Learning Visible Connectivity Dynamics for Cloth Smoothing
Authors: Xingyu Lin
Yufei Wang
Zixuan Huang
David Held
Venue: CoRL2021 Poster (2021)
Keywords: Deformable object manipulation
dynamics model learning

Abstract: Robotic manipulation of cloth remains challenging due to the complex dynamics of cloth, lack of a low-dimensional state representation, and self-occlusions. In contrast to previous model-based approaches that learn a pixel-based dynamics model or a compressed latent vector dynamics, we propose to learn a particle-based dynamics model from a partial point cloud observation. To overcome the challenges of partial observability, we infer which visible points are connected on the underlying cloth mesh.  We then learn a dynamics model over this visible connectivity graph. Compared to previous learning-based approaches, our model poses strong inductive bias with its particle based representation for learning the underlying cloth physics; it can generalize to cloths with novel shapes; it is invariant to visual features; and the predictions can be more easily visualized. We show that our method greatly outperforms previous state-of-the-art model-based and model-free reinforcement learning methods in simulation. Furthermore, we demonstrate zero-shot sim-to-real transfer where we deploy the model trained in simulation on a Franka arm and show that the model can successfully smooth cloths of different materials, geometries and colors from crumpled configurations. Videos can be found in the supplement and on our anonymous project website: https://sites.google.com/view/vcd-cloth.

Learning Visible Connectivity Dynamics for Cloth Smoothing
Xingyu Lin' Robotics Institute Carnegie Mellon University x1in cmu. edu
Yufei Wang" Robotics Institute
Carnegie Mellon University
yufeiw2@andrew cmu edu
Zixuan Huang
David Held
Robotics Institute
Robotics Institute
Carnegie Mellon University zi ixuanhu@andres cmu. edu
Carnegie Mellon University dhe d@anddew.ww... cmu. edu
Abstract: Robotic manipulation of cloth remains challenging due to the com- plex dynamics of cloth, lack of low-dimensional occlusions. In contrast to previous model-based approaches that learn pixel- based dynamics model learn a particle-based dynamics model from partial point cloud observation. To overcome the challenges of partial observability, connected on the underlying cloth mesh. We then learn a dynamics model over this visible connectivity graph. Compared to previous learning-based approaches, our model poses strong inductive bias with its particle based representation for learn- ing the underlying cloth physics; it can generalize to cloths with novel shapes; it is invariant to visual features; and the predictions can be more easily visualized. We show that our method greatly outperforms previous state-of-the-art model-based and model-free reinforcement learning methods in simulation. Furthermore, we demonstrate zero-shot sim tim-to-real transfer where we deploy the model trained in simulation on a Franka arm and show that the model can successfully smooth cloths of different materials, geometries and colors from crumpled configurations. Videos can be found in the supplement and on our anonymous project website."
representation, and self-
compressed latent vector dynamics, we propose
infer which visible points
Keywords: Deformable Object Manipulation, Dynamics Modeling
Introduction
Robotic manipulation of cloth has wide applications across both industrial and domestic tasks such as laundry folding and bed making. However, cloth manipulation remains challenging for robotics due the complex cloth dynamics. Further, like most deformable objects, cloth cannot be easily described by low-dimensional state representations when placed in arbitrary configurations. Self- occlusions make state estimation especially difficult when the cloth is crumpled.
One approach cloth manipulation explored by previous work, which we also adopt, is learn cloth dynamics model and then use the model for planning to determine the robot actions. However, given that crumpled cloth has many self-occlusions and complex dynamics, it is unclear how to choose the appropriate state representation. One possible state representation is to use a mesh model of the entire cloth [1]. However, fitting full mesh model to arbitrary crumpled cloth configuration difficult. Recent work have approached fabric manipulation by either compressing the cloth representation into fixed-size latent vector [2, 3, 4] directly learning visual dynamics model in pixel space [5]. However, these representations do not enforce any inductive bias of the cloth physics, leading suboptimal performance and generalization.
Equal contribution, order by dice rolling. "htpps:/ //si tes. google e. com/vo wwwp//iiwwvveeeeeee oth
Conference on Robot Learning (CoRL 2021), London.

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Observation
Planning with VCD
Robot execution
Figure Cloth smoothing by planning using dynamics model with visible connectivity graph.
