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17 results for “Robotic manipulation”
The relevance of signal timing in human-robot collaborative manipulation
<p><em><strong>Dataset version 1.0.1. The data collected here are attached to the following journal article: F. Cini*, T. Banfi*, G. Ciuti, L. Craighero, M. Controzzi, The relevance of signal timing in human-robot collaborative manipulation. Science Robotics Vol. 6 Issue 58, 2021. DOI: 10.1126/scirobotics.abg1308</strong></em></p> <p>To achieve a seamless human-robot collaboration, it is crucial that robots express their intentions without perturbating or interrupting the task that a human partner is performing at that moment. Although it has not received much attention so far, this issue is important when robots assist humans in physical and manipulation tasks. The main question addressed here is whether there is a more appropriate time to inform a human partner that a robot is requesting to pass them an object. This question is posed in a reference scenario where human individuals are involved in a continuous pick-and-place task that cannot be interrupted. Our findings showed that providing a cue at the beginning of a reach-to-grasp movement could severely interfere with the ongoing human action,<br> increasing the number of errors made by humans, slowing down and degrading the smoothness of their arm movement, and deflecting their gaze. These disruptive interferences strongly decreased, until they disappeared, when the robot provided the cue to the human partners shortly after the participants picked up an object, identifying this as the best signaling timing. The results of this work showed how the signaling timing may have a decisive influence on the performances of the human-robot teamwork and contribute to understating the mechanisms underpinning the phenomenon of cognitive-motor interference in humans.</p>
Dataset for: An experimental comparison of anomaly detection methods for collaborative robot manipulators
<p>The dataset contains data recordings from a UR5e robot during normal and anomalous operation and is recorded to support the authors Master thesis project and the associated Paper: <em>"An Experimental Comparison of Anomaly Detection Methods for Collaborative Robot Manipulators" </em>(inProceeding).</p> <p>An in-depth description of the dataset can be found in the pdf uploaded with the dataset and an example of a data loader is also provided.</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Dataset]
<p>Dataset used for the paper submitted to RO-MAN 2022</p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p>
Robotic manipulation datasets for offline compositional reinforcement learning
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Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Video]
<p>Video of the paper submitted at RO-MAN 2022 </p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p> <p>Code for trainings and test available at:</p> <p>https://github.com/giorgionicola/SMAHRCO</p>
Compliant Aerial Manipulators: Developing the New Generation of Aerial Robotic Workers
<p><strong>This video demonstrates the results found in the research of a new topic in aerial manipulation. We attempt to successfully collide on the environment with the UAV without crashing. This can be useful to robustly establish contact with the environment in realistic outdoor scenarios, where precise knowledge on the position of the drone might not always available.</strong></p> <p> </p> <p><strong>In the video three different experiments are shown. In all of these experiments, the manipulator arm (in this case a rotating rod) is in front of the drones center of mass.</strong></p> <p><strong>The first experiment shows the collision of the drone with the environment when the manipulator is rigidly connected to the drone. This causes a severe impact, which destabilizes the drone. It is simply too much energy for the drone to handle. In the second experiment the manipulator arm is connected to the drone via a spring-damper system to reduce the severeness of the impact. This shows significant improvement, but the drone is unable to maintain contact and bounces. The key to success in this work was to add a mechanical one-direction stop on the manipulator. This stop allows the arm to be pressed in during impact, but prevents the arm from releasing the energy afterwards. The third experiment shows how this works and demonstrates a beautiful smooth impact to achieve contact.</strong></p>
Motion Planning Integration on ABB Robot Manipulator for Robot Welding
<p>Integration of Motion Planning based on Moveit Robot Operating System (ROS) on ABB Robot Manipulator for Robot Welding. Stochastic Trajectory Optimization for Motion Planning (STOMP) and Rapidly exploring Random Tree Star (RRT*) are used in this implementation. STOMP is used as primary path planner as it has less computation time whereas RRT* is used as failsafe if STOMP fails to compute a plan.</p>
Multi-contact loco-manipulation trajectories for the ANYmal robot with a 6-DoF Arm
<p>Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed based on a system's ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behaviors with hand-crafted state machines, densely engineered rewards, or pre-recorded expert demonstrations. Here, we propose a minimally-guided framework that automatically discovers whole-body trajectories jointly with contact schedules for solving general loco-manipulation tasks in pre-modeled environments. The key insight is that multi-modal problems of this nature can be modeled within the context of integrated Task and Motion Planning (TAMP), resulting in a tractable bilevel optimization formulation. An effective bilevel search strategy is achieved owing to the fusion of domain-specific rules with the well-established strengths of different planning techniques: trajectory optimization and informed graph search, coupled with sampling-based planning. We showcase emergent behaviors for a quadrupedal mobile manipulator exploiting both prehensile and non-prehensile interactions to perform real-world tasks such as opening/closing heavy dishwashers and traversing spring-loaded doors. These behaviors are also deployed on the real system using a two-layer whole-body tracking controller.</p>
Data from: Robotic manipulation of cardiomyocytes to identify gap junction modifiers for arrhythmogenic cardiomyopathy
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Multi-contact loco-manipulation trajectories for the ANYmal robot with a 6-DoF Arm
Open the record for dataset details and reuse information.
