Learning Efficient Robotic Garment Manipulation with Standardization
Authors: Changshi Zhou
Feng Luan
hujiarui
Shaoqiang Meng
Zhipeng Wang
Yanchao Dong
Yanmin Zhou
Bin He
Venue: ICML 2025 poster (2025)
Keywords: Deformable Object Manipulation，Bimanual Manipulation，Self-supervised learning
Primary area: applications->robotics

Abstract: Garment manipulation is a significant challenge for robots due to the complex dynamics and potential self-occlusion of garments. Most existing methods of efficient garment unfolding overlook the crucial role of standardization of flattened garments, which could significantly simplify downstream tasks like folding, ironing, and packing. This paper presents APS-Net, a novel approach to garment manipulation that combines unfolding and standardization in a unified framework. APS-Net employs a dual-arm, multi-primitive policy with dynamic fling to quickly unfold crumpled garments and pick-and-place(p&p)  for precise alignment. The purpose of garment standardization during unfolding involves not only maximizing surface coverage but also aligning the garment’s shape and orientation to predefined requirements. To guide effective robot learning, we introduce a novel factorized reward function for standardization, which incorporates garment coverage (Cov), keypoint distance (KD), and intersection-over-union (IoU) metrics. Additionally, we introduce a spatial action mask and an Action Optimized Module to improve unfolding efficiency by selecting actions and operation points effectively. In simulation, APS-Net outperforms state-of-the-art methods for long sleeves, achieving 3.9% better coverage, 5.2% higher IoU, and a 0.14 decrease in KD (7.09% relative reduction). Real-world folding tasks further demonstrate that standardization simplifies the folding process. Project page: https://hellohaia.github.io/APS/

Learning Efficient Robotic Garment Manipulation with Standardization
Changshi Zhou 123 Feng Luan 123
Jiarui Hu 234 Shaogiang Meng 123
Zhipeng Wang 234 Yanchao Dong 234
Yanmin Zhou 234
Bin He 234
Abstract
1. Introduction
Garment manipulation is a significant challenge for robots due to the complex dynamics and po- tential self-occlusion of garments. Most existing methods of efficient garment unfolding overlook the crucial role of standardization of flattened gar- ments, which could significantly simplify down- stream tasks like folding, ironing, and packing. This paper presents APS-Net, a novel approach to garment manipulation that combines unfold- ing and standardization in a unified framework. APS-Net employs a dual-arm armm multi- multi-primitte primitive pol- icy with dynamic fling quickly unfold crum- pled garments and nick-and-place(p66) cise alignment. The purpose of garment standard- ization during unfolding involves not only maxi- mizing surface coverage but also aligning the gar- ment's shape and orientation to predefined require- ments. To guide effective robot learning, we intro- duce novel factorized reward function for stan- dardization, which incorporates garment coverage (Cov), keypoint distance (KD), and intersection- over-union (IoU) metrics. Additionally, we intro- duce a spatial action mask and an Action Opti- mized Module to improve unfolding efficiency by selecting actions and operation points effectively. In simulation, APS-Net outperforms state-of-the- art methods for long sleeves, achieving 3.9% bet- ter coverage, 5.2% higher IoU, and a 0.14 de- crease in KD (7.09% relative reduction). Real- world folding tasks further demonstrate that stan- dardization simplifies the folding process. Project page: https///ellohaia.gitbbb..kk io/PP//.
Garment manipulation(Zhang & Demiris, 2022; Hoque et al., 2022) presents a critical challenge in robotics, with applications ranging from household assistance et al.) and Meaglhcare(Moglia al., 2024; Wang al., 2025) in- dustrial antomation(Arente & Greitans, 2022). Unlike rigid objects( (aang et al., 2024a), garments are deformable(Jing et al., 2023), exhibit complex dynamics(Lin et al., 2022; Zhou et al., 2025b), and are prone to self-occlusion(Huunn et al., 2022), which makes them particularly difficult to ma- nipulate. These challenges are amplified when tasks such as folding(Xue et al., 2023), ironing(Canber) et al., 2023), sorting(Jing et al., 2024), and packing(Chen et al., 2024) require precise control over the garment's 's shape, orientation, and coverage.
for pre-
While significant progress has been made in garment unfold- ing(Zhu et al., 2023), existing methods methods-esppeciale those based on single-arm systems(Salhotra et al., 2023; Chen et al., 2023; Yang et al., 2024b)-ofie often require many inter- actions and long execution times. Dual-arm systems(Ha & Song, 2022; He et al., 2024) that leverage dynamic fling actions have been introduced to speed up the unfolding process, but they focus primarily on maximizing surface coverage, neglecting the crucial task of garment standard- ization. Standardization--aiignin suring consistent orientation(Gu al., 2024b), and achiev- ing maximum surface coverage-is essential for successful downstream tasks, such as folding and ironing. Without standardization, further manipulation becomes increasingly difficult, leading to suboptimal outcomes.
the garment's shape, en-
To address these challenges, we propose a novel approach for garment manipulation, called the Action-Primitio Primitive Se- lector Network (APS-Net) which learns to predict spatial action maps-actions defined on a pixel grid-using over- head RGB-D images. APS-Net integrates both garment unfolding and standardization into a unified framework. By employing a dual-arm, multi-primitive policy, APS-Net in- telligently selects between two key actions: dynamic fling and p&p (see Figure 1). The dynamic fling action used to rapidly unfold crumpled garments, while p&p ensures fine- tuned alignment and standardization, preparing the garment for downstream tasks such as folding.
Shanghai Research Institute for Intelligent Autonomous Sys- tems, Tongji University, Shanghai, China "The National Key Lab- oratory of Autonomous Intelligent Unmanned Systems, Tongji University. Shanghai 201210, China 'The Frontiers Science Cen- ter for Intelligent Autonomous Systems, Shanghai 201210, China "College of Electronics and Information Engineering, Tongji Uni- versity, Shanghai 201 104, China. Correspondence to: Yanmin Zhou <yammin, zhou @ tongji.edu.cn>.
Proceedings of the 4nnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Learning Efficient Robotic Garment Manipulation with Standardization
Primitive
Initial
Action Sequence
Fling
Pick
Lift
Fling
Place
P&P
Pick
Lift
Move
Place
Fold
Pick
Pick
Pick
Place
Figure 1. Action Primitives. The fling action involves the robot grasping two predefined points on the garment, lifting and stretching then performing a controlled fling motion forward and backward while gradually lowering onto the workspace. The p&p action involves robot grasping garment a specified pick point, lifting it, moving target place point, and releasing In the fold action, keypoint detection selects the pick and place points, and both arms execute heuristic folding.
To ensure effective standardization, introduce novel factorized reward function for each action primitive. This function incorporates garment coverage, keypoint distance, and IoU metrics both before and after each action. This ensures that once unfolded, the garment achieves maximum coverage, maintains consistent orientation, and aligns with its goal shape, while preserving visible keypoints, which are essential for downstream folding tasks. Further, address the issue of invalid action selections-suce outside the garment regions prone to arm collisions-we introduce spatial action mask that filters out actions di- rected at void regions. Additionally, we present the action optimized module, which enhances unfolding efficiency by focusing on keypoints, particularly the shoulder regions, during the dynamic fling process.
trary crumpled states into standardized configurations, while preserving clear visible keypoints for down- stream tasks.
We introduce factorized reward function that com- bines garment coverage, IoU, and keypoints to guide APS-Net training.
We design a spatial action mask to filter out invalid actions and introduce an action optimized module that enhances unfolding efficiency by targeting shoulder keypoints during dynamic fling.
targeting areas
We conduct extensive real-world experiments on gar- ments from three categories- sleeve, jumpsuits, and
the effectiveness of APS- Net in both garment standardization and subsequent folding tasks using a dual-URS robot.
To validate the effectiveness of garment standardization in downstream tasks like folding, we combine garment key- point detection with heuristic folding methods. Additionally, we collect real images of various garment types and create a dedicated dataset for garment folding keypoint detection. Experimental results demonstrate that our method success- fully unfolds arbitrarily crumpled garment configurations into standardized states, simplifying the downstream task.
