Driven by the need for rapid and reliable heavy payload transport in logistics and manufacturing, researchers are increasingly exploring early applications of humanoid robotics in these domains. Although bipedal locomotion excels on challenging terrain, wheeled modes of transportation remain significantly more energy-efficient on flat surfaces. In this work, we develop a control system that enables a humanoid robot to achieve fast transportation - by riding a two-wheeled hoverboard - and robust heavy payload handling through whole-body grasping, where the robot uses its chest and arms to stabilize bulky objects. Our approach models payload-induced disturbances using a Linear Inverted Pendulum Mode extended with external forces and leverages tactile feedback from an integrated robotic skin to estimate the payload’s weight and center of mass. Feeding these estimates into the hoverboard controller reduces drift and enhances stability. Experimental evaluations on a full-sized real humanoid robot show that our system can withstand strong disturbances and autonomously navigate to deliver payloads of up to 20 kg.

THE sense of touch is an indispensable requirement for humans to effectively interact with the physical world around them and perform dexterous tasks. Similarly, this should be no different for robots. Imagine, for example, a robot that can open a bottle of medicine and dispense pills to an elderly person. Although this might seem a straightforward task for a human, it remains a significant challenge for a robot. Critically, the completion of the task depends on tactile sensing: the robot needs to receive and interpret the feedback from interacting with the bottle, determine the appropriate force based on the size and hardness of the pills, and adjust its pose to safely dispense them. Each step involves contact-rich interactions that can only be effectively deciphered through tactile sensing. Typically, tactile sensing works in conjunction with other modalities, such as vision, enabling the robot to adjust its actions dynamically and complete the task. In response to this vision of robots interacting with the physical world through touch, tactile robotics has now emerged as a key research area. Tactile robots can be defined as intelligent systems equipped with tactile sensors that can extract and process tactile data to guide their operations and interactions. The development of tactile robots presents scientific challenges, ranging from the design and fabrication of tactile sensors to methodologies for processing tactile data, integrating tactile feedback into task execution, and combining it with other sensory modalities to improve robot perception. As a result, tactile robotics demands collaborative efforts across several disciplines, involving material and data scientists …

This work presents a novel method to keep stable contact and balance while stepping over partial footholds for biped humanoid robots with flat feet. We exploit plantar tactile feedback to detect the geometry of the terrain and reconstruct online the new supporting polygon after landing every step. Plantar tactile feedback detects early contacts to stop the swing foot motion. Then we compute the convex hull of the cluster of contact points detected by distributed normal force sensors over the foot soles. The centroid of the supporting polygon is then used for retargeting the reference ZMP and DCM positions. Finally, the supporting polygon is used to define constraints for ZMP balance feedback control. These methods were implemented in two biped humanoid robots running different walking controllers.

In this work, we propose a new estimation method for second-order kinematics for floating-base robots, based on highly redundant distributed inertial feedback. The linear acceleration of each robot link is measured at multiple points using a multimodal, self-configuring and self-calibrating artificial skin. The proposed algorithm is two-fold: i) the skin acceleration data is fused at the link level for state dimensionality reduction; ii) the estimated values are then fused limb-wise with data from the joint encoders and the main inertial measurement unit (IMU), using a Sigma-point Kalman filter. In this manner, it is possible to estimate the joint velocities and accelerations while avoiding the lag and noise amplification phenomena associated with conventional numerical derivation approaches. Experiments performed on the right arm and torso of a REEM-C humanoid robot, demonstrate the consistency of the proposed estimation method.

Humanoid robots are expected to interact in human environments, where physical interactions are unavoidable. Therefore, whole-body control methods that include multi-contact interactions are required. The new emerging technologies in touch sensing are fundamental to acquire online and rich information about these physical interactions with the environment. These technologies lead to the design of novel control systems that can profit from the tactile sensor information in an efficient form, thus producing reactive and compliant robots capable of interacting with their environment. In this paper, we present a novel control framework to integrate the multi-modal tactile information of a robot skin with different control strategies, producing dynamic behaviours suitable for Human-Robot Interactions (HRI). The control framework was experimentally evaluated on a full-size humanoid robot covered with more than 1260 skin cells distributed in the whole robot body. The results show that multi-modal tactile information can be fused hierarchically with multiple control strategies, producing active compliance in a position-controlled stiff humanoid robot.

This article presents a holistic approach to the engineering of an artificial robot skin for robots. An example of a multimodal skin cell is given, one that supports multiple human-like sensing modalities, and support for skin cell network is also provided; this is essential to form large-area skin patches in order to cover the surfaces of robots. The essential elements of efficiently handling a large amount of tactile data are explained. A general control framework, which supports robots commanded in position, velocity, and torque, is provided and validated. Several applications of this robot skin will be presented, demonstrating the effectiveness and efficiency of our artificial robot skin to support a wide number of robotic platforms as well as its ease of use across different domains.

Skin technology enabled a powerful way to sense the environment in robotic systems. It allows simplifying the formulation of safety tasks such as collision avoidance between the robot, the environment and surrounding objects. In this paper, a hierarchy policy based on tactile feedback is proposed to let a robot interact with its environment while performing a set of tasks. Such policy lets the safety tasks as collision avoidance and physical interaction, be reduced to simple potential field rules fed directly with tactile feedback which keeps computation demand low. In this context, the concept of “Intentional Contact” is introduced to escape from classic undesired equilibrium points produced by local minima in the potential fields. Allowed contact with the environment empowers a robot to modify its surroundings in order to fulfil the main task. Such contact is permitted as long as the generated force remains under a specific limit, otherwise, a reactive action is taken to reduce it. This new concept is validated in simulation and on a real robot.