DexMove: Learning Tactile-Guided Non-Prehensile Manipulation with Dexterous Hands
Authors: Pei Lin
Yuzhe Huang
Wanlin Li
Chenxi Xiao
Ziyuan Jiao
Venue: ICLR 2026 Poster (2026)
Keywords: tactile
robotics
dexterous hand
manipulation
Primary area: applications to robotics, autonomy, planning

Abstract: Non-prehensile manipulation offers a robust alternative to traditional pick-and-place methods for object repositioning. However, learning such skills with dexterous, multi-fingered hands remains largely unexplored, leaving their potential for stable and efficient manipulation underutilized. Progress has been limited by the lack of large-scale, contact-aware non-prehensile datasets for dexterous hands and the absence of wrist–finger control policies. To bridge these gaps, we present DexMove, a tactile-guided non-prehensile manipulation framework for dexterous hands. DexMove combines a scalable simulation pipeline that generates physically plausible wrist–finger trajectories with a wearable device, which captures multi-finger contact data from human demonstrations using vision-based tactile sensors. Using these data, we train a flow-based policy that enables real-time, synergistic wrist–finger control for robust non-prehensile manipulation of diverse tabletop objects. In real-world experiments, DexMove successfully manipulated six objects of varying shapes and materials, achieving a 77.8\% success rate. Our method outperforms ablated baselines by 36.6\% and improves efficiency by nearly 300\%. Furthermore, the learned policy generalizes to language-conditioned, long-horizon tasks such as object sorting and desktop tidying.

Published as a conference paper at ICLR 2026
DEXMOVE:
LEARNING TACTILE-GUIDED NON-PREHENSILE MANIPULATION WITH DEXTEROUS HANDS
Pei Lin .12, Yuzhe Huang" -2, Wanlin Li Chenxi Xiao i1, Ziyuan Jiao 12
"School of Information Science and Technology, ShanghaiTech University
"Beijing Institute for General Artificial Intelligence BBehhang University
(linpei2024, xiaochx| (Oshangaaitech... edu.
jiaoziyuan@bigi.. ai
ABSTRACT
Non-prehensile manipulation offers robust alternative traditional pick-and- place methods for object repositioning. However, learning such skills with dex- terous, multi-fingered hands remains largely unexplored, leaving their potential for stable and efficient manipulation underutilized. Progress has been limited by the lack of large-scale, contact-aware non-prehensile datasets for dexterous hands and the absence of wrist-finger control policies. To bridge these gaps, we present DexMove, a tactile-guided non-prehensile manipulation framework for dexterous hands. DexMove combines scalable simulation pipeline that gener- ates physically plausible wrist-finger trajectories with a wearable device, which captures multi-finger contact data from human demonstrations using vision-base tactile sensors. Using these data, we train flow-based policy that enables real- time, synergistic wrist-finger control for robust non-prehensile manipulation of diverse tabletop objects. In real-world experiments, DexMove successfully ma- nipulated six objects of varying shapes and materials, achieving a 77.8% suc- cess rate. Our method outperforms ablated baselines by 36.6% and improves effi- ciency by nearly 300%. Furthermore, the learned policy generalizes to language- conditioned, long- horizoo tasks such as object sorting and desktop tidying. Project page: https: https://ptiiiiiiii /ttp://pp/nnn/ieieeeeeeeeeeiiiit 66 666.666666666 g thub. io pro projects. /DxMovee
Trajectory Force Data Capture
Past States
Generalization Across Diverse Dynamic Properties
Non-PPehenssle
Diverse Object Geometry
Trajecroty Dataset
DexMove Policy
Complex Surface Friction
Trajectory Learning
Human Demonstrated
Force
Tactile- Force
Learning
Tefo-Net
Dataset
Next State
Human- Human-Machuig Machine Colloboration
Longuage Driven
Tidy Up Desk
Action
Figure 1: Overview of DexMove. The framework integrates synthetic non- prehensile manipulation trajectories and human- demonstreeddttrtee tactile data to train fow-matching policy for dexterous hands. The learned policy generalizes across diverse objects, surface frictions, and various language-condttooned tasks such tidying.
INTRODUCTION
Non- Non- pen-prehensile manipulation a fundamental skill that humans routinely employ to interact with their surroundings (Lyu et al., 2025; Cho et al., 2024; Li et al., 2025b). Unlike grasping, it in-
Equal contribution. Corresponding authors.

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Published as a conference paper at ICLR 2026
duces object motion without lifting the object. This capability is particularly valuable for handling items that are large, heavy. fragile, or geometrically unsuitable for grasping, thereby circumventing the stability and control challenges of full pick-and-place cycles Li et al. (2025a). Consequently, non-prehensile manipulation expands robotic dexterity and applicability of dexterous hands across broader settings, making it problem worthy of deeper investigation in robotic research.