2. Related work
2.1. Garment unfolding
With the advancement of deep learning (Yang al., 2025a; Bi al., 2025b; Xin et al., 2024; Li al., 2024; Yang et al., 2025b; Liu et al., 2022), large language models (LLMs) (Jiang et al., 2025; Tan et al., 2025; Bi et al., 2025a), and reinforcement learning models (Zhou et al., 2025a; Tang et al., 2025; Havrilla et al., 2024), an increasing number of studies have begun to address cloth manipulation within the
The main contributions of this paper are as follows:
We propose APS-Net, self-supervised learning net- work that intelligently selects between fling and p&p actions. effectively unfolds garments from arbi-
2

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Learning Efficient Robotic Garment Manipulation with Standardization
reinforcement learning framework. Among these, garment unfolding (Wu et al., 2024; Raval et al., 2024) remains a par- ticularly challenging task, requiring intricate manipulation to transform crumpled garments into flat, standardized states. Early approaches relied on hand-crafted heuristics (Seita et al., 2020; Sun et al., 2014) or single-arm manipulation strategies (Qian et al., 2020; Lee et al., 2021), often requir- ing multiple interactions to achieve successful unfolding. More recent methods, such those by Zhu et al. (2023) and Chen et al. (2023), employ supervised learning techniques like semantic segmentation and grasping pose estimation to improve unfolding performance. Wu et al. (2023) further introduced topological visual correspondences to handle gar- ments with varying deformations. However, these methods still rely heavily on large labeled datasets and struggle when dealing with heavily wrinkled or deformed garments.
reduce task complexity and accommodate diverse garment types. Lips et al. (2024) and Canberk et al. (2023) used syn- thetic data for training keypoint detection models, achieving effective results, but struggled with the sim-to-real gap in real-world. To address these challenges, we collect a diverse dataset of 10,000 garment states with a coverage rate above 0.8 during ASP-Net training. We then train a keypoint detec- tion model with (Chenn et al., 2017) and fine-tune it using 1,100 real-world images, improving robustness and adaptability in practical scenarios.
3. Methods
3.1. Problem Formulation
Standardized garment manipulation involves a sequence of actions that transform a crumpled garment into a standard- ized, aligned state, followed by folding it into a desired configuration. At each time step t, given RGB-D image Oz E I an action of type m .M is selected based on the policy To. where M is discrete set of pre- defined action primitives (See Figure 1). The policy is represented as follows:
Reinforcement learning (RL) provides promising alter- native by enabling robots to optimize unfolding strategies through direct interaction with the environment. Chen et al. (2022) introduced high-velocity dynamic fling actions, but performance plateaus once a surface coverage threshold is reached. Avigal al. (2022) and He al. (2024) combined dynamic fling with static pick-and-place actions, but pri- marily focused coverage while neglecting standardized alignment. Canberk et al. (2023) explored both single-arm and dual-arm manipulation, though their approach suffers from a significant sim-to-real gap that limits upplicability in real-world scenarios. To address these challenges, we pro- pose APS-Net, which ensures efficient garment unfolding with standardized alignment. To reduce the sim-to-real gap. APt-Net uses RGBD input for spatial accuracy, simulates cloth with variable mass and stiffness, and adds procedural wrinkles to enhance realism.
(o:) az
(1)
The chosen action at is then applied to the garment, resulting in a new state o1+1, determined by the state transition T:
Oc+1 + T(oo, ae)
(2)
This process continues until the garment reaches a suffi- ciently smooth state Osmoath- After which keypoint detection identifies features for final folding:
K₂ detteaa ( (Osmooth)
(3)
2.2. Garment folding
The garment is then folded based on the detected keypoints using the fold primitive:
Garment folding(Gu et al., 2024a) is crucial in applica- tions such as hospitals, homes, and warehouses, where efficient fabric manipulation is essential. Farly methods relied on traditional image processing to identify garment features(Maitin MaittresMMatiinnShpaard... et al., 2010; Doumanoglou al., 2016; Stria et al., 2014; Ma al., 2024), but their strong assumptions about garment types and textures limited gen- eralization to real-world scenarios. More recent approaches leverage goul-conditioned policies(Zhou et al., 2024: Chane- Sane et al., 2021; Kim et al., 2024) using reinforcement learning, self-supervised learning, and imitation learning. For example, Mo et al. (2022) introduced space-time at- tention mechanism for multi-step folding, while Weng et al. (2022) used optical flow techniques to improve performance. However, these methods still depend on predefined goal states, which are often unavailable for novel instances. Re- cent research has focused on learning- based methods to detect key garment features, such as corners and edges, to
Ofolded (ioll( (Osmoothh Ko)
(4)
During this process, we formulate the unfolding process as self-supervised learning problem, where the policy ny is learned. The folding process is treated as a supervised learn- ing problem, where Kdeteer learned for keypoint detection.
3.2. Action-Primitive Selector Network (APS-Net)
Searse-grain dynamic dual-arm flings can efficiently un- fold garments from crumpled states, but are insufficient for the fine- fine-grained adjustments required to achieve stan- dardized alignment. To address this, we introduce the Action-Primitivy Selector Network (APS-Net) (See Fig- ure 2), which intelligently selects the optimal manipulation action fling or p&p for efficient and accurate garment un- folding. Garment unfolding requires precise grasping point
3

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Learning Efficient Robotic Garment Manipulation with Standardization
Garment Action Gym
Grasp
Down
Swing
Swing
Rotated & Scaled
APS-Net
Spatial Action Mask
Cov Eisoodder
Fling Teconder
Dlighest Value
Cuss
PPa Dexender
P&P Spatinl
Action Mask
90
I0U
Fling Decorder
128
Map
argmoz,mto.cc
45
16
P&P Destmner
Hlightet Volue
Imput Image
Keypoint
85
Fliin Spatial
Action Optimized
22.5
4
1llng Desenler
Action Mask
0'
P&P Decomder
Fling Value blpe
Fling Value Map
0.75x
1.0x
1.5x
Yes
env=1
T.ain
Get keypoint
Scale &
Rotation
Keypoint Detection
Sufficiontly
Enconder
Smoothed
Garment
Test
DeepLah
Initial
Step
Step
Step 3
Finial
Figure Approach overview. APS-Net takes producing pair of decoders the fling and p&p decoders which weighted to generate the corresponding Spatial Action Map. Spatial Action Mask then applied to filter out invalid actions. The valid primitive batches are concatenated, and the action to be executed is parameterized the maximum value pixel. Once the garment is sufficiently flattened, keypoint detection-based method employed to perform the folding.
rotated and scaled RGBD images as input and uses three encoders, each
selection for effective manipulation. For dual-arm fling, directly predicting both grasp points is challenging due to collision avoidance. Instead, we reframe the problem by predicting the midpoint (x, y), angle (rotation), and grasp width w, with collision avoidance achieved by adjusting w. For single-arm p&p, collision avoidance not required. However, to maintain consistency with dual-arm fling and provide unified output from the same network, define (x, y) as the pick point, while 0 and w represent the rotation and length between the pick and place points. Therefore, we generalize the two cases as solving for (a,, y, 0, w).
value maps (V,,,)), ((m,2)).... .... mm follows:
TTm = max E (1.
(5)
Here, V.TH represents the maximum desirability value found in the i-th transformed value map for action
The final action primitive is determined by comparing the maximum desirability values from the spatial action maps:
fling p&p
V pick
m
otherwise
Even so, the naive approach of directly predicting (x, y, f, w) fails to account for the invariance of the optimal grasp under the garment's physical transformations. To ad- dress this, apply series of mansformations-rttaiie and scaling to the observation ot, generating set of trans- formed images T, to ensure that the grasp points remain consistent across identical garment configurations. APS-Net then processes these transformed garment images batches to generate spatial action maps representing the desirabil- ity of performing each action each pixel of the garment. For each action primitive E [fling, p&p), APS-Net com- putes the maximum desirability across the set of transformed
Subsequently, the final value Vimat is selected based on the chosen action primitive:
V(nigg)) (pick..)
if m fling
if m p&p
The index is then obtained from the APS-Net output as follows:
(x., U. i) + APSNet(T.) arg max [ymaxx)
(8)

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Learning Efficient Robotic Garment Manipulation with Standardization
F&F Action Execution
(b)
2.0
P&P Spatial
Sode 1.5%
1.5x
Action Mask
1.5%
(u.7-4]
F& Value Mage
200 292.5'
Valid P&P Value
271P 252.8' 3LD"
Valse Map
Highest Value Action Execution
16 cotations
16 rotriions
200.5
2.0g
Action Mask
Flingg Spatial
Soalé 2.0x
1,5x
1.5g
Max Fling Value Map
Valid Filing Valuue Maps
W?