In contrast to a pixel-based or latent dynamics model, particle-based models have recently been shown to be able to learn dynamics for fluid and plastics [6, 7, 8]. A particle-based dynamics rep- resentation has the following benefits: first, it captures the inductive bias of the underlying physics, since real-world objects are composed of underlying atoms that can be modeled on the micro-level by particles. Second, we can incorporate inductive bias by directly applying the effect of the robot gripper on the particle being grasped (though the effect on the other particles must still be inferred). Last, particle-basee models are invariant to visual features such as object colors or patterns. As such, in this paper we aim to learn a particle-based dynamics model for cloth. However, the challenges in applying the particle-based model cloth that cannot directly observe the underlying particles composing the cloth nor their mesh connections. The problem is made even more chal- lenging due to the partial observability of the cloth from self-occlusions when it is in crumpled configuration.
Our insight into this problem is that, rather than fitting mesh model to the observation, we should learn the visible connectivity dynamics (VCD): dynamics model based on the connectivity structure of the visible portion of the cloth. To do so, we first learn to estimate the viyible connectivity graph: estimate which points in the point cloud observation are connected in the underlying cloth mesh (see Figure 1). Estimating the mesh connectívity of the observation is simplification of the problem of fitting single full mesh model of the entire cloth the observation; however, it significantly easier to learn, since we do not need to find a globally consistent explanation of the observation which requires reasoning about occlusion; to estimate the mesh conneetivity of the observation, only need to consider the visible local cloth structure. While the graph is constructed only based on the visible points, show that the dynamics model can be trained to be robust to partial observation.
In this work, we focus on the task of smoothing a piece of cloth from a crumpled configuration. We propose method that infers the observable particles and their connections from the point cloud, learns a visible connectivity dynamics model for the observable portion of the cloth, and uses for planning to smooth the cloth. We show that for smoothing, planning with a visible connectiv- ity dynamics model greatly outperforms state-of-the-arr model-based and model-free reinforcement learning methods that use fixed-size latent vector representation or learn a pixel-based visual dy- namics model. We then demonstrate zero-shot sim-to-real transfer where we deploy the model trained in simulation on a Franka arm and show that the learned model can successfully smooth cloths of different materials, geometries, and colors from crumpled configurations.
2 Related Work
Vision-based Cloth Manipulation: Some papers cloth manipulation assume that the cloth is already lying flat on the table [9. 10, 11]. the cloth starts in an unknown configuration, then one approach is to perform sequence of actions that are designed to move the cloth into set of known configurations from which perception can be performed more easily [12, 13, 1 14]. For example, the robot might first grasp the cloth by an arbitrary point and raise it into the air; it can then detect the lowest point, either while the cloth is held in the air [12, 15, 16, 17, 18, 19] or after throwing the cloth on the table [14]. By constraining the cloth to this configuration set, the task of perceiving the cloth or fitting mesh model [1] is greatly simplified. However, these funneling actions are usually scripted and are not generalizable to different cloth shapes or configurations. In contrast, our work aims enable robot to interact with cloth from arbitrary configurations and shapes.
Other early works designed vision systems for detecting cloth features that can be used for down- stream tasks, such Harris Comer Detector [20] or wrinkle-detector [21]. More examples of such approaches are described [1]. However, these approaches require task-specific manual de- sign of vision features and are typically not robust to different variations of the cloth configuration.

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Policy Learning for Cloth Manipulation: Recently, there have been number of learning based approaches cloth folding and smoothing. One approach to learn policy achieve given manipulation task. Some papers approach this using learning from demonstration. The demonstra- tions can be obtained using heuristic expert [22] scripted sequence of actions based cloth descriptors [23]. Another approach to policy learning is model-free reinforcement learning (RL), which has been applied cloth manipulation [4, 3, 24]. However, policy learning approaches often lack the ability to generalize to novel situations; this especially problematic for cloth manipulation in which the cloth can be in wide variety of crumpled configurations. We compare method to a state-of thee--rr-- policy learning approach [3] and show greatly improved performance.
Model-based RL for Cloth Manipulation: Model-based RL methods learn dynamics model and then use it for planning. Model-basee based reinforcement learning methods have many benefits such as sample efficiency, interpretability, and generalizability to multiple tasks. Previous works have tried learn pixel-based dynamics model that directly predict the future cloth images after action is applied [25, 5]. However, learning a visual model for image prediction difficult and the predicted images usually blurry, unable capture the details of the cloth. Another approach is represent the cloth with a fixed-size latent vector representation [2] and to plan in that latent space. However, cloth has intrinsic high dimension state representation; thus, such compressed representations typically lose the fine-gen ane-grained details of their environment and are unsuitable for capturing the low- level details of the cloth's shape. such folds wrinkles, which can be important for folding or other manipulation tasks. Our method also falls into the model-based RL category; unlike previous works, we learn particle based dynamics model [6. 7], which can better capture the cloth dynamics due to the inductive bias of the particle representation. Additionally, the particle representation is invariant to visual features and enables easier sim-to-real transfer.
