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Providing artificial agents with the same computational models of biological systems is a way to understand how intelligent behaviours may emerge. We present an active inference body perception and action model working for the first time in…
Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine, and entertainment. The core challenges in developing a motion-capture system that operates in…
Bio-inspired vehicles are currently leading the way in the quest to produce a vehicle capable of flight and underwater navigation. However, a fully functional vehicle has not yet been realized. We present the first fully functional vehicle…
Intra-vehicular free-flying robots are anticipated to support various work in human spaceflight while working side-by-side with astronauts. Such example of robots includes NASA's Astrobee, DLR's CIMON, and JAXA's Int-Ball, which are…
Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert human pilots are able to fly a drone through a race track by mapping pixels from a single…
The mobile robotic testbed is an essential and critical support to verify the effectiveness of mobile robotics research. This paper introduces a novel multi-robot testbed, named Robopheus, which exploits the ideas of virtual-physical…
High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper…
Collaborative space robots are an emerging technology with high impact as robots facilitate servicing functions in collaboration with astronauts with higher precision during lengthy tasks, under tight operational schedules, with less risk…
The kinematic features of a centaur-type humanoid platform, combined with a powerful actuation, enable the experimentation of a variety of agile and dynamic motions. However, the higher number of degrees-of-freedom and the increased weight…
Robotic manipulation is essential for the widespread adoption of robots in industrial and home settings and has long been a focus within the robotics community. Advances in artificial intelligence have introduced promising learning-based…
This work introduces a robot navigation controller that combines event cameras and other sensors with reinforcement learning to enable real-time human-centered navigation and obstacle avoidance. Unlike conventional image-based controllers,…
Safety in industrial robotic environments is a hot research topic in the area of human-robot interaction (HRI). Up to now, a robotic arm on an assembly line interacts with other machines away from human workers. Nowadays, robotic arm…
Teleoperation (i.e., controlling a robot with human motion) proves promising in enabling a humanoid robot to move as dynamically as a human. But how to map human motion to a humanoid robot matters because a human and a humanoid robot rarely…
Aerial manipulation combines the versatility and speed of flying platforms with the functional capabilities of mobile manipulation, which presents significant challenges due to the need for precise localization and control. Traditionally,…
Most telemanipulation systems for aerial robots provide the operator with only 2D screen visual information. The lack of richer information about the robot's status and environment can limit human awareness and, in turn, task performance.…
The main motivation of this work is to propose a simulation approach for a specific task within the Unmanned Aerial Vehicle (UAV) field, i.e., the visual detection and tracking of arbitrary moving objects. In particular, it is described…
This paper presents an aerial platform capable of performing physically interactive tasks in unstructured environments with human-like dexterity under human supervision. This aerial platform consists of a humanoid torso attached to a…
This paper contributes towards the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory…
Advanced machine learning algorithms require platforms that are extremely robust and equipped with rich sensory feedback to handle extensive trial-and-error learning without relying on strong inductive biases. Traditional robotic designs,…
Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. To train such a…