Given this potential, non-prehensile manipulation has attracted considerable attention in recent years. Most prior work has focused on pushing or pressing objects with robot grippers or rods (Lyu et al., 2025; Cho et al., 2024; Zhou et al., 2023; Suresh et al., 2021). In contrast, the use of dexterous, multi-fingered hands in this context remains largely unexplored (Li et al., 2025b; Wang et al., 2025). Our key insight is that dexterous hands are inherently well-suited for non- non-prehensile tasks, as they can establish multiple distributed contacts, providing greater stability than a two-finger gripper or pushing rod. This multi-contact capability not only broadens non-prehensile manipulation to thin, cylindrical, and round objects whose dynamics are less predictable during pushing (Lyyu et al., 2025; Cho et al., 2024), but also enhances efficiency through coordinated finger motions.
Despite recent advances, deploying dexterous hands for non-prehensile manipulation still faces two key challenges. First, learning generalizable strategies requires large, physically plausible datasets covering variations in object geometry, mass distribution, and surface properties (Lin et al., 2024; Zhu et al., 2024). However, such datasets are not yet available. Acquiring them via teleoperation is limited by inefficiency and compromised force fidelity due to the absence of high-fidelity haptic feedback. Purely simulation-based synthesis is also challenging because of domain gaps, particu- larly in tactile perception. Second, multi-contact manipulation generates coupled forces and motions across fingers through hand-object dynamics. However, current research lacks a whole-hand motion planner for coordinating multi-contact interactions. Together, the scarcity of scalable, high-fidelity datasets and the absence of force-aware, qulti-contace coordination policies hinder progress toward generalizable dexterous non -reeheneee manipulation.
To address these challenges, we propose DexMove, a framework for dexterous non- non-prehensile ma- nipulation (Fig. 1). First, to overcome the difficulty of scaling multi-contact interaction data, we build large-scale simulation pipeline that synthesizes diverse, force-aware wrist- finger trajectories across objects with varying geometry, friction, and mass distribution. Second, to exploit tactile in- formation, we develop a wearable system with vision-based tactile sensors that captures fingertip force distributions from human demonstrations. The tactile knowledge is distilled into TaFo-Net, network that learns a spatiotemporal inter- inter-finger force representation from human demonstrations. Third, seamlessly bridge the trajectory in simulation and tactile data from the real domain, we introduce the DexMove-Policy a flow-matching network that learns synergistic, tactile-based coor- dination strategies for jointly controlling the wrist and fingers. The significance of DexMove lies in three key aspects: (i) the first non-prehensile policy tailored for tactile dexterous hands, (ii) novel data synthesis paradigm that integrates large-scale trajectory simulation with limited human tactile demonstrations, enabling the incorporation of tactile data with minimal domain gaps. (iii) a novel wearable exoskeleton system with vision-based factile sensors.
The remainder of this paper is organized as follows. Sec. 2 reviews prior work on tactile-based non-prehensile manipulation. Sec. 3 details our hybrid data acquisition pipeline, which integrates simulation-bassed trajectory generation with tactile force data from human demonstrations. See. 4 presents the proposed learning framework, including the contact-establishmee Policy for jointly controlling the wrist and fingers to reposition the object, and actile-force planning network (TaFo-Net). Sec. 5 reports quantitative and qualitative experiments, including performance evaluation, ablation studies, and downstream applications. Finally, Sec. 6 summarizes our findings, discusses limitations, and outlines directions for future research.
policy, DexMove-
2
RELATED WORK
2.1 NON-PREHENSILE MANIPULATION
Non-prehensile manipulation involves interacting with objects without grasping (Oller et al., 2024; Yang & Posa, 2024; Lyu et al., 2025), enabling efficiencies beyond repeated pick-and-place cycles. Based on contact dynamics, it is commonly divided into single-contan and multi-contact strategies. Single-contact methods use tool end-effector to drive object motion; planar pushing (Mason, 1986; Stüber et al., 2020) is a canonical example where a rod translates or rotates an object. Later work (Chi et al., 2024; Ferrandis et al., 2024) extended this setting controlled contact breaking
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and reestablishment, enabling intermittent push sequences. However, single-contace manipulation is intrinsically less certain because lateral forces are weakly constrained, especially under mon-unifone friction uneven surfaces. Multi-contact manipulation instead distributes forces across multiple contact points, leading to more stable interactions. Prior work (Bhat et al., 2023) showed that even two-point contact can substantially improve stability over single-point strategies. Building this, we leverage the multiple fingers of a dexterous hand and integrate tactile sensing for closed-loop finger control, improving robustness and efficiency. Moreover, whereas prior approaches often focus on ad hoc object sets (Li et al., 2025b; Wang et al., 2025), our method generalizes non- von-prehensile manipulation unseen objects.
