Estimating Material Properties of Interacting Objects Using Sum-GP-UCB

Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately. We present a Bayesian optimization approach to identifying the material property parameters of objects based on a set of observations. Our focus is on estimating these properties based on observations of scenes with different sets of interacting objects. We propose an approach that exploits the structure of the reward function by modeling the reward for each observation separately and using only the parameters of the objects in that scene as inputs. The resulting lower-dimensional models generalize better over the parameter space, which in turn results in a faster optimization. To speed up the optimization process further, and reduce the number of simulation runs needed to find good parameter values, we also propose partial evaluations of the reward function, wherein the selected parameters are only evaluated on a subset of real world evaluations. The approach was successfully evaluated on a set of scenes with a wide range of object interactions, and we showed that our method can effectively perform incremental learning without resetting the rewards of the gathered observations

Multi-Robot Object Transport Motion Planning with a Deformable Sheet

Using a deformable sheet to handle objects is convenient and found in many practical applications. For object manipulation through a deformable sheet that is held by multiple mobile robots, it is a challenging task to model the object-sheet interactions. We present a computational model and algorithm to capture the object position on the deformable sheet with changing robotic team formations. A virtual variable cables model (VVCM) is proposed to simplify the modeling of the robot-sheet-object system. With the VVCM, we further present a motion planner for the robotic team to transport the object in a three-dimensional (3D) cluttered environment. Simulation and experimental results with different robot team sizes show the effectiveness and versatility of the proposed VVCM. We also compare and demonstrate the planning results to avoid the obstacle in 3D space with the other benchmark planner.Comment: 8 pages, 10 figures, accepted by RAL&CASE 2022 in June 24, 202

Hierarchical Decentralized LQR Control for Formation-Keeping of Cooperative Mobile Robots in Material Transport Tasks

This study provides a formation-keeping method based on consensus for mobile robots used in cooperative transport applications that prevents accidental damage to the objects being carried. The algorithm can be used to move both rigid and elastic materials, where the desired formation geometry is predefined. The cooperative mobile robots must maintain formation even when encountering unknown obstacles, which are detected using each robot's on-board sensors. Local actions would then be taken by the robot to avoid collision. However, the obstacles may not be detected by other robots in the formation due to line-of-sight or range limitations. Without sufficient communication or coordination between robots, local collision avoidance protocols may lead to the loss of formation geometry. This problem is most notable when the object being transported is deformable, which reduces the physical force interaction between robots when compared to rigid materials. Thus, a decentralized, hierarchical LQR control scheme is proposed that guarantees formation-keeping despite local collision avoidance actions, for both rigid and elastic objects. Representing the cooperative robot formation using multi-agent system framework, graph Laplacian potential and Lyapunov stability analysis are used to guarantee tracking performance and consensus. The effectiveness and scalability of the proposed method are illustrated by computer simulations of line (2 robots) and quadrilateral (4 robots) formations. Different communication topologies are evaluated and provide insights into the minimum bandwidth required to maintain formation consensus

RGB-D Tracking and Optimal Perception of Deformable Objects

Addressing the perception problem of texture-less objects that undergo large deformations and movements, this article presents a novel RGB-D learning-free deformable object tracker in combination with a camera position optimisation system for optimal deformable object perception. The approach is based on the discretisation of the object''s visible area through the generation of a supervoxel graph that allows weighting new supervoxel candidates between object states over time. Once a deformation state of the object is determined, supervoxels of its associated graph serve as input for the camera position optimisation problem. Satisfactory results have been obtained in real time with a variety of objects that present different deformation characteristics

Coverage of deformable contour shapes with minimal multi-camera system

Perception over time is a critical problem in those cases where deformable objects are manipulated. The goal of this study is to cover the contour of an object along a deformation process and according to a prescribed coverage objective, in terms of visibility and resolution. This task is carried out by a set of limited field-of-view cameras. We propose novel methods for guaranteeing feasibility of the coverage objectives, which include the computation of the maximum visibility and resolution of the contour. Then, we introduce the coverage objectives in an offline constrained optimization problem to compute a priori the minimum number of cameras that achieve the coverage requirements. Finally, we propose an online technique that provides optimized configurations faster than the offline one, even when the object’s reference deformation is unknown. We report experimental results in which our method achieves 100% of the coverage in simulation and in a real task

Hierarchical Decentralized LQR Control for Formation-Keeping of Cooperative Mobile Robots in Material Transport Tasks

This study provides a formation-keeping method based on consensus for mobile robots used in cooperative transport applications that prevents accidental damage to the objects being carried. The algorithm can be used to move both rigid and elastic materials, where the desired formation geometry is predefined. The cooperative mobile robots must maintain formation even when encountering unknown obstacles, which are detected using each robot's on-board sensors. Local actions would then be taken by the robot to avoid collision. However, the obstacles may not be detected by other robots in the formation due to line-of-sight or range limitations. Without sufficient communication or coordination between robots, local collision avoidance protocols may lead to the loss of formation geometry. This problem is most notable when the object being transported is deformable, which reduces the physical force interaction between robots when compared to rigid materials. Thus, a decentralized, hierarchical LQR control scheme is proposed that guarantees formation-keeping despite local collision avoidance actions, for both rigid and elastic objects. Representing the cooperative robot formation using multi-agent system framework, graph Laplacian potential and Lyapunov stability analysis are used to guarantee tracking performance and consensus. The effectiveness and scalability of the proposed method are illustrated by computer simulations of line (2 robots) and quadrilateral (4 robots) formations. Different communication topologies are evaluated and provide insights into the minimum bandwidth required to maintain formation consensus

Survey on multi-robot manipulation of deformable objects

Autonomous manipulation of deformable objects is a research topic of increasing interest due to the variety of current processes and applications that include this type of tasks. It is a complex problem that involves aspects such as modeling, control, perception, planning, grasping, estimation, etc. A single robot may be unable to perform the manipulation when the deformable object is too big, too heavy or difficult to grasp. Then, using multiple robots working together naturally arises as a solution to perform coordinately the manipulation task. In this paper, we contribute a survey of relevant state-of-the-art approaches concerning manipulation of deformable objects by multiple robots, which includes a specific classification with different criteria and a subsequent analysis of the leading methods, the main challenges and the future research directions