21 accepted papers
End-to-end robot learning, particularly for long-horizon tasks, often results in unpredictable outcomes and poor generalization. To address these challenges, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Therblig-Based Backbone F
Physical manipulation of garments is often crucial when performing fabric-related tasks, such as hanging garments. However, due to the deformable nature of fabrics, these operations remain a significant challenge for robots in household, healthcare, and industrial environments. In this paper, we pro
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 methods to address this challenge, with imitation learning eme
Human hands are able to grasp a wide range of object sizes, shapes, and weights, achieved via reshaping and altering their apparent grasping stiffness between compliant power and rigid precision. Achieving similar versatility in robotic hands remains a challenge, which has often been addressed by ad
Due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning a
Robots for minimally invasive surgery are becoming more and more complex, due to miniaturization and flexibility requirements. The vast majority of surgical robots are tendon-driven and this, along with the complex design, causes high nonlinearities in the system which are difficult to model analyti
Virtual reality (VR) rehabilitation systems have been proposed to enable prosthetic hand users to perform training before receiving their prosthesis. Improving pre-prosthetic training to be more representative and better prepare the patient for prosthesis use is a crucial step forwards in rehabilita
In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control
Robot modelling is an essential part to properly understand how a robotic system moves and how to control it. The kinematic model of a robot is usually obtained by using Denavit-Hartenberg convention, which relies on a set of parameters to describe the end-effector pose in a Cartesian space. These p…
Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state a…
In robotically assisted surgical procedures the surgical tool is usually inserted in the patient’s body through a small incision, which acts as a constraint for the motion of the robot, known as remote center of Motion (RCM). The location of the insertion point on the patient’s body has huge effects…
Stiffness modulation in walking is critical to maintain static/dynamic stability as well as to minimize energy consumption and impact damage. However, optimal, or even functional, stiffness parameterization remains unresolved in legged robotics. We introduce an architecture for stiffness control uti
Most state-of-the-art bipedal robots are designed to be anthropomorphic and therefore possess legs with knees. Whilst this facilitates more human-like locomotion, there are implementation issues that make walking with straight or near-straight legs difficult. Most bipedal robots have to move with a…
In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety and accuracy of the surgical task. Mechanical designs and actuation schemes have inevitable limitations such as backlash and joint limits. Moreover, surgical robots need to operate in narrow pat…
This paper addresses the problem of human body detection-particularly a human body lying on the ground (a.k.a. casualty)-using point cloud data. This ability to detect a casualty is one of the most important features of mobile rescue robots, in order for them to be able to operate autonomously. We p…
In reinforcement learning, it is common to let an agent interact for a fixed amount of time with its environment before resetting it and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed period, or (ii) a…
Encoders have been an inseparable part of robots since the very beginning of modern robotics in the 1950s. As a result, the foundations of robot control are built on the concepts of kinematics and dynamics of articulated rigid bodies, which rely on explicitly measuring the robot configuration in ter…
This paper challenges the well-established assumption in robotics that in order to control a robot it is necessary to know its kinematic information, that is, the arrangement of links and joints, the link dimensions and the joint positions. We propose a kinematic-free robot control concept that does…
In this paper, a robot learning approach is proposed which integrates Visuospatial Skill Learning, Imitation Learning, and conventional planning methods. In our approach, the sensorimotor skills (i.e., actions) are learned through a learning from demonstration strategy. The sequence of performed act…
We propose a new algorithm capable of online regeneration of gait patterns. The algorithm uses a nonlinear optimization technique to find step parameters that will bring the robot from the present state to a desired state. It modifies online not only the footstep positions, but also the step timing…
Autonomous manipulation of objects requires reliable information on robot-object contact state. Underwater environments can adversely affect sensing modalities such as vision, making them unreliable. In this paper we investigate underwater robot-object contact perception between an autonomous underw…