2.2 DATA COLLECTION FOR TACTILE-GUIDED MANIPULATION
Tactile sensing has been central to robotic manipulation (She et al., 2021; Do et al., 2023; Jin al., 2023; Qi et al., 2023; Huang et al., 2024; Zhu et al., 2025; Taylor et al., 2024; Higuera et al., 2025). To exploit tactile feedback, recent work increasingly relies on data-driven policy learning (Dong et al., 2021; Suresh et al., 2024; Sun et al., 2025), but scaling tactile data collection remains a key bottleneck. Although simulation can generate data, it is computationally costly due soft-body dynamics and often suffers from sim-to-real gaps (Si & Yuan, 2022).
Teleoperation offers a domain gap-free alternative, yet most systems focus on parallel-jaw grip- pers (Wu et al., 2025a). Dexterous multi-fingered hands are substantially harder to operate with- out haptic feedback, causing greater signal variability and lower success rates (Zhang al., 2025). To address this, exoskeleton-basee teleoperation has been explored (Yang, 2025; Zhang et al., 2025; Fang et al., 2025), providing real-time haptics but requiring labor-intensive setups and restrict- ing natural motion, which limits scalability. Another direction is collecting tactile data directly from humans: tactile gloves (Liu et al., 2024; Jiang et al., 2024; Xing et al., 2025) sense distributed hand pressure, but mismatched sensor layouts versus robot hands introduce domain gaps. More recently, isomorphic sensor designs (Zhu et al., 2025; Wu et al., 2025b) enable portable, domain-gan saaninn----aae gap-freee tactile capture while preserving natural hand motion.
Motivated by these developments, we propose a hybrid acquisition paradigm that combines simula- tion scale with real- world fidelity. We generate large-scal scale motion data in simulation and augment it with real tactile measurements to compensate for limited simulated tactile realism. On the hardware side, an exoskeleton interface transfers human interactions robotic hands equipped with isomor- phic sensors, enabling policies to benefit from both data sources in a complementary way.
DATA ACQUISITION FOR NON-PREHENSILE MANIPULATION
This section introduces a hybrid data synthesis pipeline that incorporates tactile information into the manipulation process. In See. 3.1, we present an optimization procedure that generates multi- finger motion trajectories across diverse grasp poses and force levels to guide objects toward target poses. In Sec. 3.2, we complement this with human demonstrations to capture how contact forces are modulated during manipulation. Both datasets support policy training in Sec. 4.
3.1 TRAJECTORY SYNTHESIS
3.1.1 HAND-OBJECT CONTACT ESTABLISHMENT
Non-prehensile manipulation begins by establishing an initial hand-object contact. To generate di- verse contact poses, we uniformly sample candidate wrist poses, each defined by rotation R. e R3x3 and translation Terist E R following Yang et al. (2021) and subscript indicates the initial frame of the entire manipulation sequence. Then, a displacement vector from each fingertip to its nearest surface point is computed, and the fingertip is translated along d until contact. To promote diversity, Gaussian noise perturbs the direction, yielding d = d+ Given the fingertips' positions PUP e 3 the finger joints' angles Aland
E R denotes the number of joints) is synthesized
by solving:
A hand arg min Abuud
[FK(Ab hand
R wrist Twiitt
P
[[2 + Wpinch Eregion:
I Tregion
|PP
a where d TIP (P -PDIP)
(2)
where FK is the forward kinematic function, which can output positions of fingertips, and Liegion encourages contacts within the tactile sensor's 's effective region. TIP denotes the orientation vector of fingertip from the DIP joint position DDIP to the fingertip joint position !!IPP
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Tactile
Trajectory force Trajectory wo force Force
Fingertip joint
HEJ
Sample & Filtering
(a) Generate Grasping Pose
(b) Non-prehensile Trajectory
(c) Trajectory with Force
Figure 2: Trajectory data verification and force augmentation. (a.b) After generating initial grasp poses. the set of target directions along which the object fingertips' trajectorics computed by sampling target object poses. (c) The contact force is synthesized as surrogate for the finger's penetration depth into the object, and we augment the trajectories with diverse forces.
be manipulated is pruned using physics simulation, and the
To build a diverse dataset of hand-object contacts, we use 88 objects from the YCB dataset (Calli al., 2015), which then randomly scaled and rotated to produce 352 object layout instances. For each instance, we generate 1,024-2.048 candidate poses. After discarding poses with self-collisions or penetrations, a total of 412k valid configurations remain.
3.1.2
SYNTHESIZE FORCE-CONDIEN CONDITOONDD TRAJECTORIES
After establishing initial contacts, the object is manipulated toward a target pose through coordinated wrist and finger motions which are parameterized by A hand Rwisst and Twiss These motions induce object movement with DoFs: x/ axis, and yaw rotation. While such variables could be optimized using shooting methods (e.g., iIQQR (Li Todorov, 2004)), these approaches are computationally expensive and often yield uncertain trajectories due unmodeled physical effects. To address these challenges, we employ rejection-sampling strategy for trajectory synthesis.
We simulate the repositioning process in MuJoCo (Todorov et al., 2012) by incrementally translating the hand along random directions. If all fingertips maintain stable contact after displacement of 50 em, the direction is accepted as feasible. From the resulting admissible set, generate data by uniformly sampling target object position Puyy Target R and yaw warget E IR as the target pose.
