several experiments in simulated and real-world environments were performed. The results show that the robot successfully finds the target object in the environment while covering only a small portion of the search space. The real-world experiments with the TurtleBot 2 mobile robot validate the proposed approach and demonstrate that the method performs well also in real environments.

Semantic navigation is the navigation paradigm in which environmental semantic concepts and their relationships are taken into account to plan the route of a mobile robot. This paradigm facilitates the interaction with humans and the understanding of human environments in terms of navigation goals and tasks. At the high level, a semantic navigation system requires two main components: a semantic representation of the environment, and a reasoning system. This paper is focused on develop a model of the environment using semantic concepts. This paper presents two solutions for the semantic navigation paradigm. Both systems implement an ontological model. Whilst the first one uses a relational database, the second one is based on KnowRob. Both systems have been integrated in a semantic navigator. We compare both systems at the qualitative and quantitative levels, and present an implementation on a mobile robot as a proof of concept.

This paper develops a wireless communication system that connects robots with many remote control devices used by many different users. The most important issue of this system is safety. To get high safety level a quick and efficient communication system is required. The emergency system and its communication system must work in parallel and independently of the main control of the robot. The robot must react to an emergency signal, but as a previous step, it must make sure that the security system is enabled and it so must also have some knowledge of how many remote control devices are related and if any of them has lost the wireless connection. Besides all the research and design stage to develop the communication system, the system has been implemented and tested. To build it, a microcontroller Arduino Fio and a radio frequency module Xbee has been used. Finally, the system has been tested in order to characterize the communication system, settling, connection time and the battery life.

This paper presents a framework for multimodal human-robot interaction. The proposed framework is being implemented in a personal robot called Maggie, developed at RoboticsLab of the University Carlos III of Madrid for social interaction research. The control architecture of this personal robot is a hybrid control architecture called AD (Automatic-Deliberative) that incorporates an Emotion Control System (ECS) Maggie’s main goal is to interact establish a peer-to-peer relationship with humans. To achieve this goal, a set of human-robot interaction skills are developed based on the proposed framework. The human-robot interaction skills imply tactile, visual, remote voice and sound modes. The multi-modal fusion and synchronization are also presented in this paper.

In this paper a system for generation of topological maps is presented. This system is considered as one of the deliberative skills of the mobile robots architecture named AD. AD is a two level architecture: deliberative and automatic. Those skills which require high computational time as consequence of high level reasoning are found in the deliberative level, while the automatic level skills interact with robot sensors and actuators. The topological map generated with this deliberative skill is the map belonging to the EDN navigation system, and is named Navigation Chart. In the Navigation Chart, the information obtained from the chart is stored as nodes and as edges. Nodes correspond to the sensorial events and edges correspond to the sensorimotor skills.