VIMA: General Robot Manipulation with Multimodal Prompts
<p>VIMA dataset for learning general robot manipulation with multimodal prompts.</p>
Bimanual Robot Arm Cloth Manipulation Dataset
<p>This dataset contains 945 trials of a bimanual robot arm setup, where one arm holds a piece of cloth (e.g., a towel) by one corner while the other arm, equipped with a wrist-mounted camera, captures images from approximately 180 different angles by making a half-circle motion around the cloth. Each trial includes images and the corresponding TCP (Tool Center Point) poses of the second arm. Additionally, the dataset provides annotated positions of the two corners adjacent to the held corner, which are critical for unfolding the cloth by grasping these points. This dataset is valuable for research in robotic manipulation, computer vision, and cloth handling tasks.</p>
A loop-type modular soft robot with integrated locomotion and manipulation capability
<p>Modular Soft Robot.</p>
Pilot Trial of the Robotic Uterine Manipulator
ClinicalTrials.gov study NCT03517228. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Vision-Guided Shared Control for Assistive Robotics Manipulators
ClinicalTrials.gov study NCT04323449. IPD Sharing: NO. Countries: 1. Publications: 0.
A 3UPS/S Spherical Parallel Manipulator Designed for Robot Assisted Hand Rehabilitation after Stroke
<p><span>Hand dysfunction is a common symptom in stroke patients. This paper presents a r<span>obot</span>ic device which assists rehabilitation process in order to reduce the need of physical therapy, i.e., a <span>3UPS/S parallel robot</span>ic device is employed for repetitive robot-assisted rehabilitation. Euler angle representation was used to solve the robot's inverse kinematics. The robot's joint space and rotational workspace are determined for two scenarios. In the first scenario, the workspace is obtained considering the actuator’s stroke limitations, while in the second scenario, the workspace is determined by adding a second condition, i.e. the range of motion of the spherical joints. Singularity analysis is performed using geometric algebra approach. The robot was manufactured using additive manufacturing technology. The solution of the inverse kinematic problem is employed to control the robot. The robot can perform full range of motion during wrist ulnar deviation and radial deviation motions, with the exception of limited wrist flexion and extension motions. The robot has singular configurations within its workspace. Although the spherical joints have roles in reducing the workspace, the primary causes are actuator selection, radiuses of base and moving platforms and the length of the central leg. These factors can be considered to improve the workspace. Singularity can be avoided by carefully selecting the rotation of the moving platform about Z-axis and avoiding same leg lengths.</span></p>
Full Data Set for the Paper: An Evaluation of Open Source Trajectory Planners for Robotic Manipulators with Focus on Human-Robot Collaboration
<p>This data sets contain the full evaluation data for the paper "An Evaluation of Open Source Trajectory Planners for Robotic Manipulators with Focus on Human-Robot Collaboration".</p>
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International Brain Laboratory public data
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OpenNeuro
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