(=1+1
(a)
Predict mesh dynamics
Dynamics GNN
BCRD oburvation
Nonelized doaal
0
Predicted mesh
Edge Prediction
odges
GNN
(b)
(c)
Processor Message passing
Processor Message passing
Decoder
Decoder
Encoder
Encoder
Predict
Predict accelenation node
(a) Overview our visible connectivity dynamics model. takes in the voxelized point cloud, constructs the mesh and predicts the dynamics for the point cloud. (b) Architecture for the edge prediction GNN which takes in the point cloud connected by the nearby edges and predicts for each nearby edge whether it is mesh edge. (c) Architecture for the dynamics GNN which takes in the point cloud connected by both the nearby edges and the mesh edges and predict the acceleration of each point the point cloud.
Figure 2:
3 Method
An overview of our method, VCD (Visible Connectivity Dynamics), can be found in Figure 2. We represent the cloth using Visible Connectivity Graph, which we connect points partial point cloud with nearby edges and the inferred mesh edges. Next, we learn a dynamics model over this graph, and finally we use this dynamics model for planning robot actions.
3.1 Graph Representation of Cloth Dynamics
We represent the state of a cloth with a graph (V, E). The nodes fri)-e.... represent the particles that compose the cloth, where 2s (I), 2.) denotes the particle's current position and

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velocity, respectively. There are two types of edges E in the graph, representing two types of inter- actions between the particles: mesh edges and nearby edges. The mesh edges, EM, represent the connections among the particles on the underlying cloth mesh. The mesh connectivity is determined by the structure of the cloth and does not change throughout time. Each edge etj (v,, v4) E E connects nodes i/ to b/ and models the mesh connection hetween them. The other type of edges are nearby edges, E which model the collision dynamics among two particles that are nearby in space. These which can bring two particles close to each other even if they are not connected by a mesh edge. Unlike the mesh edges which stay the same throughout time, these nearby edges dynamically constructed at each time step based on the following criteria:
be different from the mesh edges due to the folded configuration of the cloth,
(1) where R is distance threshold and Fi.e: 2,, are the positions of particles i, j at time stept. t. Through- out the paper, we use the subscript to denotes the state of a variable at time step t if the variable changes with time. Additionally, we assume that E C E since mesh edge connects nodes that are close to each other and hence should also satisfy Eqn.
ET = (eog | - z|ام|2 < R).
3.2 Inferring Visible Connectivity from a Partial Point Cloud
In the real world, we observe the cloth in the form of a partial point cloud. In this case, we represent the nodes of the graph using the partial point cloud and infer the connectivity among these observed points. We denote the raw point cloud observation as Praw = {z, i==...NN... where is the position of each point and Nraw is the number of points. We first pre-proos peoeesocees the point cloud by filtering it with a voxel grid filter: we overlay a 3d voxel grid over the observed point cloud and then take the centroid of the points inside each voxel obtain voxelized point cloud p (m=))=1,.... N.. This preprocessing step is done both in simulation training and in the real world, which makes our method agnostic the density of the observed point cloud and more robust during sim2real transfer.
We create graph node i/ for each point x; in the voxelized point cloud P. The nearby edges are then constructed by applying the criterion from Eqn. 1. However, inferring the mesh edges is less straightforward, since the real world we cannot directly perceive the underlying cloth mesh connectivity. To overcome this challenge, we use a graph neural network (GNN) [26] to infer the mesh edges from the voxelized point cloud, Given the positions of the points in P. we first construct a graph (P, E with only the nearby edges based on Eqn. 1. As we assume EM C E we then train a classifier, which is a GNN, to estimate whether each nearby edge e E EC is also a mesh edge. We denote this edge GNN as Gedge. The edge GNN takes as input the graph E),. propagates information along the graph edges in a latent vector space, and finally decodes the latent vectors into a binary prediction for each edge E F.C (predicting whether the edge is also a mesh edge). For the edge GNN, we use the network architecture in previous work [7] (referred to as GNS). See Appendix A. for the detailed architecture. The edge GNN is trained in simulation, where we obtain labels for the mesh edges based on the ground-truth mesh of the simulated cloth. After training, can then be deployed in the real world to infer the mesh edges from the point cloud. We defer the description of how we obtain the ground -tuth mesh labels in Sec. 3.5.
