Soft Object Manipulation Research Articles

LLM for Differentiable Soft Robotic Vision Objects Manipulation

Most existing neural soft object manipulation systems rely on differentiable simu- lation as the default physics engine. However, current explicit differentiable sim- ulation solvers adopt a heuristic searching approach to manipulate the soft body, which easily gets stuck when the initial stage of the end effectors is sub-optimal on single-stage tasks or when performing multi-stage tasks that require complex hi- erarchical actions, which often leads to local-minima. Furthermore, existing deep soft object manipulation systems never consider the soft multi-body dynamics and model the dynamic system with naive explicit visual cues (e.g., image frame). To address this challenge, we propose an implicit, i.e., with energy-based models, soft objects manipulation differentiable simulator (iDSoftor) that guides the dif- ferentiable physics solver to deform the various soft object. The key idea of our work is to integrate energy-based models into the soft-object differentiable simu- lation to address the local minima in common explicit (heuristic transport-based) methods to stabilize the gradient of training differentiable simulators. On simple tasks, such as one-stage tasks, our proposed method can feasibly find a suitable initial stage based on theoretical arguments, refer to. On complex multi-stage tasks with multimodal, like implicit visual cues states, our proposed methods can iteratively adjust the manipulation trajec- tory from the perspective of energy priorities. To evaluate the feasibility of our proposed method, we formulate various potential novel soft-object manipulation tasks and demonstrate a preliminary story.

Combining Suction and Friction to Stabilize a Soft Gripper to Shear and Normal Forces, for Manipulation of Soft Objects in Wet Environments

Soft robotic gripping in wet environments is generally limited by the presence of a liquid that lubricates the interface between the gripper and an object being manipulated. The use of soft grippers is particularly beneficial for manipulating soft, delicate objects, yet is further limited by low grip strengths. We propose the use of suction, a form of adhesion that functions well in wet environments, to enhance soft robotic grippers. We stabilized the suction against shear disturbances using soft actuated fingers decorated with fluid-channeling patterns to enhance friction, counteracting the interfacial lubrication experienced in wet environments. We therefore combined the uses of attachment via suction and shear stability via friction to create an adhesive soft gripper. We evaluated the contributions to attachment of each component to help stabilize it against dislodgement forces that act in parallel and normal to an object that it aimed to manipulate. By identifying these contributions, we envision that such an adhesive gripper can be used to benefit soft robotic manipulation in a variety of wet environments, from surgical to subsea applications.

LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation

Soft object manipulation has recently gained popularity within the robotics community due to its potential applications in many economically important areas. Although great progress has been recently achieved in these types of tasks, most state-of-the-art methods are case-specific; They can only be used to perform a single deformation task (e.g. bending), as their shape representation algorithms typically rely on "hard-coded" features. In this paper, we present LaSeSOM, a new feedback latent representation framework for semantic soft object manipulation. Our new method introduces internal latent representation layers between low-level geometric feature extraction and high-level semantic shape analysis; This allows the identification of each compressed semantic function and the formation of a valid shape classifier from different feature extraction levels. The proposed latent framework makes soft object representation more generic (independent from the object's geometry and its mechanical properties) and scalable (it can work with 1D/2D/3D tasks). Its high-level semantic layer enables to perform (quasi) shape planning tasks with soft objects, a valuable and underexplored capability in many soft manipulation tasks. To validate this new methodology, we report a detailed experimental study with robotic manipulators.

Fourier-Based Shape Servoing: A New Feedback Method to Actively Deform Soft Objects into Desired 2-D Image Contours

This paper addresses the design of a vision-based method to automatically deform soft objects into desired two-dimensional shapes with robot manipulators. The method presents an innovative feedback representation of the object's shape (based on a truncated Fourier series) and effectively exploits it to guide the soft object manipulation task. A new model calibration scheme that iteratively approximates a local deformation model from vision and motion sensory feedback is derived; this estimation method allows us to manipulate objects with unknown deformation properties. Pseudocode algorithms are presented to facilitate the implementation of the controller. Numerical simulations and experiments are reported to validate this new approach.

Automatic 3-D Manipulation of Soft Objects by Robotic Arms With an Adaptive Deformation Model

In this paper, we present a new feedback method to automatically servo-control the 3-D shape of soft objects with robotic manipulators. The soft object manipulation problem has recently received a great deal of attention from robotics researchers because of its potential applications in, e.g., food industry, home robots, medical robotics, and manufacturing. A major complication to automatically control the shape of an object is the estimation of its deformation properties, which determines how the manipulator's motion actively transforms into deformations. Note that these properties are rarely known beforehand, and its offline parametric identification is difficult and/or impractical to conduct in many applications. To cope with this issue, we developed a new algorithm that computes in real time the unknown deformation parameters of a soft object; this algorithm provides a valuable adaptive behavior to the deformation controller, something we cannot achieve with traditional fixed-model approaches. In contrast with most controllers in the literature, our new method can explicitly servo-control 3-D deformations (and not just 2-D image projections) in an entirely model-free way. To validate the proposed adaptive controller, we present a detailed experimental study with robotic manipulators.

粘弾性体の位置と変形の同時制御―一次元粘弾性体の位置決め可能性―

A feature of soft object manipulation in free space is coupling of motion and deformation of the soft object. Herein, we formulate simultaneous control of position and deformation of a viscoelastic object. We show that the stability of the system depends not only on control laws and parameters but also on physical parameters of the object, and that positive feedback gain within a certain range stabilizes PID controlled systems; outside this range, the object cannot be stably controlled since the object is movable. We also validate the stability of the system for control inputs and mapped manipulated and positioned points. The positioned points converge to their desired positions using the mapping based on closest relationship between the positioned and manipulated points. We show that both the viscosity of a soft object and the damping component of the control law are essential for stability when a force is used as a control input.