Collaborative Object Manipulation Research Articles

Trimmed helicoids: an architectured soft structure yielding soft robots with high precision, large workspace, and compliant interactions

The development and use of architectured structures is changing the means by which we design and fabricate soft robots. These materials utilize their topology and geometry to control physical and mechanical structural properties. We propose an architectured structure based on trimmed helicoids that allows for independent regulation of the bending and axial stiffness which facilitates tuneability of the resulting soft robot properties. Leveraging FEA and computational analysis we select a geometry that provides an optimal trade-off between controllability, sensitivity to errors in control, and compliance. By combining these modular trimmed helicoid structures in conjunction with control methods, we demonstrate a meter-scale soft manipulator that shows control precision, large workspace, and compliant interactions with the environment. These properties enable the robot to perform complex tasks that leverage robot-human and robot-environment interactions such as human feeding and collaborative object manipulation.

Manipulation Guidance Field for Collaborative Object Manipulation in VR

Object manipulation is a fundamental interaction in virtual reality (VR). Efficient and accurate manipulation is important for many VR applications, especially collaborative VR applications. We introduce a collaborative method based on the manipulation guidance field (MGF) to improve manipulation accuracy and efficiency. MGF aims to guide users of different manipulation types to different manipulation viewpoints to efficiently and collaboratively manipulate objects. First, we introduce the concept of MGF and its construction method. Two strategies are offered to accelerate the MGF updating process. A collaborative manipulation method to manipulate objects using the guidance of MGF is then proposed. Finally, a user study (n = 36 participants) was conducted to evaluate the efficiency and accuracy of our MGF-based collaborative object manipulation method in three scenes: (1) Livingroom scene; (2) WaveHouse scene; (3) Pipe scene. Compared to a control method without MGF, the results show that our MGF-based method has significantly reduced task completion time, position error, rotation error, and task load.

Dual-Arm Box Grabbing With Impact-Aware MPC Utilizing Soft Deformable End-Effector Pads

Safely generating impacts is challenging due to subsequent discontinuous velocity jumps and high impact forces. For this reason, state-of-the-art multi-robot controllers performing collaborative object manipulation typically approach contacts with low relative velocities. We instead aim for high-speed bi-manual impact-aware swift grabbing tasks, generating intentionally maximum feasible impacts. Therefore, we propose that the robot end-effector tips be equipped with deformable soft pads. The hardware modification partially absorbs the shock and enables us to control the deformation state during the prolonged impact duration. Mapping the robot's structural hardware constraints from the high-dimensional configuration-space to the low-dimensional contact-space enables us to model the bounded deformation dynamics explicitly. Exploiting constrained model-predictive control, we maximize the impact velocity within the feasible limits for dual-arm setups with deformable end-effector tips. Our control paradigm is assessed with real-robot experiments on two redundant Panda manipulators, demonstrating high pre-impact velocities for boxes with varying weights and size. The approach also applies to grabbing soft objects with rigid end-effectors.

The Grasp Strategy of a Robot Passer Influences Performance and Quality of the Robot-Human Object Handover.

Task-aware robotic grasping is critical if robots are to successfully cooperate with humans. The choice of a grasp is multi-faceted; however, the task to perform primes this choice in terms of hand shaping and placement on the object. This grasping strategy is particularly important for a robot companion, as it can potentially hinder the success of the collaboration with humans. In this work, we investigate how different grasping strategies of a robot passer influence the performance and the perceptions of the interaction of a human receiver. Our findings suggest that a grasping strategy that accounts for the subsequent task of the receiver improves substantially the performance of the human receiver in executing the subsequent task. The time to complete the task is reduced by eliminating the need of a post-handover re-adjustment of the object. Furthermore, the human perceptions of the interaction improve when a task-oriented grasping strategy is adopted. The influence of the robotic grasp strategy increases as the constraints induced by the object's affordances become more restrictive. The results of this work can benefit the wider robotics community, with application ranging from industrial to household human-robot interaction for cooperative and collaborative object manipulation.

A Model for Human–Human Collaborative Object Manipulation and Its Application to Human–Robot Interaction

During collaborative object manipulation, the interaction forces provide a communication channel through which humans coordinate their actions. In order for the robots to engage in physical collaboration with humans, it is necessary to understand this coordination process. Unfortunately, there is no intrinsic way to define the interaction forces. In this study, we propose a model that allows us to compute the interaction force during a dyadic cooperative object manipulation task. The model is derived directly from the existing theories on human arm movements. The results of a user study with 22 human subjects prove the validity of the proposed model. The model is then embedded in a control strategy that enables the robot to engage in a cooperative task with a human. The performance evaluation of the controller through simulation shows that the control strategy is a promising candidate for a cooperative human–robot interaction.