3.3 Modeling Visible Connectivity Dynamics with a GNN
In order to predict the effect of robot's action on the cloth, we must model the cloth dynamics. While there exists various physics simulators that support simulation of cloth dynamies [27, 28, 29]. applying these simulators for a real cloth is still challenging due to two difficulties: first, only a partial point cloud of crumpled cloth is observed in the real world, usually with many self- occlusions. Second, the estimated mesh edges from Sec. 3.2 may not all be accurate. To handle these challenges, we learn dynamics model based on the voxelized partial point cloud and its inferred visible connectivity (Sec. 3.2). Formally, given the cloth graph G, = (V, E). a dynamics GNN Gaya predicts the particle accelerations in the next time step, which can then be integrated to update the particle positions and velocities. Here, V refers to the voxelized point cloud, and E refers to inferred visible connectivity that includes both the predicted mesh edges EM well the nearby edges. Our dynamics GNN Gayn uses the similar GNS architecture as the Gedge- It takes a cloth mesh input with state information edges in a latent vector space, and finally decodes the latent vectors into the predicted acceleration on each node. See Appendix A.I for the detailed architecture of the GNN.
each node, propagates the information along the graph

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3.4 Planning with Pick-and Actions
We plan in a high-level, pick-and-place action space over the VCD model. For each action (Tiick, Jplace.. J. the gripper grasps the cloth at Tpick, moves to Eplace, and then drops the cloth. As the GNN dynamics model is only trained to predict the changes of the particle states in small time intervals in order to accurately model the interactions among particles, we decompose each high- level action into a sequence of low-level movements, where each low-level movement is small delta movement of the gripper and can be achieved in a short time. Specifically, we generate a sequence of
small delta movements A A from the high-level action, where Tpick +> Ar;
2place. Each delta movement Ar; moves the gripper a small distance along the pick-and-place direction and the motion can be predicted by the dynamics GNN in single step. When the gripper is grasping the cloth, we denote the picked point as u. We assume that the picked point is rigidly attached to the gripper; thus, when considering the effect of the tth low-level movement of the robot gripper, we modify the graph by directly setting the picked point u's position uu,, piick + Σi= Ar, and velocity šu. Ar;/ At, where At is the time for one low-level movement step. The dynamics GNN will then propagate the effect of the action along the graph when predicting future states. For the initial steps where the historic velocities are not available, we pad them with zeros for input to the dynamics GNN. If no point is picked, e.g., after the gripper releases the picked point, then the dynamics model is rolled out without manually setting any particle state.
Our goal is to smooth piece of cloth from crumpled configuration. To compute the reward based on either the observed or the predicted point cloud, we treat each point in the point cloud as a sphere with radius R and compute the covered area of these spheres when projected onto the ground plane. Due to computational limitations, we greedily optimize this reward over the predicted states of the point cloud after a one-step high-level pick-and-place action rather than optimizing over sequence of pick-and place actions. Given the current voxelized point cloud of a crumpled cloth P. we first estimate the mesh edges using the edge predictor EM Gedge ( ((P, E))) We keep the mesh edges fixed throughout the rollout of a pick-and and-place trajectory since the structure of the cloth is fixed. In theory, it could be helpful to update the mesh edges based on the newly observed point cloud at each low-level step, but this is challenging due to the heavy occlusion from the robot's s arm during the execution of a pick-and-plac action. After the execution of each pick-and-place action, new particles may be revealed and we update the mesh edges when re- re-planning the next action. The pseudocode of the planning procedure can be found in the appendix.
3.5 Training in Simulation
The simulator we use for training is Nvidia Flex, particle-based simulator with position-based dynamics [30, 31J, wrapped in SoftGym [28]. In Flex, a cloth is modeled as a grid of particles, with spring connections between particles to model the bending and stretching constraints.
One challenge that we must address is that the points in the observed partial point cloud do not di- rectly correspond to the underlying grid of particles in the cloth simulator. This presents a challenge for obtaining the ground-truth labels used for training the dynamics GNN and the edge GNN, in- cluding the acceleration for each point in the observed point cloud and the mesh edges among them. To address this issue, we perform bipartite graph matching to match each point in the voxelized point cloud to a simulated particle by minimizing the Euclidean distance between the matched pairs. Details about the matching can be found in the appendix. After get the mapping from the points to the simulator particles, the ground -ruth acceleration of each point is simply assigned to be the acceleration of its mapped particle, which is used for training the dynamics GNN. For training the edge GNN, a nearby edge is assumed to be a mesh edge if the mapped simulation particles of the edge's 's both end points are connected by spring in the simulator.
