DefGraspSim: Physics-Based Simulation of Grasp Outcomes for 3D Deformable Objects

Huang, Isabella; Narang, Yashraj; Eppner, Clemens; Sundaralingam, Balakumar; Macklin, Miles; Bajcsy, Růžena; Hermans, Tucker; Fox, Dieter

doi:10.1109/lra.2022.3158725

Cited by 23 publications

(32 citation statements)

References 49 publications

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“…Deformability makes the evaluation of grasps more complex and requires suitable simulation engines. Therefore, metrics for grasps of deformable objects came into research focus only recently (e.g., [22]) and still have a high barrier for use as an evaluation tool.…”

Section: F Simulation Of Transferred Movementsmentioning

confidence: 99%

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

Fabisch¹,

Laux²,

Brust³

2022

Preprint

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Manipulating objects with robotic hands is a complicated task. Not only the pose of the robot's end effector, but also the fingers of the hand need to be controlled and coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer human hand motions to robotic systems and use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic arm with a mounted robotic hand needs to grasp and manipulate deformable objects or small, fragile material.

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Section: F Simulation Of Transferred Movementsmentioning

confidence: 99%

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

Fabisch¹,

Laux²,

Brust³

2022

Preprint

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“…Most recent research on deformable object manipulation has mainly focused on manipulating cloth items [10]- [12] and ropes [13]- [15]. Grasping deformable objects remains a sparsely explored research area [16] with the majority of works focusing on formulating quality metrics to quantify the goodness of a grasp [3], [4], [17], [18] or proposing control strategies [6]- [9].…”

Section: A Grasping Deformable Objectsmentioning

confidence: 99%

“…Furthermore, similar to PyBullet, the Isaac simulator also provides the capability to integrate robot-related functions, making it easier to build robotic applications. Huang et al [16] also provides a grasping framework to automatically perform and evaluate grasp tests on an arbitrary target object. We use this framework in our work to generate training data and to test grasps.…”

Section: Soft-body Simulationmentioning

confidence: 99%

Deformation-Aware Data-Driven Grasp Synthesis

Le¹,

Lundell²,

Abu‐Dakka³

et al. 2021

Preprint

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Grasp synthesis for 3D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmful-humans are, for example, able to exploit deformations to generate new potential grasps. How to achieve that on a robot is though an open question. This paper proposes an approach that uses object stiffness information in addition to depth images for synthesizing high-quality grasps. We achieve this by incorporating object stiffness as an additional input to a state-of-the-art deep grasp planning network. We also curate a new synthetic dataset of grasps on objects of varying stiffness using the Isaac Gym simulator for training the network. We experimentally validate and compare our proposed approach against the case where we do not incorporate object stiffness on a total of 2800 grasps in simulation and 420 grasps on a real Franka Emika Panda. The experimental results show significant improvement in grasp success rate using the proposed approach on a wide range of objects with varying shapes, sizes, and stiffness. Furthermore, we demonstrate that the approach can generate different grasping strategies for different stiffness values, such as pinching for soft objects and caging for hard objects. Together, the results clearly show the value of incorporating stiffness information when grasping objects of varying stiffness.

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“…For problems in grasping soft objects, modeling and simulation of deformable objects have recently been considerable interest in many robotic applications [2], [3], to manipulating soft tissue [4], [5], cloth [6], [7], and even fluids [8], [9]. Among soft structures, deformable linear objects (DLOs), including rope-like objects, strings, cables, beams, etc., are studied (cable routing [10], wire insertion [11], flexible rope [12], and knotting of surgical thread [13]).…”

Section: Introductionmentioning

confidence: 99%

Differentiable Robotic Manipulation of Deformable Rope-like Objects Using Compliant Position-based Dynamics

Liu¹,

Entong²,

Lu³

et al. 2022

Preprint

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Robot manipulation of rope-like objects is an interesting problem that has some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials. While more data-driven approaches have become more popular for finding controllers that learn to do a single task, there is still a strong motivation for a modelbased method that could be used to solve a large variety of optimization problems. Towards this end, we introduced compliant, position-based dynamics (XPBD) to model rope-like objects. Using geometric constraints, the model can represent the coupling of shear/stretch and bend/twist effects. Of crucial importance is that our formulation is differentiable, which can solve parameter estimation problems and improve the matching of rope physics to real-life scenarios (i.e., the realto-sim problem). For the generality of rope-like objects, two different solvers are proposed to handle the inextensible and extensible effects of varied material stiffness for the rope. We demonstrate our framework's robustness and accuracy on realto-sim experimental setups using the Baxter robot and the da Vinci research kit (DVRK) [1]. Our work leads to a new path for robotic manipulation of the deformable rope-like object taking advantage of the ready-to-use gradients.

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DefGraspSim: Physics-Based Simulation of Grasp Outcomes for 3D Deformable Objects

Cited by 23 publications

References 49 publications

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

Deformation-Aware Data-Driven Grasp Synthesis

Differentiable Robotic Manipulation of Deformable Rope-like Objects Using Compliant Position-based Dynamics

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