271
99155-3115
16
16 motrioos
dual collisions, the mitation limital
Fling Action Esscation
Figure 3. Spatial Action Mask. The series of smaller maps in (a) represents different slices of the spatial action maps for various primitives, each corresponding to layer different scale and rotation. The masks applied to each layer serve to filter invalid actions those that would cause the robot's 's
to collide or extend beyond the garment.
Once is determined, the corresponding width w and rota- tion angle are retrieved from the i-th entry of the proposal set Tn:
prevent collisions during dual-arm fling operations, constraint is imposed to ensure that the left grasp point (L) remains to the left of the right grasp point (R). This is enforced by limiting the rotation angle to the range 900 90%), ensuring safe and coordinated movements.
0 = Tn
(9)
Based on the above reasoning, we obtain (z, 0, w). Note that 0 a discrete variable. As described in Appendix F, 0 guantized into 16 discrete rotation angles to cover the full 360 range.
3.3. Factorized Reward Function
In this work, we propose novel factorized reward function to train the policy to manipulate the garment into standard- ized unfolding. APS-Net consists three encoders, each computing a distinct reward: coverage reward Rc, Inter- section over Union (IoU) reward R,, and keypoint reward Rx. Each encoder is paired with two decoder heads-one for each action primitive, fling and p&p. These rewards are computed separately for each action primitive and then combined into a weighted sum to guide network training.
The grasp points a defined based the selected action primitive:
((x y + w). (xr y - w)} Qt =
fling
(10)
((,,)), (x, w)}
if m p&p
According to the definition (see Figure 1), for single-arm p&p, both the pick and place points need to be predicted. As indicated in Equation (10), when m = p&p, the pick point is (z, y). and the place point is (x, y - w).
balance the contributions of these reward functions, a weighted sum applied for each action primitive:
For dual-arm arm fling. only the two pick points are predicted, while the place points follow predefined fling trajectory, described in Equation (11). Specifically, when fling, the dual-arm pick points are (I,y + w) and (x, w). During execution, both arms follow the trajectory through lift, swing, and placement phases, dynamically adjusting acceleration and velocity at each stage to perform the fling motion.
RCIK = = Rc + 3 R, +
(8) Rx
(12)
where a, E (0, 1) are hyperparameters. By tuning a and 8, this reward mechanism enables APS-Net to optimize fab- rie coverage, alignment, and keypoint positioning, achieving standardized garment configuration critical for subsequent folding tasks. See Appendix A for more details.
3.4. Spatial Action Mask
fling h4) (0. fm (2))
Directly sampling actions the spatial policy maps can lead to invalid actions, such as empty actions in non non-garment areas or double-arm collisions. To address this, we introduce the Spatial Action Mask (SAM), which filters out infeasi- ble actions, ensuring that the robot only considers feasible
(0. fm bm, h1) - (0. fm. Ap)]
(11)
Here, h2 is the lift height, fm denotes the forward swing distance, bm is the backward swing offset, and hp is the final placement height.
5

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Learning Efficient Robotic Garment Manipulation with Standardization
with the garment's shoulder keypoints K₁ and K2, if they are sufficiently close, ensuring quick flattening(See Figure 5). The module calculates the Euclidean distance between each predicted point and the corresponding keypoint:
15
d(P,, Kj) = (Pr.z - ,²,0)²+
+P K (13)
(8) long sloeve
(b) pants
(c) skimss
where i,j E f1, 2}. Two possible matching combinations are evaluated, with P1 paired with 1ı and paired with k2 which results in the following:
Figure 4. The folding rules for long sleeves, pants, and skirts.
Dj d(Pi, + d(P2, K2)
(14)
and pairing with 1 and with k1, yielding:
Desplabe8
D2 d(Pi, K2) + d(P2,
(15)
D.S D, Vie
The module selects the pair with the minimum total distance, min( D1, D2). If both distances in the selected pair satisfy KG) < 6, where = 5, the predicted points P1 and P2 are replaced by K₁ and K2. Otherwise, they are retained.
(0) &
Iepal tragz
[9,,8) 6
[Pr Ki) K
Acticn
Applac
Figure 5. Action Optimized Module. Optimizes the aligument of garment shoulder keypoints for efficient flattening, based on the predicted shoulder points from APSNet.
4. EXPERIMENTS
4.1. Tasks and Metries
Intersection over Union (IoU): Measures the quality of Garment Unfold by comparing the manipulated Garment's mask with the target mask.
actions within the valid workspace (See Figure 3). Specifi- cally, we define both workspace and garment masks for each action primitive, and the valid action space is determined by their intersection. See Appendix B for more details.
Coverage (Cov): Compares initial and achieved Garment coverage the maximum possible area.
3.5. Keypoint-Basee Strategy for Garment Folding
Success Rate (SR): A task is considered successful if the robot first smooths the crumpled garment and then correctly executes the folding operation according to the specified fold primitive.
Heuristics are highly interpretable and easy to define, mak- ing them effective for garment folding. In this approach, Lbv3 detector(Chen et al., 2017) trained for each garment class, and keypoints from the detected garments are projee-rooected into 3D space, then transformed into the workspace frame of reference. By representing garment as set keypoints, sidestep its infinite DoF by using few meaningful keypoints as the representation, which makes it simpler to define heuristics over them.
In simulation: The cloth modeled as a particle-based grid, where ground-trut -trth particle positions are available. We compute the mean distance between particles in the achieved and target cloth states. A trial is deemed successful if the average particle distance error is less than 0.03.
In the real world: We use the Intersection over Union (loU) between the predicted cloth mask and that of human demonstrator to evaluate performance. the IoU exceeds 0.8, the folding considered successful.
For lone-sleeve garments, we define six key points: two at the sleeves, two the shoulders, and two at the waist. First, fold the sleeves along the folding lines (Figure 4(a)), then fold the garment in half along the central line. For pants and skirts (Figure 4(b) and 4(c)), we define four key points: two at the waist and two at the bottom. Begin by folding along the waist and bottom lines, then fold 3/4 of the waist down toward the bottom key point to complete the fold.
Keypoint distance (KD): Represents the distance between key feature points of the garment.
These metrics are applied to three representative garment manipulation tasks: unfolding and folding long sleeves, skirts, and pants. In all tables, J indicates that lower better while indicates that larger values are preferred.
3.6. Action Optimized Module
When performing fling action to flatten garment, key- points on the shoulders are typically prioritized for efficient flattening. The Action Optimized Module (AOM) refines the APSNet-predicted points P1 and Pa by aligning them
4.2. Baselines
We compare our method with several baselines, includ- ing quasi-static, dynamic, and of (SOTA) ap-
6

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Learning Efficient Robotic Garment Manipulation with Standardization
Step
Step2
Step 3
Step
Step
Step
Step
Step 8
Stop
End
BMd
E30
catr
Ma
P&P
Ge Cara
eie 2000
Lob
eda
Flingbot
S-Fling
Cloth-fannels
6.75
5.00
0.10
2714
0.70
2000
0.000
Ours
Figure 6. Qualitative comparison of simulation unfolding. Our method outperforms the four other methods. The red arrow denotes p&p action, while the green line indicates a fling action. The gray contour line represents the standardized garment template.
Table 1. Results of our method and four baselines.
gitk
pet fing
(MCT Aurret
TYPE
Cov(%))
loU(%)]
KDJ
ReIK J
P&P
61.3
51.6
2.759
0.261
0.6
0.0
S-FLING
80.5
63.3
1.892
0.135
FLINGBOT
80.1
60.9
2.084
0.086
Tplalo Skg
Spandd
CLOTH FUNNLES
87.2
74.1
1.972
0.058
OURS
91.1
79.3
1.832
0.042
Figure 7. Garment Unfolding Coverage and IoU vs. Steps.
proaches. Quasi-static Methods include p&p, where the garment is sequentially lifted and placed, similar to (Lee et al., 2021). Dynamic Methods consist of FlingBot(Ha & Song, 2022), which uses dual-arm dynamic flinging with coverage as the reward function, and S-fling, variant of our method using single fling action. SOTA Methods include Cloth (Canberk al., 2023), which com- bines p&p and fling actions, utilizing factorized distances between cloth particles for unfolding.