Under the non-slip assumption, each fingertip's trajectory is computed by rigidly transforming its initial relative contact offset with respect to the object reference point:
PuP = R_ (P
-POb).
t=
where T is the length of the manipulation sequence, Pot denotes the initial position object, wi denotes the interpolated yaw angle from zero to Wirget at step t, R. (wi"h)) is the rotation matrix representing the object's yaw at step f, and pasi denotes the interpolated object translation from the initial origin to the target pose at step t.
To model the contact forces along these trajectories, we approximate the fingertip-ohject normal force G using the indentation depth measured by the vision-based tactile sensor:
G D sensor r diittacceeeeep" surface),
(4)
where r the fingertip radius and distance(-)) denotes the Euclidean distance between the fingertip joint position and the contact object surface. Deensor is the indentation depth of tactile sensor. As shown in Fig. 2, we further sample multiple force profiles by displacing each fingertip along its contact normal n:
TTIP - +i-N(0,,
(5)
which produces augmented fingertip trajectories with varying force magnitudes. The contact normal is computed the direction vector from the contact point to the fingertip T Given these updated fingertip positions, the corresponding joint and wrist configurations are recovered by solving an inverse kinematics (IK) problem with additional wrist-motion regularization.
Given these force- augmentd fingertip trajectories, the joint and wrist configurations are recovered by solving optimization-han based inverse kinematics problem:
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Thumb
Index
Middle
Ring
Market
Tactile
Sensor
Image
time
Depth Map
Human Demonstration
Tactile Marker Sequence
Figure 3: A wearable device for collecting real tactile-force data. The collected force during manipulation characterized by the displacement markers within vision-based tactile sensor.
X hand = (A hand R wrist Twisst
Ju:T
(6)
X hand
arrgmnn x FK(Xland) -
0:T la + wrise wrist
(7)
L, wrist = MSF (R. wrist R Rwriss) + M-MS wrist I wrist).
(8)
where MSE denotes the mean squared error. To bias the solution toward finger-driven manipulation, add L penalty wrist motion that reduces the likelihood of moving the outside its workspace. Finally, a filtering step further constrains the robot arm: in simulation, any trajectory that leaves the feasible workspace is discarded. We totally collected 2M sequences.
3.2 TACTILE FORCE FROM HUMAN DEMONSTRATIONS
The contact forces exerted by individual fingers are important contact-rich tasks but challenging to estimate in simulation due to two main challenges. First, high-fidelity object dynamics are diffi- cult to model uccurately. Second, our agid-body simulation framework is unable to generate tactile outputs, it lacks realistic soft-body contact modeling. To address these limitations, adopt demonstration-basee strategy that infers contact force from historical tactile observations. We de- veloped a wearable device with tactile sensors mounted human fingers (Fig. 3). The vision-based tactile sensor used here is an R-Tac sensor (Lin et al., 2025), augmented with additional visual markers that enable direct representation of interaction forces. The exoskeleton design allows easy mounting on human fingertips for data collection and subsequent attachment to the robot hand. Its isomorphic design to the robot hand helps minimize the domain gap caused by hardware differences, following the intuition in Fang et al. (2025). Further hardware details are provided in Appx. A.
During each trial, we recorded the tactile information as well as the object's target pose and real-time pose throughout the manipulation. The tactile data include the normal force magnitude G, estimated from the inferred indentation depth, and tangential (shear) forces derived from 2-D displacement of surface markers (represented as normalized direction together with a scalar magnitude). This results in tactile vector field 12 where v = 33 the number of markers (Fig. 3), Data were captured 30 FPS, yielding approximately 300k frames across 20 objects.
POLICIES FOR NON-PREHENSILE DEXTEROUS MANIPULATION
Next, we present our method for integrating kinematic trajectories with contact force planning leameed from human demonstrations. Specifically, we introduce three components of our framework: a policy for establishing contact, the DexM policy for jointly controlling the wrist and fingers to manipulate objects, and TaFo-Net for coordinating finger forces. Fach component is described in detail below.
4.1 ESTABLISH CONTACT
A Flowing Matching (FM) (Lipman et al., 2023: Liu al., 2022) policy is utilized infer an initial contact hand pose with the object (Fig. 4 (left)). Compared to diffusion policy (Chi et al., 2024), found FM offers faster training and inference speed. FM aims generate the hand state
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Establish Contact
DexMove-Policy
Planned Force
Past States
Global
Target
11:27
Linear
X
A
R
Poli langet Miaegee
FILM
positional
formr Encoder
polj
obj
Transformer Decoder
Velocity Field
X1
Linear
Matching
FILM
TaFo-Net
V-Tp
D
pabj
obj barges
pobj
w W
Cross Finger Attention
PointNet**
GL:Ty
FILM
Planned
Target token
Past State token
Tactile Vector Field token
Force
I:T
Finger
Mask
Loss
Transformer
1:T,
Decoder
Query
Figure 4: Pipeline for Manipulation. Establish Contact: a flow- How-matching model to predict contact hand posc by fusing the conditions. DexMove-Policy: a transformer-basee flow-matching network predicts future hand trajectory and states conditioned on force, past states, and global target. TaFo-Net: transformer decoder predicts tactile vector fields (i.e., forces) for DexMove-Policy.