3.6 Graph Imitation Learning for Occlusion Reasoning
To better allow our model to reason about occlusions, we introduce graph-based privileged imita- tion learning, which transfers features from a teacher model trained on the full cloth to a student model trained the partial cloth observation. This idea is related to other recent work which trains a student with partial observations to imitate a teacher with full-state information [32, 33, 34]. Specifically, we first train privileged teacher dynamics model with ground-truth information of the particle state of the full cloth, i.c., it takes as input all particles (including the occluded particles). Next, we train the student model, which takes partial point cloud as input. The student is trained
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VCD (ours)
VCD-graph--mitttiion
Rectangular Cloth (Trained on Square)
T-shirt (Trained Square)
Square Cloth
Normalized Improvement
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of pick-and-place actions
of pick-and-place actions
pick-and-place actions
Figure 3: Normalized improvement square cloth (left), rectangular cloth (middle), and t-shirt (right) for varying number of pick-and-place actions. The height of the bars show the median while the error bars show the 25 percentile. For detailed numbers, see the appendix.
to imitate the features of the corresponding nodes in the teacher [33]. Graph imitation offers direct supervision the intermediate features and enables the student model implicitly reason about the occluded part of the cloth, by imitating the features from the teacher which has full state informa- tion. We also introduce an auxiliary task of reward (covered area) prediction [35], which implicitly encodes the cloth shape into the learned features. Details on our graph-based privileged imitation learning can be found in the appendix.
4 Experiments
4.1 Experimental Setup
Simulation Setup As mentioned, we use the Nvidia Flex simulator wrapped in SoftGym [28] for training. The robot gripper is modeled spherical picker that move freely in 3D space and can be activated so the nearest particle will attached to it. For training, we generate random pick- and-place trajectories
a square cloth. The side length of the cloth varies from 25 to 28 cm. For evaluation, we consider three different shapes: 1) the same type of square cloth as used training; 2) Rectangular cloth, with its length and width sampled from [19, 21] x [31, 34] cm. 3) Two layered For each shape, the experiment was run 40 times, each time with a different initial configuration of the fabric. We report the 25%, 50% and 75% (Q25, Q50- Q7s) percentiles of the performance. For all our quantitative results, numbers after + denotes max( (250 Q2sl. (Q5. -ooo)).
T-shirt (the square cloth used for training was single-layerede
Our goal for cloth smoothing is to maximize the covered area of the cloth in the top-down view. We report two performance metries: Normalized improvement (NI) and normalized coverage (NC). NI computes the increased covered area normalized by the maximum possible improvement NI 8-80 where 8p, 8, Smar are the initial, achieved, and maximum possible covered area of computes the achieved covered area normalized by the maximum possible covered area. We report NI in the main paper and NC in the appendix.
the cloth. Similarly, NC
We evaluates two variants of our method: Visible Connectivity Dynamics (VCD) and VCD with graph imitation learning. We compare with previous state-of-the art methods for cloth smoothing: VisuoSpatial Foresight (VSF) [5], which learns a visual dynamics model using RGBD data; Con- trastive forward model (CFM) [2], which learns latent dynamics model via contrastive learning; Maximal Value under Placing (MVP) [3], which uses model-free reinforcement learning with specially designed action space. More implementation details can be found in the appendix.
Real World Setup We use our dynamics model trained in simulation smooth cloth in the real world with a Franka Emika Panda robot arm and standard panda gripper, with Frankalnterface library [36]. We obtain RGBD images from side view Azure Kinect camera. We use color thresh- olding for segmenting the cloth and obtain the cloth point cloud. We evaluate on three pieces of cloth: Two square towels made of cotton and silk respectively, and one t-shirt made of cotton. We use our dynamics model trained in simulation without any fine-tuning. More details are in the ap- pendix.

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4.2 Simulation Results
For each method, we report the after different numbers of pick-and-place actions. A smoothing trajectory ends early when NI90. We note that the edge GNN can achieve a high prediction accu- racy of 0.91 on the validation dataset. See appendix for visualizations of the edge GNN prediction.
We first test all methods on the same type of square cloth used in training. The results are shown in Figure 3 (left). Under any given number of pick-and-pa alddpllae actions, VCD greatly outperforms all of the baselines. The graph imitation learning approach described in Section 3.6 further improves the performance. To test the generalization of these methods to novel cloth shapes that are not seen during training, we further evaluate on a rectangular cloth and a t-shirt. For this experiment we only compare VCD to VSF, since VSF achieves the best performance on the square cloth among all the baselines. The results are summarized in Figure 3 (middle and right). VCD shows larger improvement over VSF on the rectangular cloth. T-shirt is more different from the training square cloth and VSF completely fails, while VCD still shows good generalization. The graph imitation learning still leads to marginal improvement and better stability rectangular since it has a similar shape to the square cloth. However, as the t-shirt has very different shape compared to the square cloth, VCD-graph-imitation does not lead to much improvement and has larger variance on it.