Figure 6 and Figure 7 present representative qualitative re- sults. comparison between S-fling, Flingbot, and p&p reveals that the former two methods significantly improve coverage due to the dynamic nature of the fling actions. However, while coarse-grain high-velocity actions ef- fective for unfolding, they are not versatile enough for stan- dardization. Cloth funnels outperform S-fling in coverage and loU by facilitating finer adjustments with quasi-static actions. However, its reward function, based on 3D distance between cloth particles, can lead local minima during policy learning, limiting its ability achieve opti- mal unfolding. In contrast, our method introduces novel 2D plane-based factorized reward function, which achieves higher coverage and IoU with fewer manipulation steps, en- sures garment standardization. and demonstrates the synergy between coarse- coarse-grain high-velociny flings and fine-graie grain p&p actions for optimal performance.
4.3. Results and Analysis
Due to the complex shape and high manipulation difficulty of long sleeves garments, we selected them as the target for our experiments, Each policy was evaluated through 50 trials with random initial configurations of the garments.
For unfolding, Table shows the quantitative results, while

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Learning Efficient Robotic Garment Manipulation with Standardization
Table 3. Effect of different hyperparametters and
Step
Step
Skep
Skop
Supp
Step
Pants
PARAMETER
COV(%))
IOU(%))
KDJ
0.2, 8 0.2
89.9
76.5
1.844
0.2, 8 0.4
91.1
79.3
1.832
a 0.4. 3
90.7
78.6
1.841
Skirts
a 0.4. 3 = 0.4
89.4
77.5
1.863
0.6. 3 = 0.2
88.7
77.9
1.869
Figure Unfolding results for different garment types.
Table 4. Ablation Studies.
METHOD
COV(S))
IOU(%)
KDJ
Table 2. Performance metrics for Pants and Skirts.
W/O KPR
88.2
76.4
1.851
W/O AOM
89.5
77.6
1.860
TYPE
Cov(%)t
ToU(%)t
KDJ
W/O IR
90.3
72.2
1.994
OURS
91.1
79.3
1.832
PANTS
91.8
80.4
1.819
SKIRTS
86.7
79.6
1.825
4.4. Ablation Studies
To evaluate the importance of different components in our method, we conduct ablation experiments by comparing our method with the following, with 50 trials per ablation using random configurations of long sleeves garments:
We trained models on two additional garment categories- pants and skirts usitt--using the same configuration(Sec Figure 8). As shown in Table 2, our method achieves around 80% IoU across all categories, demonstrating its generalizability.
W/O AOM: Excludes the Action Optimized Module, resulting no optimization for the fling action.
W/O IR: Excludes the IoU Reward, which helps the
Unfold
Flod
garment achieve better coverage, potentially reducing accuracy in spatial matching.
Flingbot
W/O KPR:
Removes the KeyPoint Reward, which di- rectly aids keypoint detection, potentially impairing performance in precise keypoint localization.
Clath fannels
Table 4 shows quantitative comparisons with ablations. Clearly, each component improves method's capability.
Ours
Table 5. Results of real world experiments on Garment Unfolding.
METHOD
IOU (%)t
COV (%)
Figure Simulation Folding Qualitative Comparison. We pare the unfolding results of our method with those of other exist- ing methods using our folding heuristic.
P&P
43.7
52.3
FLINGBOT
51.3
72.8
CLOTH FUNNELS
65.2
82.2
OURS
70.5
86.3
For folding, we compare our method with existing ap- proaches. Our method achieves superior folding perfor- mance(See Figure 9). Unlike methods focused solely on maximizing coverage, standardized unfolding aligns the gar- ment's shape and keypoints, which simplifies the folding process and making it well-suited for garment packing in factory settings. In our experiments, we set the hyperparam- eters to a 0.2 and B 0.4. To assess the sensitivity of our method to these parameters, we conducted a series of experiments with different values of and 3 (See Table 3).
4.5. Real World Results
In our real-world experiments, we performed unfolding and folding tasks on long sleeve garments, conducting 15 trials for each group, with models trained in simulated envi- ronment. For the unfolding task, quantitative results are presented in Table 5, Our method achieved the best perfor- mance, reaching 70.5% loU and 86.3% in Cov, while preserving the standardization of garment, thus validating the conclusions drawn from the simulation.
8

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Learning Efficient Robotic Garment Manipulation with Standardization
Unfold
Fold Process
Initial
Flat state
Step
Step 2
Step
Fold State
Instance #1
Instance
Instance
Instance
Figure 10. Real-world Folding.
Table 7. Results of real world experiments on other garment types.
Pants
METHOD
SLEEVE
PANTS
SKIRT
Step
Stop
Finial
FOLD
12/15
14/15
11/15
Initial
Step1
Step
Finial
Figure 11. Real-world Folding in pants and skirts.
(See Figure 11 and Table 7). The results demonstrate that our method generalizes to different types of garments.
Table 6. Success different methods.
5. Conclusion
METHOD
SUCCESS RATE
P&P
1/15
In this paper, presented APS-Net, novel approach for garment manipulation that integrates both unfolding and standardization into unified framework. By leveraging dual-arm, multi muulti-rrimitive policy, APS-Net efficiently un- folds crumpled garments and ensures precise standardiza- tion, which simplifies downstream tasks like folding. We introduced factorized reward function, spatial action mask. and an action optimized module enhance performance. Experimental results show that APS-Net outperforms isting methods in both simulation and real-world folding tasks, demonstrating its potential for practical garment ma- nipulation. Our approach provides solid foundation for further advancements in robot- assisted garment handling for a range of applications.
FLINGBOT
6/15
CLOTH FUNNELS
10/15
OURS
12/15
We applied our keypoint-baas based folding model to various un- folding methods, shown in Table (quantitative results) and Figure 10 (qualitative results). Our model achieved success rate of 12 out of 15, demonstrating superior per- formance compared to existing methods. Furthermore, the results demonstrate that standardization reduces the com- plexity downstream tasks.
Additionally, we conducted experiments on pants and skirts

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Learning Efficient Robotic Garment Manipulation with Standardization
Impact Statement
Chen, L.-C., Papandreou, G., Schroff, F,, and Adam, H. Rethinking atrous convolution for semantic image seg- mentation, 2017. URL https: /ttp/aaxivvv./// / 05587.
This paper presents a method for garment manipulation. has potential applications in household and industrial automation, reducing manual labor. We have not identified any particular ethical issues that need to be emphasized.
Chen, L. Y., Huang, H., Novoseller, E., Seita, D., Ichnowski, J., Laskey, M., Cheng, R., Kollar, T., and Goldberg, K. Efficiently learning single-arm fling motions to smooth garments. In The International Symposium of Robotics Research, pp. 36-5 51. Springer, 2022.
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (No. 62088101), part by the Science and Technology Commission of Shanghai Munic- ipality (No. 2021SHZDZZ0000,, 222R 1467 100), and the Fundamental Research Funds for the Central Universities (No. 22120240291).
Chen, V. K., Chin, L., Choi, J., Zhang, A., and Rus, D. Real-time grocery packing by integrating vision, tactile sensing, and soft fingers. In 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft), pp. 392-399. IEEE, 2024.
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Learning Efficient Robotic Garment Manipulation with Standardization
A. Factorized Reward Function Details
robot. For the p&p action, only one arm is used at time. The valid action space is determined by the union of the workspace masks for the left and right arms, To account for each arm's 's reach, the workspace masks for both arms are each shifted down by w pixels, as described in formula (9) (see Figure 12(c), (d)). The final p&p workspace mask (pdep) is obtained by taking the union of the shifted masks mh me
The Coverage Reward Re measures the proportion of newly covered garment area relative to the total garment area, pro- moting actions that maximize garment coverage:
C
RC=
(16)
Cmuxx
(see Figure 12(e)).
where C' is a function that computes the current area of the cloth based the image pixels, and Cuux is the maximum area of the garment.
For the fling action, both arms are used simultaneously. The valid action space is determined based on the position of the grabbing point p1. If p1 corresponds to the left arm, the left arm's workspace mask is shifted upwards by w pixels, and the right arm's workspace is shifted downwards by w pixels. The valid action space is the intersection of these two shifted masks (see Figure 12()). If P1 corresponds to the right arm, the left arm workspace mask is shifted downwards by w pixels, and the right arm workspace mask is shifted upwards by w pixels. The valid action space is the intersection of these two shifted masks (see Figure 12(k)). The final fling workspace arm mask m is obtained by taking the union of these two intersection masks (see Figure 12()).
The IoU Reward R evaluates the overlap between the cur- rent garment configuration m. and the target configuration Myt. ensuring proper alignment:
me O mgt
R₁
(17)
m U mgil
The Keypoint Reward Rx is designed to evaluate the accu- racy of key garment feature positioning by minimizing the distance between the predicted and target keypoints.