(Ahand, hand Rristt Terist) conditioned on the object point cloud D and target object pose (Pabj fargets witurget
Using the target object pose as conditioning input, its goal is to generate hand poses capable of moving the object to the desired target. The FM policy learns time-dependent velocity field u(-) from intermediate samples:
X₁ = (1-
where t~ (0, 1]. Xo Xo I), and X2 is drawn from the distribution of ground-truth samples. The objective is:
Tecomaact = BB B - - (X,, condition)]{
so that the velocity field learns to generate feasible contact configurations by integrating the ODE from randomly sampled Xo to X₁, To allow for the acquisition of conditioned features, Point- Net++ (Qi et al., 2017) is used to extract features from the point cloud. These conditional features are incorporated into the latent representation of X, via FiLM (Perez et al., 2017). These details are illustrated in Fig. 4 (left) and further discussed in Appx. A.3.2.
4.2 TRAJECTORY LEARNING: DEXMOVE-POLICY
After establishing initial contact, the repositioning process is controlled by DexMove-Policy, goal- conditioned cow-matching model. Conditioned on the observed state history, the target object pose, and a desired force schedule, the model generates rollout of future hand states for execution. Specifically, the system state history is represented as:
phandd hand R wrist Twrist pobi nhj C, G
where Phand E Jx3 denotes joint positions, Fx3 denotes contact positions (each expressed in the fingertip's 's local frame), and G R² denotes the pressing force of each finger, inferred from sensor indentation during execution. Here, the number of fingers and -T, 0 the past Tp steps until now. In addition to historical observations, the network also receives desired finger forces from Sec. 4.3, denoted GI.T, Ty x X where T is the number of future frames to predict.
The network structure is illustrated in Fig. 4 (top-right). Historical states and the target object pose are fused via cross-attention. The fused data and force commands are passed to Transformer de- coder that predicts the flow-matching velocity field. The predictive sample X, consists of T, frames of future hand states:
X1 = (haand A hand
wrist Twiist
(10)
J J:TT""

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4.3 FORCE PLANNING: TAFO-NET
The trajectories discussed above conditioned the desired finger forces GI:T, E ERTxxP, In this section, we introduce TaFo-Net (Fig. 4 (bottom-right)), a network designed to predict desired forces.
Given the target object pose (Pobj
target Winree),, the past Tp frames of object states (P -Ty.:0 w -T,00)>
and per-finger tactile vector fields V VTT..0 T xXTxxFxxXXX TaFo-Net predicts the tactile vector fields for the next y frames, I:T, E I2' xxxxX Here, v is the number of markers per finger, F the number of fingers and C is the number of channels per marker. The predicted tactile vector fields are used to extract the per-finger pressing forces G1:7,, which serve as targets for the trajectory policy described earlier. Our key insight is that historical tactile vector fields encode environmental properties implicitly, such as surface friction and contact state. The history of object poses together with the target pose provides a distance-to-ge signal, from which future actions be inferred.
To capture spatial-temporal inter-finger interactions, TaFo-Net has three stages:
(i) Per-finger spatial encoding. For each time t and finger the tactile vector field V f. E IR "x is encoded into a finger token Ut, via a lightweight transformer, enriched with learnable and geometry-informed marker positional embeddings.
(ii) Cross-finger attention. At each frame i, we form the set {UL,,...,,U, and apply multi- head self-attention across fingers (CF), augmented with finger-type embeddings {g, ETR)). This produces cross fonger enhanced tokens Ü., E ER,, denoted as: U. (F.
(ii) Finger-wise causal temporal attention. After merging the tactile vector fields with the target and past states, we apply finger-wise causal mask to the queries so that query at can attend only to tokens from times S i (across all fingers), preventing information leakage from the future and enabling goal-conditioned, temporally consistent, and cross-finger consistent reasoning.
To enhance robustness, we randomly drop out time steps, fingers, and tactile markers during training. Finally, the model is trained by minimizing reconstruction loss:
Ty
F
Cree
Viy-Veli 11
(1I)
EXPERIMENT
In this section, we present comprehensive experiments to evaluate the proposed DexMove pipeline. First, we compare our system with other non-prehensile manipulation methods in terms of task per- formance. Next, we conduct ablation studies to examine the contribution of each technical module as well as system robustness. Finally, we demonstrate downstream applications enabled by our ap- proach. Our hardware settings and implementation details can be found at Appx. A.3.