Since VCD learns particle-based dynamics model, it incorporates the inductive bias of the cloth structure, which leads to better performance and stronger generalization across cloth shapes, com- pared to RGB based method like VSF Please see the appendix for examples of some planned pick- and-place action sequences of our method on all cloth shapes as well visualizations of the predic- tions of our model.
frona
frona
from
troee 5
foms
Sat
Frome
frems
nrom
hroms 3
frme
Pome 2
froms
nome 10
Parrs
Figure 4: Smoothing cloths of different colors, materials and shapes with method Franka robots square cotton (top), square silk (middle), cotton t-shirt (bottom). Each row shows one trajectory. Frame shows the initial configuration of the cloth, and each frame after shows the observation after some number of pick-and-plas actions, with the number labeled on the frame. The green arrow shows the 2D projection of the pick-and -laace action executed.
# of pick-and-place
Material
Cotton Square Cloth
0.342 0.265
U.725 0.445
0.941 0.360
0.941 0 0.153
Silk Square Cloth
0.456 + 0.197
0.643 + 0.391
0.952
0.229
0.952 = 0.095
Cotton T-Shirt
0.265 0.119
0.356 0.096
0.502 0.135
0.619 0.155
Table Normalized improvement VCD the real world.
4.3 Real-world Results
We also evaluate our method for smoothing in the real world. We only evaluate VCD (i.e., without graph imitation learning) since it works to evaluate the baselines in the real world due to the difficulties of transferring their RGB data direct input to the dynamics model
stably simulation. Unfortunately, were not able
policies from simulation. All of the baselines or the learned policy, making them sensitive to the camera view and visual features. In contrast, method uses point cloud input, which makes it robust to the camera position well as variation in visual features such as the cloth color or patterns. The point cloud representation allows method to easily transfer to the real world.
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We evaluate 12 trajectories for each cloth. The quantitative results are in Table I and visualization of smoothing sequences shown in Figure Despite the drastic differences of the cotton and silk cloths in visual appearances, shapes, as well as the different dynamics, our model is able to smooth the cotton and silk cloths and generalize well t-shirt. We also report the performance if our method is able to terminate optimally in hindsight and choose the frame with the highest performance in each trajectory; the result is shown in the last column of Table 1. Videos of complete trajectories and the model predicted rollouts can be found on our project website.
4.4 Ablation Studies
We perform the following ablations to study the contribution of each component of our method. The first ablation replaces the learned GNN dy- namics model with the Flex simulator to test whether a learned dynamics model performs better for our task than the physical simulator. In more detail, after we use the edge GNN to in- fer the mesh edges on the point cloud, we create a cloth using Flex where a particle is created at each location of the voxelized points and spring connection is added for each inferred mesh edge. The results is shown in Table 2, row 2. We see that using the Flex simulator instead of the dynamics GNN produces significantly worse perfor- mance. The main reason is that the cloth created from the partial point cloud with the inferred mesh edges deviates from the common cloth mesh structure used in Flex; thus, using the Flex simulator under this condition does not create realistic dynamics. On the other hand, the dynamics GNN is trained directly on the partial point cloud; therefore it can learn to compensate for the partial ob- servability when predicting the cloth dynamics. This ablation validates the importance of using a dynamics GNN to learn the dynamics of the partially observable point cloud.
Algorithm
Nomalized Improvement
VCD (Our method)
0.778 = 0. 206
Replace dynamice GNN with Flex No edge GNN (dynamic nearhy edges)
0.616 :: 0.143
0.531 1: 0.296
No edge GNN (fxeed nearby edges) Remove edge GNN at test time
0.599 + 0.327
0.259 = 0.118
Table 2: Normalized improvement of all ablations in simulation after 10 pick-and-place actions.
The next set of ablations aims to test whether using an edge GNN to infer the mesh edges as de- scribed in Section 3.1 is necessary for learning a good dynamics model. First, we train a dynamics GNN without using the edge GNN, where the edges are constructed solely based on distance by Eqn. (I), Since this ablation does not use an edge GNN, it cannot have two different edge types (nearby edges vs mesh edges). Thus at test time, all edges can either be kept fixed throughout the trajectory (similar to the mesh edges in our model), or dynamically reconstructed using Eqn. (1) at each time step (similar to the nearby edges in our model). The results of these two ablations are shown in Table 2, rows 3 and 4. As can be seen, the performance is worse without the edge GNN.
Additionally, we perform another ablation where we train with both nearby edges and mesh edges, but at test time, do not use edge GNN to infer the edge type; instead consider the edges that satisfy the criteria of Eqn. (1) in the first time step as the mesh edges. The result of this ablation is shown in Table 2, row 5. The performance is again much worse. All these ablations validate the importance of using an edge GNN to infer the mesh edges.