The workspace mask for the action Mw.nask can thus be represented as:
RK= Σ
P Pil
(18)
where p3 represents the current position of keypoint i, p. is its target position, and N is the total number of keypoints.
(phpp) m mm
if = p&p
M maskk
(20)
if m fling
B. Spatial Action Mask Details
Garment mask. The valid garment mask for different ac- tion primitives is computed as follows. In the p&p action, since the grabbing points are directly on the garment, the valid garment mask is simply the original garment mask mg. For the fling action, the grabbing points for both arms are determined using formula (9). To ensure that these points lie within the garment mask, the original garment mask is shifted downward by w pixels for the left arm and upward by w pixels for the right arm. These shifted masks are shown in green and blue (see Figure 13(c)). The final fling gar- ment mask m is obtained by taking the intersection of the original garment mask with both the upward and downward shifted masks (see Figure 13(d). Therefore, the garment mask for action My My.ma is:
Directly sampling actions directly on the existing spatial policy maps will lead to many invalid actions, such as empty actions in parts other than garments. The shape of the gar- ment mask varies due to differences in rotation and scal- ing. To address this, we introduce the Spatial Action Mask (SAM) to filter out invalid actions, ensuring that the robot only considers feasible actions within its workspace.
The robot's arm reach is constrained by its physical lim- its, which are represented by masks in the simulation. The workspace is defined by a table width w = 1.53 m, with the left and right arm bases at positions [0.765, 0, 0] and [-0.765,0 0, Oj, respectively. The maximum reachable dis- tance of single arm is denoted as ra. The pixel radius Tpix is calculated by scaling ra relative w, as:
mg
m pp&
M mask
(21)
r pix = D . rr/w [/
(19)
mg
if m fling
where D is the rendered image dimension. Circular masks for both arms are created, with the left arm centered at the upper half and the right arm at the lower half of the image. These masks are drawn in white, indicating reachable areas (see Figure 12 (a), (b)).
The valid action space is determined by the intersection of the garment mask Mg.mask and the workspace mask Mw.mask:
M₁ = Mp.mask O n.M mask
(22)
This combined mask M, filters the action probabilities out- put by APS-Net. If an action a falls within the valid region (Me(a.) = 1), it retained; otherwise, its probability is set
Workspace Mask. Both the p&p and fling actions rely on workspace masks to filter the valid action space for the
13

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Learning Efficient Robotic Garment Manipulation with Standardization
Initial
Right Mask
P&P
109
100
108
139
138
200
96
M0
(6) Lowered Left Misk
(4) Highest Right Mask
(6) P&P Worllageee Mask
43
42
Fling
143
100
(1) Highest Left Mask
(e) Lowest Left Mack
(b) Highest Right Mark
( Lewest Right Mask
( P1-1.ett
(0) P1-Right
()) Fling Workspace
Figure 12. Dual arm workspace for different action primitives.
C.2. Real World Setup
icm Thay Deezze
Oanccall Maat
Coreed Visk
Our experimental setup comprises 6-DoF URS robot arms, each equipped with a Robotiq 2F-85 gripper, posi- tioned 1.45 meters apart and facing each other. Two top- down RGB-D cameras an Azure Kinect and a Realsense D455-capture the workspace. The Grounded-SAr model (Liu al., 2023; Kirillov et al., 2023) is used to segment the garment from the RGB image with the prompt "garment."" To minimize the domain gap between simulation and real- apply an Affine Transform crop and correct the camera's 's perspective, focusing the central region of the garment. The resulting mask is then multiplied by the RGBD image to generate the input for the model. (See Figure 14) This approach allows the simulation-trained model to be effectively adapted for woal-word use without requiring additional training.
Figure 13. Garment for different action primitives.
to -0, excluding it from consideration. The final action probability T. (a))o)) is:
world images,
To(a))or)
if (ax)
(a:lo)))
(23)
000
if M. = O.
C. Experiment Setup.
C.I. Simulation Setup
D. Data Collection
We built upon the OpenAl Gym API(Brockman et al., 2016) and PyFleX(Li et al., 2018) bindings to Nvidia FleX, inte- grated with SoftGym(Lin et al., 2021), to develop rein- forcement learning environment called Cloth Action Gym, which supports loading arbitrary cloth meshes, such T- shirts, pants, and skirts, through Python API. The robot gripper is modeled as spherical picker that can move freely in 3D space and can be activated to attach to the nearest particle. Observations are rendered Blender 3D, with the cloth's 's color uniformly sampled across specified HSV ranges.
In the simulation environment, we collected two datasets, one for training garment unfolding and another for training garment keypoint detection. Each task is characterized by specific parameters such as the cloth mesh, mass, stiffness, and initial configuration. The cloth meshes were sampled from subset of shirts in the test split of the CLOTH3D datrset(Bertiche al., 2020), resized to ensure they fall within the robot's operational range. The cloth mass was selected from range of [0.2 2.0 kg]. and internal stiff- ness was set between [0.85 kg/s² 0.95 kg/s?]. To initial-
14

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Learning Efficient Robotic Garment Manipulation with Standardization
Azure Kincect
ReeaSennee D
Affine MfineTrunsfort
Top-View
Robotiq 2 2-A3
Robotiq 25-85
Grounded-SAM
Left Arra
Affine Transform
Right Uró Arm
Segmented Garment
WorkSpace
Dual Arm Robot system
Garment Mask
Figure 14. Real-world Setup.
ize the cloth states, we randomly sample from subset of CLOTH3D, which provides curated collection of garment meshes.
after each action:
ARCIK ReIK before
(24)
For the garment unfolding dataset, the initial configuration was created by randomly rotating the garment, selecting a random point on the garment, and dropping it from a height between [0.6 m, 1.6 m]. The garment was then translated by random distance the range of [0 m, 0.25 m), resulting in highly crumpled configuration. Each garment category in- cludes 2000 training tasks and 50 testing tasks using unseen garment meshes.
The value network minimizes the Mean Squared Error (MSE) loss between the ground truth rewta-reward A & and the predicted delta-reward A ARIIK
N
LMSE
Σ N
1
R CIK - ARCIK)2
(25)
For the key-point detection model, training is conducted for 200 epochs with a batch size 32, learning rate of 1.0 x 10 4 and includes rotation and scaling augmentations. The training loss L(y p) is defined as the mean point-wise cross-entropy loss for each keypoint n in N:
For the garment keypoint detection dataset, we filtered im- ages from the replay buffers generated during the unfold task, selecting images where the coverage exceeded 0.8. This process resulted in a dataset of 10000 images for train- ing and 500 for testing. Real-world data collection involved 1100 images, which were labeled using LabelMe.
L(,,)) N
(Un log(pn) + (1 Vv.) log(1 pn))
E. Training Details
(26)
For the unfolding network, the initial observation o, of size (H, W) (128, 128) undergoes 16 rotations (covering 360%) and 5 scales {0.75, 1.0, 1.5, 2.0, 2.5). yielding m 80 stacked transformed observations. Exploration follows 1) with half-lives of 5000 steps for selecting action primitives (fling vs. p&p). Optimization is conducted using the Adam optimizer with a learning rate of 1.0 x 10-3 over 100,000 steps. The value network is supervised using delta-reward values, defined the difference in weighted reward (ARoik) before and
where Dn, is the true label and Pa is the predicted probability for each keypoint n. All experiments are conducted on an
NVIDIA RTX 4090 GPU with an Intel 19-13900K CPU (5.80 GHz), supported by 64 GB RAM on Ubuntu 18.04 LTS.
decaying e-greedy strategy (initial
F. Experimental Results
Figure shows how our method transfers the garment from an arbitrary configuration to a standardized one. Figure 16 presents the qualitative results of garment unfolding in the
15

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Learning Efficient Robotic Garment Manipulation with Standardization
Step
Step 2
Step
Step
Step 5
Step
Step 7
Step
Step 9
End
(a)
mapping
(b)
rotate and scale
To
Cala
(c)
Figure Our method transfers garment arbitrary configuration a standardized configuration. The row shows the spatial action map output by the APS-Net network at each step. The second row maps the highest pixels of the spatial action map to the transformed image each step. producing corresponding actions. The third row applies rotation scaling each step transform these actions back to the original input image.
real world, comparing our method with other approaches. Figure 17, compare the unfolding results of our method with those of Cloth Funnels using folding heuristic. Finally, Figure 18 compares optimized and unoptimized shoulder keypoint alignment for garment flattening.