5.1 PERFORMANCE BENCHMARK
To the best of our knowledge, non orechnneee manipulation with tactile dexterous hand remains expereex,lored. and no publicly available baselines exist. We therefore benchmark our approach against the following baselines: (i) an open-loop replay policy, and (ii) two gripper-based learning methods, CORN from Cho et al. (2024) and DyWA from Lyu et al. (2025). All evaluations are conducted a desktop under two friction conditions: clean tabletop and with tape strips. We benchmarked six objects: a randomly assembled LEGO (Fig. 5 (a)), a keyboard, a mouse, book, a large cylindrical can, and a small cylindrical can. The selected objects span diverse masses, sizes, and geometries, as shown in Fig. 12. Other implementation details are illustrated in Appx. A.5.
We evaluate all baselines using the following metrics: (i) Success rate: a trial is successful if the terminal state relative to the target pose is within 10% error in both yaw angle and position, and accidental self-collision occurs; (ii) Efficiency: the time taken reach the terminal state. Each object is tested 30 trials, and each trial has identical initial and target poses for different methods.
5.1.1
COMPARISON ON SUCCESS RATE
The benchmarked success rates are reported in Tab. First, the Open-Loop baseline simply replays previously successful DexMove trajectory, yielding the lowest success rate among all policies; without feedback, it cannot handle movement errors that arise during manipulation. Second, the Dy WA and CORN baselines, which employ grippers for discrete-contact non-prehensile manipula-
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(a) Random object geometry
(b) Different surface friction m
(c) Object sorting
(d) "Put the grip of the electric drill into a person's s right hand.
(e) "Put the objects on the table back in their place."
(f 1) Deformable object (rag doll).
(f 2) Deformable object (tissues)
Figure 5: Demonstrations non-prehensile manipulation. Our Icarned policy robustly adapts to (a) objects with challenging geometries and (b) varying surface friction. addition, the learned skill supports reasoning tasks such as (c) object sorting, and linguage-guided repositioning tasks, including (d) non-prehensile handover and (e) tidying up objects desktop, (f) deformable objects (rag dolls and packet of tissues).
tion, also achieve lower success rates than our policy. Both DyWA and CORN predominantly fail by not achieving the desired rotations, particularly for cylindrical objects. We believe this due to dependence single contact point for repositioning. In contrast, the dexterous hand establishes stable, continuous, multi-surface contacts, enabling accurate rotation of everyday objects.
Furthermore, DexMove demonstrates robustness against non- man-umfform surface friction (Fric. A Fric. B, where Fric. B is unseen during data collection (Fig. 5 (b))). The performance gap between the two surface conditions is only marginal. By contrast, DyWA and CORN exhibit pronounced performance degradation, reflecting their sensitivity to unmodeled spatial friction variability.
Table 1: Success rate (%) of DexMove under different initial yaw angle Whiner (degrees).
w Wunget
obj lunget
30
obj 30 < Wargett < 60
obj < Wargett
< .00
Method
Fric. A
Fric.
Fric.
Fric.
Fric. A
Fric. B
Open-loop
36.7
10.0
23.3
0.0
3.3
0.0
DyWA (Lyu et al., 2025)
50.0
36.7
46.7
30.0
50.0
33.3
CORN (Cho et al., 2024)
43.3
36.7
46.7
40.0
43.3
43.3
DexMove (Ours)
86.7
86.7
80.0
83.3
70.0
60.0
5.1.2 COMPARISON ON EXECUTION EFFICIENCY
In addition to the success rate, we evaluate efficiency using the average completion time. Timing measured from the moment the hand or gripper first contacts the object until the object reaches the target pose within the defined success threshold. The results are reported in Tab. 2. Among all comparison groups, DexMove achieves average completion time less than half that of Dy WA and CORN, owing to its use of multi finger contact and the reduced number of action primitives to reach the target pose. These findings highlight DexMove highly efficient manipulation policy.
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Table 2: Average execution time (s) across different pushing distances, highlighting the efficiency of DexMove.
Method
0< P < 15 cm
15 < < 30 cm
30 < P < 45 cm
DyWA (Lyu et al., 2025) CORN (Cho et al., 2024)
36.1
52.2
60.6
41.4
54.5
62.1
DexMove (Ours)
8.3
10.9
12.4
5.2 ABLATION STUDY
We perform ablation studies quantify the contribution of each module. The environmental setting is the same as Sec. 5.1. The baselines we compared include: (1) Wrist-Only: using the robot wrist to automatically move objects via a policy, with all finger joints locked after initial contact. (2) Wrist- only" denotes the same setting, but with the wrist controlled through teleoperation. (3) w/o Cross- Finger: the cross-finger attention blocks TaFo-Net are removed. (4) w/o Shear Force: the tactile vector field excludes shear components, retaining only the normal component. (5) w Heuristic Force: we disable TaFo-Net and replace with a hand-crafted strategy (if slip is detected, incrementally increase the force by a fixed increment following Lin et al. (2025)). The implementation details are illustrated in Appx. A.5.