Conclusion
In this paper, we propose the visible connectivity dynamics (VCD) model, that infers a visibility connectivity graph from the partial point cloud and learns a particle-based dynamics model over the graph for planning to perform cloth smoothing. VCD has the advantage of posing strong inductive bias that fits the underlying cloth physics, being invariant to visual features, and being interpretable. We show that VCD greatly outperforms previous state-of-the methods for cloth smoothing, and achieves zero-shot sim-to-real transfer on a Franka arm for smoothing various types of cloth.
Our work demonstrates the importance of the choice of state representation for efficient and gen- eralizable manipulation, as well the benefits of graph-based representation. While there may not be a universal representation suitable for all objects, we believe that a graph representation can be an important alternative to raw images or latent vectors, especially for deformable objects such as cloth. In the future, we hope VCD can also be applied to different types of object manipulation tasks, such as manipulation of cables, bags, and food.
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Acknowledgments
We would like to thank Wen-Hsuan Chu for his initial Pytorch implementation of the GNS model. We would like to thank members of the RPAD lab, Gokul Swamy and Erica Weng for their feedback on the early draft of the paper. This material is based upon work supported by the National Science Foundation under Grant No. IIS- 1849154 and LG Electronics.
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Reviews:
[{"review_id": "vw_RI95RAZ2", "reviewer": "Reviewer_ViDy", "summary": "This paper presents a new model-based RL approach for cloth manipulation, which involves learning a novel graph-neural-net based dynamics of point-clouds. The system appears to outperform prior model-free and model-based approaches, and shows impressive sim2real transfer. ", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n\n- The paper proposes a viable solution to the Cloth manipulation problem, which is very hard because of partial observability, high configuration space, and underlying real-world soft-body dynamics. \n\n- Zero shot sim2real transfer is impressive.\n\nWeaknesses\n\n- I found the paper hard to read in certain sections particularly the details of GNNs and how exactly the dynamics is simulated.\n\n- Not much analyses is provided on how sensitive the overall success is to the choice of architecture\n\n- It is surprising that one step dynamics fitting works for this application since other model-based RL approaches crucially rely on multi-step losses or DAGGER like corrections.\n\n- One baseline that would be interesting is blackbox policy optimization against FLEX cloth models directly.\n ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "Novel approach for model-based cloth manipulation that shows promising sim2real transfer and relies on a new way to use GNNs to model point cloud dynamics. Overall, the presentation of architectural details could be significantly improved.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "issues": "See weaknesses above.", "reviewer_expertise": "Very good: Comprehensive knowledge of the area"}, "extra_text": {}}, {"review_id": "H2QkafvEDn", "reviewer": "Reviewer_9vXf", "summary": "This paper proposed a general and efficient cloth smoothing scheme using the predicted visible connectivity dynamics (VCD).\nBy representing the cloth shape as a graph structure using GNN with point cloud data as input, the authors can obtain a representation of the dynamics that is robust to the effects of visibility and self occlusion.\nAlthough the control policy used seems to be simple, it is still able to achieve smoothing of multiple types of cloths efficiently.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The effectiveness of the framework was clearly demonstrated through careful comparative experiments. \n- The contribution of each component in the framework was also verified by conducting ablation experiments, which increased the value of this paper.\n- I think this paper is not on a new technology, but rather on a proper system integration for using GNNs for cloth shape prediction with reliable procedure.\n\nWeaknesses:\n- It is unclear how to optimize the design parameters that may be correlated with the size of the object, such as voxelization and node connection threshold.\n- The control policy was simple and did not make full use of the high-level dynamics model obtained, and although it was faster than conventional methods, it was still not at a practical level.\n- It is difficult to say that this paper is self-contained, since much of the information is referred to in appendices and the past papers.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "The proposed VCD has been carefully implemented from the prediction method to verification, and is a significant contribution.\nAlthough there are still some open problems, they are not within the scope of this paper and are well worthy of acceptance.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "reviewer_expertise": "Good: General knowledge of the area"}, "extra_text": {"issues": "- The proposed graph representation method seems to produce dense graphs, but is there any problem of computational cost? \nAlso, isn't there a trade-off between computational cost and accuracy in terms of the degree of voxelization?\n- As auxiliary purpose inspired by active sensing to the control strategy, is it possible to give the reward for behavior that increases the prediction accuracy of VCD?\n- It seems to be a learning problem without accurate supervised signals.\nI would like to know whether this learning problem is unstable due to noises and/or outliers from wrong annotations.