Unfuld
Feld
Instance #1
Cloth funel
Initial
Ours
P&P
Instance
Flingbot
Chars
Cleth-
Figure 17. We compare the unfolding results of our method with those of Cloth Funnels using our folding heuristic.
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Figure 16. Qualitative results of garment unfolding in real world. Our method outperforms three other methods on long sleeve garments with four different colors and textures.
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Figure 18. Comparison of Optimized Unoptimized Shoulder KeyPoint Alignment for Garment Flattening.
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Reviews:
[{"review_id": "KDLMQ8EU66", "reviewer": "Reviewer_fdEB", "summary": "This paper introduces APS-Net, a novel framework for robotic garment manipulation that seeks to both unfold garments and align them into standardized orientations—essentially “standardizing” them as part of the unfolding process. Unlike many existing solutions that focus on either single-arm quasi-static approaches or dynamic actions that only maximize coverage, APS-Net combines dynamic dual-arm fling actions for fast unfolding with more precise pick-and-place (p&p) actions for alignment. The authors propose a factorized reward function incorporating garment coverage, intersection-over-union (IoU), and keypoint positioning, guiding the system to flatten garments while preserving meaningful geometry for downstream tasks like folding", "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": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the time and effort you have dedicated to evaluating our work, and we are grateful for your recognition of our key contribution—the integration of dynamic flinging with precision pick&place for garment standardization, and the factorized reward function. Below, we provide a point-by-point response to the comments, incorporating additional experiments and analyses.\n\n## Weaknesses\n\n### 1. Fling Reliability Across Cloth Weights and Texture\n\nIn simulation, we tested cloth masses ranging from 0.2 kg to 2.0 kg, with internal stiffness values between 0.85 kg/s² and 0.95 kg/s². Procedurally generated normal maps were used to simulate wrinkles and surface details. In real world, we experimented with garments of various weights, from lightweight long-sleeve shirts (e.g., Instance 2 in Figure 16) to heavier sweaters (e.g., Instance 4 in Figure 16). In terms of texture, we evaluated garments with complex patterns (e.g., Instances 1 and 3 in Figure 16). The results demonstrated that the fling motion operated reliably across all conditions. Therefore, variations in cloth weight and texture do not affect fling reliability.\n\n### 2. Stress Testing Method under Extreme Visual Conditions\n\nWe have tested our method with two garments whose colors are similar to the table surface, both in crumpled and smooth states, to evaluate the performance under extreme conditions. We tested each method 10 times on garments in both crumpled and smooth states to evaluate performance under extreme conditions. The results demonstrate that, even in these challenging scenarios, our segmentation algorithm successfully identifies the garment region (see Figure 1 in https://github.com/hellohaia/img/blob/main/1.pdf).  \n\nHowever, keypoint detection performs poorly under these conditions, as shown in Table 1.1. In the future, we will explore using depth images for both keypoint detection and garment segmentation under extreme conditions, which would mitigate the impact of color similarity.\n\nTable 1.1 Results of Segmentation and Keypoint Detection.\n\n| Method             | Precision (%)  |\n|--------------------|----------------|\n| Segmentation       | 10/10          |\n| Keypoint detection | 0/10           |\n|||\n\n\n\n\n### 3. Discussion of End-to-End Approaches for Garment Folding\n\nRecent works, including end-to-end approaches like SpeedFolding [1] and UniFolding [2], have demonstrated competent garment folding capabilities. Below, we discuss their methods:\n\n\nSpeedFolding is a bimanual system that folds crumpled garments based on user-defined lines. While it performs well on short-sleeve garments, it doesn't generalize to other types. Additionally, it requires 4,300 human-annotated training samples, which is labor-intensive. Its performance degrades with highly crumpled garments, as key areas are occluded, and it struggles with controlling garment orientation during unfolding, limiting standardization.\n\n\nUniFolding uses the UFONet neural network to integrate unfolding and folding into a single policy. Tested on long- and short-sleeve shirts, it hasn't generalized to other garment types. Its performance degrades with highly crumpled garments, as keypoints can be obscured. It relies on labor-intensive human demonstrations in virtual reality for data collection. While it flattens garments, it cannot standardize them, and the model tends to focus on manipulating the sleeves, often causing the collar to roll in, preventing full flattening.\n\nOur work introduces dual-arm, multi-primitive policy to quickly unfold crumpled garments with standardization, improving downstream tasks like folding, ironing, and packing. Unlike their methods, our model trains without human-collected data in simulations and achieves zero-shot transfer, delivering strong real-world performance.\n\n## References\n\n[1] Y. Avigal et al. Speedfolding: Learning efficient bimanual folding of garments. IROS 2022.  \n[2] H. Xue et al. Unifolding: towards sample-efficient, scalable, and generalizable robotic garment folding. CoRL 2023.", "claims_and_evidence": "This paper claims four main things: (1) that combining fling and pick-and-place yields more efficient and accurate garment flattening, (2) that a novel factorized reward function leads to superior performance, and (3) that standardization (i.e., aligning shape/orientation) meaningfully benefits downstream tasks such as folding. Across their experiments, these claims appear largely substantiated. First, their method clearly improves coverage and IoU metrics relative to baselines like single-arm pick-and-place or exclusively fling-based approaches. The factorized reward containing coverage, IoU, and keypoint distance likewise shows consistent gains in shaping the final garment state. Ablation studies confirm that omitting parts of the reward (e.g., excluding IoU or ignoring keypoint alignment) harms performance.", "methods_and_evaluation_criteria": "The evaluation criteria include coverage, IoU, and keypoint distance; these are standard evaluation metrics in the garment manipulation literature, so they make sense.", "theoretical_claims": "This is not a theory paper.", "experimental_designs_or_analyses": "The experiments are structured around evaluating coverage, IoU, and keypoint distance after each rollout, alongside real robot demonstrations. The environment in simulation uses the SoftGym framework with PyFleX, which is a well-established simulator for deformable objects. The tasks are repeated with multiple random initial configurations, and baseline comparisons are made with relevant prior works (including single-arm pick-and-place, a standard fling-based method, and a state-of-the-art cloth manipulation baseline).", "relation_to_broader_scientific_literature": "This works extends a rich body of literature on robot garment folding. The system presented in this work is more \"complete\" and effective than prior well-known works in the literature, such as flingbot.", "essential_references_not_discussed": "All essential related works are discussed to the best of my knowledge.", "other_strengths_and_weaknesses": "Weaknesses:\n1. While real-world tests are present, the paper might have benefited from more quantitative comparisons across different cloth weights or textures to fully test fling reliability.\n2. The method’s reliance on overhead segmentation and keypoint detection might fail if the garment color is too close to the table or if extreme wrinkles obscure important garment keypoints. I'd like to see the method being stress tested under more extreme visual conditions. \n3. Given that recent works have demonstrated competent garment folding capabilities from pure end-to-end approaches (e.g., pi-0), I think some discussions on these approaches are warranted.", "other_comments_or_suggestions": "N/A.", "questions_for_authors": "See my comments above.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "twCsU8TCZ8", "reviewer": "Reviewer_U7iv", "summary": "This paper introduce a novel robotic garment manipulation system with standardization, which has better performance than the previous framework in this challenging robotics task.", "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": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewers for their insightful feedback and for recognizing the novelty of our standardized robotic garment manipulation system. Below, we address the key concerns raised, particularly regarding generalizability, the simplicity of the folding strategy, and evaluation metrics.\n\n### 1. Concern About Predefined Information and Generalizability\n\nRegarding target bounding edges：  \nWe agree that target bounding edges are used in our method, but their role is to act as an alignment metric for garment standardization. Once the model is trained, target bounding edges are no longer needed.\n\nAdditionally, garments can be simply categorized into common types (e.g., skirts, pants, long sleeves), which share similar features within the same category. In simulation, garments are modeled as grids of particles, and the ground truth positions are accessible, allowing for easy computation of bounding edges. Thus, manual annotation of boundaries is not needed for different garment types. Our model, trained in simulation, generalizes across these categories, as demonstrated in our experiments on pants and skirts (see Figure 8).