From the results in Tab. 3, we observed that DexMove achieved the highest success rate in most cases, underscoring the necessity of using a tactile dexterous hand with active finger control. In the Wrist-Only configuration, flat planar objects (book and keyboard) can be manipulated with high success rate. But rarely succeeds when object shape induces non-coplanar fingertip contacts where finger adjustments become necessary. Without the Cross-Finger module, TaFo-Net longer capture coordinated inter-finger constraints and therefore performs well only on flat, planar objects. When the Shear-Force module is ablated, the model collapses toward predicting smoothed (averaged) states; this remains workable for light objects (Lego and mouse) but fails on heavier objects because shear feedback (critical for slip detection) is absent. Without human's 's heuristic force from Tet, the hand-crafted strategy did not perform well on most of these tasks.
Table 3: Success rate (%) of ablated baselines across different objects.
Method
Lego
Mouse
Book
Keyboard
Large Can
Small Can
Wrist-Only 13.3
0.0
33.3
20.0
0.0
0.0
Wrist-Only
0.0
73.3
100.0
100.0
6.7
10.0
w/o Cross-Finger
13.3
3.3
63.3
50.0
0.0
3.3
w/o Shear-Force
70.0
66.7
33.3
13.3
0.0
0.0
Heuristic Force
36.7
43.3
66.7
0,0
0.0
0.0
DexMove (Ours)
66.7
86.7
90.0
90.0
63.3
70.0
5.3 DISCUSSIONS ON ROBUSTNESS DEXMOVE
To further assess the robustness of DEXMOVE, we examine its performance under more diverse con- ditions, including deformable objects, uneven surface and using markerless method to track objects.
Deformable Objects. We evaluated a rag doll and a tissue packet. Across 30 trials per object, the success rates were 96.7% and 100%, respectively. Qualitative results are shown in Fig. 5. These experiments suggest that the compliant nature of deformable objects can help stabilize contact for- mation and consequently achieving higher performance than previous rigid objects tested.
Uneven Surfaces. everyday scenarios, support sur- faces may be uneven non-continuous. To emulate such settings, we created an uneven surface by ran- domly stacking additional objects beneath the manip- ulated items. We conducted 30 trials each with three objects: book, large can, and LEGO brick. The scene and reported performances are shown in Fig. We evaluate under two conditions. (1) w/o finetune: the original policy weights are used without any retraining. (2) w/ finetune: collect 15 minutes of tactile data on
(b)
Lego
Book
Large Can
w/o finetune
36.7%
76.7%
20.0%
w finetune
53.3%
86.7%
66.7%
Figure 6: Evaluation uneven surface.
uneven surface to finetune TaFo-Net. Additionally, to simulate cases where the fingers momentarily lose contact with the object during manipulation, we mask intervals of the contact positions in the force-aware trajectory data when finetuning the MexMove policy.

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Table 4: Effect of tactile noise on force prediction emror (Err) and success rate of DexMove (SR).
Noise a Err-MSE (book)
SR-% % (book)
Err-MSE (large can) SR-9 % (large can)
0
0.0112
90.0%
0.0351
63.3%
0.05
0.0108
86.7%
0.0615
56.7%
0.1
0.0415
80.0%
0.1239
43,3%
0.2
0.1721
53.3%
0.3005
20.0%
0.4
0.3219
13.3%
0.5312
3.3%
Markerless object pose estimation. Our ap- proach does not necessarily require ArUco markers. To demonstrate this, integrate FoundationPoss: (Wen et al., 2024) as pose estimator, under single-camera setting with markerless objects. Qualitative results are shown in Fig. Our system achieves success rates of 16.7%. 13.3%, 93.3%, 96.7%. 60.0%, and 76.6% to manipulate the six objects evaluated in Table 3, indicating that it remains effective under single-camera and markerless setup. However, hand-object occlusions can degrade pose estimation accuracy especially for small objects. This limitation is widely recognized in the vision community and is not specific our system.
Figure 7: Visualization of markerless object pose estima- tion.
5.4 SENSITIVITY OF SYSTEM To TACTILE NOISE
We also evaluate the influence of tactile noise and the prediction errors of predicted force, which may arise when the tactile sensor is poorly calibrated or when TaFo-Net introduces prediction in- accuracies. To emulate such cases, we add Gaussian noise e ~ (M a²) of varying magnitudes into each channel of the tactile vector field, which is normalized to [_1, We then measure the force prediction error of TaFo-Net and evaluate its influence on the Dex.Move policy by comparing the resulting task success rates. Each setting is tested over 30 trials using both the book and the large can. The results are summarized in Tab. 4. Our system maintains strong performance even when the noise standard deviation reaches 0.1, demonstrating strong tolerance tactile noise. This robust- ness likely comes from two factors: the tactile signals in the training data are already noisy, enabling TaFo-Net learn denoising, and the random dropout of time steps, fingers, and markers (in Sec. 4.3) during training further enhances the robustness of the DexMove policy.