\n"}}, {"review_id": "5p-_1G1gBfu", "reviewer": "Reviewer_Lzhn", "summary": "In this work, a method that learns a visible connectivity dynamics model is presented for learning the observable portion of the point cloud and uses that for cloth smoothing task. The results of the proposed method was compared with state-of-the-art model-based and model-free reinforcement learning methods, showing promising results. The experiments also showed that their method is able to generalise for different shapes of fabrics and demonstrated zero-short sim-to-real transfer. ", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. The main contribution of the paper is implementation of a particle-based dynamics model for visible portion of cloth point cloud (visible connectivity dynamics) to accomodate a partial visibility. The model learns dynamics using the network presented in [7].\n2. The shape specific training is addressed by having a student model with partial observations learns to imitate a teacher model with full-state information.\n\nWeaknesses:\n1. The proposed model predicts the mesh of the fabric but it is not clear that how robot action is taken into consideration in the model. It is obvious that the robot motion, even small motions, will change the state of the fabric and as a result will change the mesh to be predicted. In line 11 of that algorithm, it is mentioned that the inputs to the dynamic network are: the current cloth points (after the pick points were moved), historical cloth points, picked point and set of collision and mesh edges. It is surprising that the robot action is not taken in the input as it will change the state of the fabric and it is also easy to obtain.\n2. The presentation can be further improved by including more details of the presented method. For example, how is the distance threshold R determined? I believe the choice is important in determining the collision edges and R may be different for different cloth types and sizes.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "As written in the Strengths and Weaknesses section, the paper proposes an interesting method for cloth smoothing. The proposed approach is leveraging a network presented in previous works, but implemented in a different application, which could advance the development of methods in robot cloth smoothing. The paper is well presented in general but the presentation can be further improved by including more details of the presented method. For example, how is the distance threshold R determined? ", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "Very good: Comprehensive knowledge of the area"}, "extra_text": {"issues": "1.\tIn Figure 3 there is no definition of error bars, please define their meaning (are these standard deviation, standard error or the percentil range described at the beginning of section 4.1.).\n2.\tThe article is well organized and easy to read, however, in my opinion, the conclusion section could have been elaborated a bit more.\n3.\tThe statement “for all shapes, we test on 40 initial configurations” (line 238) seems not fully clear for me, this could be rephrased. I believe authors mean that for each shape the experiment was run 40 times, each time with different initial configuration of the fabric (cloth or T-shirt).\n"}}, {"review_id": "1Rh9IY0fR0Y", "reviewer": "Reviewer_hhGm", "summary": "Authors proposed a particle-based model to enable manipulation with cloths. The proposed approach, VCD, is able to learn effective models of the cloth, and it works effectively under the condition of partial observability. The efficacy of the approach is validated both in simulation and on a real robot.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength:\nA very interesting way to model the cloth with particles\nA very nice way to use teacher-student model to learn a model for partial observability\nImpressive results that the learned model can sim-to-real transfer\n\nWeakness:\nThe approach is only tested on the smoothing task. It is unclear at this point how the approach performs on other tasks such as folding.\nIt is hard to specify specific goal configurations in the current formulation.\nThe current formulation can only consider a single deformable object at a time.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "I find this paper very easy to follow even without personal experience working with deformable object manipulation. Authors also make their contribution clear in the writing. Experiments are very extensive and results are very impressive.", "technical_quality": "Excellent: Very convincing and thorough.", "clarity_of_presentation": "Excellent: Enjoyable to read.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "reviewer_expertise": "Very good: Comprehensive knowledge of the area"}, "extra_text": {"issues": "- I mainly have one concern about the approach on real robots: In my understanding, the number of nodes are quite dense for VCD. In the experiment, is the graph generation robust enough to the depth sensor noise? I think authors did not mention this part in the paper. I would like to see the factor of depth sensor noise discussed to fully understand its strength and weakness in the real robot scenario.\n"}}]

Author rebuttal:

Meta-review: All reviewers were generally positive, and found the use of graph neural networks as a dynamics model for deformable objects, to be an interesting and novel contribution. Although there were no major weaknesses raised, in the rebuttal, authors should address the queries raised by the reviewers in order to maintain good review scores. In particular, I would like to hear the authors' opinions on how this method would work with tasks other than smoothing, such as how goal configurations would be specified more generally, as raised by Reviewer hhGm.

--------

The authors have provided an updated paper with new experiments, and have addressed many of the issues raised by the reviewers. This is a novel method for deformable object manipulation, with some good real-world results. All four reviewers recommend acceptance of the paper. Now that the authors have shown that the method works for folding as well as smoothing, I recommend updating the title of the paper.
Decision: Accept (Poster)