\n\nRegarding predefined skills:  \nSelecting the appropriate action primitive is crucial in robotic manipulation, with pick-and-place being commonly used for rigid objects. For clothes, we designed two primitive actions (fling and pick-and-place) that enable rapid flattening and standardization. We tested these skills on various garment types (e.g., skirts, pants, long sleeves) without any additional parameter adjustments, and the model adapted well — demonstrating that these skills do not impact generalization.\n\n### 2. Keypoint-Based Strategy and Generalizability\n\nOur keypoint-based reward serves as an optional metric to enhance garment feature visibility (e.g., collar/sleeve alignment) during standardized unfolding, but is not fundamental to the core method. The keypoints are obtained directly from simulation without requiring trained detectors, preserving generalizability across garment categories during training.\n\nThe core contribution of our work is standardized unfolding, which benefits downstream tasks like folding, ironing, and packing. To evaluate its advantages, we used a keypoint-based folding task. However, this approach may face limitations in terms of generalizability to unseen garment categories due to structural variations in garments. Our future research will focus on adapting folding models to generalize across categories.\n\nFurthermore, even without the keypoint-based reward metric, our method performs well (see ablation study, Table 4), with generalization limits applying only to folding validation, not to unfolding.\n\n### 3. Simplicity of the Folding Strategy\n\nAs explained in Question 2, folding is not the main focus of our research; it is a validation task. To this end, we adopted a simple keypoint-based folding strategy, which is sufficient to showcase the advantages of our standardized unfolding. We believe this choice strikes a balance between simplicity and functionality for the purpose of validation.\n\n### 4. Evaluation of Folding Success\n\nIn simulation:  \nThe cloth is modeled as a grid of particles, and we can access the ground truth positions of these particles. We use the mean particle distance between the cloth states achieved and the desired target state. If the average particle distance error is less than 0.03, we consider the folding to be successful.\n\nIn the real world:  \nWe evaluate performance quantitatively using the Mean Intersection over Union (MIoU) between the cloth masks achieved and the human demonstrator. If the MIoU exceeds 0.8, we consider the folding to be successful. We will clarify these evaluation metrics in the revised manuscript.\n\n### 5. Figure Quality and Presentation\n\nWe appreciate the feedback on the figure. We have simplified Figures 2 and 3 and enlarged the text for better clarity.  \nThe details of the changes are in the following link:  https://github.com/hellohaia/img/blob/main/1.pdf", "claims_and_evidence": "1. The standarization for the garment manipulation task can enhance performance.\n\n2. However, the standarization will restrict the generalizability. Also, it's hard to set up various standarization for a lot of different kinds of clothes in the real world.\n\n3. Detecting keypoints and selecting corresponding predefined skills can be helpful for resolving this question. However, it still restrict the generalizability.", "methods_and_evaluation_criteria": "1. Using predefined unfolding region and manipulation skills as the standardization\n\n2. Using proposed APS-Net to detect the fold or unfold and detect keypoints.\n\n3. Using IoU, better coverage, and keypoint distance as input to select the correct keypoints and predefined actions.\n\nThis framework uses a lot of predefined information, which are target bounding edges and skills. It will reduce the generalizablity, and it's hard to define everything for different kinds of clothes.\n\nThe folding strategy seens very simple. It's not convincing that only initial keypoint is enough for robot to accomplish dynamic motions, where the motion of the deformable object has various uncertainty. More analysis to show that only keypoint-based strategy is enough is helpful.", "theoretical_claims": "Not a theoretical paper", "experimental_designs_or_analyses": "Conducting comprehensive experiments in both simulation and real world, for both folding and unfolding.\n\nIt's not clear how to evaluate the folding is success or not. It might be subjective？", "relation_to_broader_scientific_literature": "No", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "This framework uses a lot of predefined information, which are target bounding edges and skills. It will reduce the generalizablity, and it's hard to define everything for different kinds of clothes.", "other_comments_or_suggestions": "The figure is too large and contains too many details... The words are too small, and it's almost impossible to see anything without zooming in on the page", "questions_for_authors": "No", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TMEkHs6INf", "reviewer": "Reviewer_HhgQ", "summary": "In this paper, the authors present an RL framework for grament manipulation, which consists of two stages: standardization and folding. For the standardization stage, a two-primitive polcy of fling and pick-and-place is trained using a factorized reward function, which includes garment converage, keypoint distance and IOU. After standardization, folding is performed using a keypoint detection-based method. Two real-world datasets are collected to improve the real-world performance.\n\nThe authors compare their method with four baseline methods in simulation, and their method achieves SOTA in three metrics. The authors further conduct ablation studies to verify the effectiveness of each module. And they condut unfolding and folding experiments in the real world. Their method outperforms other methods in terms of success rate.", "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": "", "was_revised": false, "final_justification": "", "rebuttal": "We truly appreciate the time and effort you invested in reviewing our paper. Thank you for recognizing the effectiveness of our two-stage RL framework for garment manipulation, particularly the learning-based primitive selection and factorized reward design. Below, we respond to your comments point-by-point:\n\n### 1. Sim-to-real gap\n\nOur method minimizes this gap by incorporating several strategies to enhance simulation realism.  \nFirstly, we use RGBD images rather than RGB alone, as the combination of color and depth provides a more accurate representation of the garment's position and shape. Depth data enhances spatial alignment, reducing the sim-to-real gap and improving real-world transferability.  \n\nSecondly, in simulation, we use SoftGym to model cloth dynamics, with Blender 3D rendering realistic cloth colors based on HSV values sampled from real-world materials. The cloth's mass (ranging from 0.2 kg to 2.0 kg) and internal stiffness (0.85 kg/s² to 0.95 kg/s²) are varied to reflect real-world cloth properties.  \n\nThirdly, we use procedurally generated normal maps to simulate wrinkles and surface details, further enhancing simulation realism.  \n\nThese strategies—RGBD images, realistic cloth properties, detailed surface textures—help minimize the sim-to-real gap, ensuring effective transfer to real-world garment manipulation.\n\n### 2. Layout of the Figures\n\nThank you for your feedback on the figure organization. We recognize the importance of a clear and structured presentation and will revise the layout in updated version to improve the flow and organization of the figures.\n\n### 3. Definition of  $R_c$, $R_I$, and $R_K$ in Equation (10):\n\nThe terms $R_c$, $R_I$, and $R_K$ in Eq. (10) represent the different components of the factorized reward function, and their definitions can be found in Appendix A. We will update the revised version to clearly show where these definitions are located.\n\n### 4. Clarification of the innovative\n\nWe sincerely appreciate the reviewer's thoughtful feedback and recognition of our method's effectiveness. Below, we clarify the innovative aspects of our approach.  \n\nExisting garment unfolding methods rely primarily on learning-based static pick-and-place strategies [1][2], requiring numerous interactions, or dual-arm approaches [3][4] maximizing coverage without standardizing garment orientation and shape—key for downstream tasks such as folding and packing. Motivated by extensive literature review and practical needs, our goal is not only to rapidly unfold garments, but to standardize their final pose.  \n\nTo achieve this, our proposed dual-arm, multi-primitive policy combines dynamic fling actions for efficient unfolding with precise pick-and-place operations for standardization. To effectively train this policy, we introduce a novel factorized reward function (Cov, KD, IoU metrics) and a specialized multi-encoder-decoder architecture that intelligently selects primitives based on garment state.\n  \nMoreover, we incorporate a Spatial Action Mask (Section 3.4, Appendix B) to filter infeasible actions and an Action Optimization Module, enhancing fling point selection effectiveness, validated through extensive ablation studies (see Table 4).  \n\nBy integrating these innovations, our approach effectively generalizes across various garment types (pants, skirts, long sleeves) in Table 2. Further downstream folding experiments confirm that our standardized poses significantly improve performance (see Table 6). Finally, extensive real-world experiments on a dual-UR5 robot validate the robustness and practical applicability of our approach.\n\n### References\n\n[1] Lin X, Wang Y, Huang Z, et al. Learning visible connectivity dynamics for cloth smoothing. CoRL 2022.  \n[2] Wu R, Ning C, Dong H. Learning foresightful dense visual affordance for deformable object manipulation. ICCV 2023.  \n[3] Ha H, Song S. Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding. CoRL 2022.  \n[4] He C, Meng L, Sun Z, et al. Fabricfolding: Learning efficient fabric folding without expert demonstrations. Robotica 2024.", "claims_and_evidence": "Yes, the effectivness of their method and each module are verified through experiments and ablation studies.", "methods_and_evaluation_criteria": "The main novelty of this method is the learning-based primitive selection and the factorized reward function. It can be considered as a technical improvement over existing pipeline, while there is no innovative contribution. And the performances in simulation and realworld show the effectiveness of their method.\n\nThe experiments are extensive and the criterias are widely-used metrics to evaluate the performances.", "theoretical_claims": "NO theoretical claims", "experimental_designs_or_analyses": "The experimental designs are good and extensive. And the analysises verify the effectivenss of their method.", "relation_to_broader_scientific_literature": "NA", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "The key difference between the proposed method and existing methods needs further discussion and explaination. For example, it is claimed in the related work that ``their sim-to-real gap limits real-world applicability\", but it is unclear how the proposed method addreeses the domain gap in the standarization stage.", "other_comments_or_suggestions": "The layout of the figures could benefit from improvement. Currently, it is disorganized and makes it difficult to follow the logical flow.", "questions_for_authors": "What is the defination of R_c, R_I and R_K in Eq(10)?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JrbWY59IBq", "reviewer": "Reviewer_BU25", "summary": "The authors present APS-Net, a unified framework for garment manipulation that integrates both unfolding and standardization. APS-Net employs a dual-arm, multi-primitive policy to unfold crumpled garments and ensure standardization, which facilitates downstream tasks like folding. Experimental results show that APS-Net outperforms baselines in both simulation and real-world tasks.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your thoughtful feedback, insightful questions, and recognition of our work’s strengths. Below we address the comments point-by-point:\n\n## Weaknesses\n\n### 1. Framework Architecture Complexity\n\nWe understand the concern about APS-Net's complexity. However, it is specifically designed for the factorized reward function, with each component essential for guiding fling and p&p actions in garment standardization. Below, we explain its necessity and novelty.\n  \nThe three encoders (coverage, IoU, and keypoint distance) directly target the core challenges in garment manipulation:  \n- Coverage Encoder: Guides the fling action to quickly unfold crumpled garments.\n- IoU Encoder: Guides p&p action fine-grained adjustments near-flattened garments for consistent shape and orientation.\n- Keypoint Encoder: Ensures visibility of key garment features (e.g., collar, sleeve, hem)  \n\nRemoving any encoder causes a performance drop, as shown in ablation studies (Table 4). Thus, this design is not arbitrary, but a novel architecture tailored to garment unfolding.  \n\nTo further validate our design, we conducted experiments with an unfactorized network structure (a single-encoder variant with two decoders (fling and p&p) and a weighted sum of the same metrics), resulting in poorer performance (see new Table 4.1).\n\nTable 4.1 Unfactorized network structure\n| Method       | COV (%) | IOU (%) | KD     |\n|--------------|---------|---------|--------|\n| Unfactorized | 87.5    | 73.7    | 1.995 |\n|||\n\n### 3. Clarifying Metric Usage\n\nWe used the same reward function for FlingBot and P&P baseline as ours, with results shown in Table 1 under \"S-Fling\" and \"P&P.\" We also compare this with Flingbot's original method, which used only coverage as the reward. These comparisons ensure a fair evaluation.\n\n## Questions\n\n### 1. Undefined Variable (k)\n\nThe variable k was a typo; the correct variable is m (m ∈ {fling, p&p}). This will be corrected in the revision.\n\n### 2.Spatial Action Map Shapes in Figure 15\n\nThe shape differences in spatial action maps arise from applying a spatial action mask (SAM), detailed in Section 3.4 and Appendix B. \n \nFor dual-arm fling, SAM filters infeasible actions, such as regions with potential arm collisions or where only one arm can grasp the garment. For example, Figures 13(a) and 13(b) show the original garment and corresponding mask, while 13(d) shows the mask applied, excluding infeasible regions. Therefore, the valid mask does not match the garment's original shape. \n \nFor single-arm p&p, collision avoidance is unnecessary, so the mask is aligned to the shape of the garment (see Figure 13(e)).\n\n### 3.Dimension Gap and Meaning of θ\n\nThe value maps are 3D, with the third dimension representing the number of transformations $T_n$ (scaling and rotation). The correct formulation for equation (8) is:  \n$$  \n⟨x, y, i⟩ ← APSNet(T_n) = argmax(V_{f_{\\max}}^{\\max})  \n$$\nOnce i is determined, the values of w and θ are retrieved from the i-th entry of $T_n$:  \n$$\nw, θ = T_n[i]  \n$$\n\nBased on the above reasoning, we obtain ⟨x, y, θ, w⟩.\n  \nAs for θ, it is indeed discrete. In Appendix E, we mention that θ undergoes 16 rotations, covering 360°. \n\n### 4.Determination of Place Points\n\nBased on the definition (see Figure 1), for single-arm p&p, both the grasp and place points need to be predicted. As defined in Equation (9) (when m = p&p), the grasp point is (x, y), and the place point is (x − w, y).  \n\nFor dual-arm fling, only two grasp points are predicted, while place points follow a predefined swinging trajectory (Equation 4.1). As defined in Equation (9) (when m = fling), the grasp points are (x + w, y) and (x − w, y). During execution, both arms follow the trajectory (lift, forward/backward motion, place), adjusting acceleration and velocity at each stage to complete the fling motion.\n\n$$\nfling = [(0,0,h_l) \\to (0,f,h_l) \\to (0,f-b,h_l) \\to (0,f,h_p)]  \\quad \\text{4.1}\t\n$$\n\nwhere $h_l$ is the lift height, $f_m$ is the forward swing distance, $b_m$ is the backward swing range, and $h_p$ is the placement height.\n\n### 5.Clarifying Midpoint in Equation (9)\n\nFor dual-arm fling, directly predicting both grasp points is challenging due to collision avoidance. Instead, we reframe the problem by predicting the midpoint (x, y), angle θ (rotation), and grasp width w, with collision avoidance achieved by adjusting w. \n \nFor single-arm p&p, collision avoidance is not required. However, to maintain consistency with dual-arm fling and provide a unified output from the same network, we define (x, y) as the pick point, while θ and w represent the rotation and length between the pick and place points.  \n\nTherefore, we generalize the two cases as solving for ⟨x, y, θ, w⟩, and determine the operation points for two action primitives through Equation (9).\n\n## Suggestions\n\n### Space missing\n\nWe will correct this in the revised manuscript and will be more attentive to formatting details in the future.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "See Other Strengths And Weaknesses.", "relation_to_broader_scientific_literature": "Compared to previous works, APS-Net integrates both garment unfolding and standardization into a unified framework.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths \n\n1. The authors conduct real-world experiments and provide detailed analysis for experiments.\n\n2. APS-Net introduces a dual-arm, multi-primitive policy and integrates garment unfolding and standardization into a unified framework, offering notable improvement over prior work such as P&P and FlingBot.\n\n\nWeaknesses\n\n1. The proposed approach is straight-forward and complicated. The framework is designed around three metrics (coverage, IoU, keypoint distance), with a corresponding three-encoder, six-decoder architecture (each encoder is paired with both fling and P&P decoders). The contribution and novelty in the pipeline architecture may be limited.\n\n2. While the Introduction is well-written and easy to follow, the Methods section could be improved for clarity, as some details are confusing (see Questions).\n\n3. The same metrics (coverage, IoU, keypoint distance) used in the experiments are also employed during the training of APS-Net. It is unclear whether these metrics are also used in the training of other baseline methods. If not, the comparison might be unfair.", "other_comments_or_suggestions": "In Line 262, a space is missing after the period.", "questions_for_authors": "1. What does $k$ represent? The paper does not define this variable.\n2. Figure 15: In Step1 and Step2, why do the shapes of the spatial action maps in the first row not match the shapes in the second row?\n3. The text and equations suggest that value maps are the aggregations of scores for each $<x, y, \\theta, w>$, implying a high-dimensional representation (dimensionality > 3). However, the visualized value maps in Figure 2 appear to be three-dimensional (xyz space), which is confusing. Could the authors provide some clarifications? Additionally, should θ be discrete? This is not explicitly mentioned how $theta$ is handled.\n4. Section3.2 explains how the grasp points are generated, but does not explain how place points are determined.\n5. The concept of 'midpoint (x, y) between two grasp points' is used for pick&place action (in Euqation9), however, it seems only one arm is used for pick&place action, so it is unclear what the midpoint represents in this context.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents a practical approach to garment manipulation that combines unfolding and standardization in a unified framework.
The proposed approach employs a dual-arm, multi-primitive policy with dynamic fling to unfold and align garments. 
The reviewers agree that this paper is well-written, and the proposed method is thoroughly evaluated in challenging real-world scenarios. The reviewers also find the novelty somewhat limited, but this is due to the nature of this work (a robotic/vision system). The area chair finds the paper very well-written and presented. The experiments are convincing.
Decision: Accept (poster)