5.5 APPLICATIONS
The application scope of DexMove is broad. We highlight three representative scenarios: (i) Struc- tured sorting. As shown in Fig. 5 (c), the system follows language instructions such as "move box A to region 1" and reliably transports the box to the designated zone. (ii) Language-driven hu- man-machine collaboration. By leveraging a vision-language model (SoFar from Qi et al. (2025)), natural language commands (e.g., "put the grip of the electric drill into person's 's hand") are con- verted into a 3-DoF target pose that serves as the goal for our policy, as shown in Fig. 5 (d). (iii) Tidying up a Desk. Given a predefined desktop layout, the system automatically relocates each item to its assigned position, as shown in Fig. 5 (e). Across these scenarios, DexMove demonstrates robust manipulation capabilities applicable to diverse real-world settings.
6 CONCLUSION AND LIMITATIONS
We presented DexMove, data-driven framework for dexterous non-prehensile manipulation. Our approach utilizes hybrid data synthesis pipeline that combines the scalability of simulation for gen- erating diverse trajectories with the realism of human demonstrations for multi-finger force control. The core of our method is the data collection methods and set of policies that handle establish- ing contact, predicting future tactile force profiles with TaFo-Net, and generating condiconn trajectories via Dex Experiments show that DexMove achieves higher success rates and efficiency than single-contact and ablated baselines, and generalizes to long-horizon, language- conditioned tasks.
Several limitations were observed in our framework. (1) Objects with articulated parts, such as a telephone with a movable handset, can shift during manipulation and destabilize contact. (2) Spher- ical objects tend to roll, making stable initial contact difficult and increasing the risk of slippage. (3) Certain hand poses may also cause failure, for example, pushing tall can while grasping only its lid can cause the object to topple and restrict its rotational motion. In future work, we plan to address these issues and further explore skills that integrate both prehensile and non-prehensile techniques.
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REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. We plan to release all hardware de- signs (including the vision-based lactile sensors and the dexterous hand used in this paper), partial software implementations (including data generation scripts and inference code). Furthermore, even without access to the same hardware setup, researchers will be able reproduce our results by leveraging our data generation pipeline to synthesize task-relevant datasets and train models that can be adapted to their own hardware settings. Our submitted supplementary video presents an ex- tensive suite of real-robot experiments that bolsters the reproducibility and credibility of the paper's findings.
REFERENCES
Pooja Bhat, Matthias Nieuwenhuisen, and Dirk Schulz. Human-inspired non-prehensile manipula- tion strategies: Design, implementation, and evaluation. SN Computer Science, 5(1):70, 2023.
Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M. Dollar. Benchmarking manipulation research: Using the yale-cmu-berkeley object and model IEEE Robotics amp; Automation Magazine, 22(3);36-52, September 2015. ISSN 1070-9932, doi: 10. 2019mmaaa.0152244553
URL http: dxx doi org/10..//....o...... 10..//.////0
1109 MRRA 2015. 2448951.
Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. The Inter- national Journal of Robotics Research, 2024.
Yoonyoung Cho, Junhyek Han, Yoontae Cho, and Beomjoon Kim. CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects. In International Conference on Learning Representations (ICLR), 2024.
Won Kyung Do, Bianca Aumann, Camille Chungyoun, and Monroe Kennedy. Inter-tinger small object manipulation with densetact optical tactile sensor. IEEE Robotics and Automation Letters, 91):5155-522, 2023.
Siyuan Dong, Devesh K Jha, Diego Romeres, Sangwoon Kim, Daniel Nikovski, and Alberto Ro- driguez. Tactile-rl for insertion: Generalization to objects of unknown geometry. 2021 IEEE International Conference Robotics and Automation (ICRA), pp. 6437-6443. IEEE, 2021.
Hao-Shu Fang, Branden Romero, Yichen Xie, Arthur Hu, Bo-Ruei Huang, Juan Alvarez, Matthew Kim, Gabriel Margolis, Kavya Anbarasu, Masayoshi Tomizuka, Edward Adelson, and Pulkit Agrawal. Dexop: A device for robotic transfer of dexterous human manipulation. arXiv preprint N0KV:22509.0441, 2025.
Gunnar Farneäcck.
Two-frame motion estimation based on polynomial expansion. In Scandinavian conference on Image analysis, pp. 363-370. Springer, 2003.
Juan Del Aguila Ferrandis, Joao Moura, and Sethu Vijayakumar. Learning visuotactile estimation and control for non-prehe manipulation under occlusions. arXiv preprint arXiv:2412.13157, 2024.
Carolina Higuera, Akash Sharma, Taosha Fan, Chaithanya Krishna Bodduluri, Byron Boots, Michael Kaess, Mike Lambeta, Tingfan Wu, Zixi Liu, Francois Robert Hogan, et al. Tac- tile beyond pixels: Multisensory touch representations for robot manipulation. arXiv preprint vrXiv:2506.1455. 2025.
Binghao Huang, Yixuan Wang, Xinyi Yang, Yiyue Luo, and Yunzhu Li. 3d-vitac: Learning fine- grained manipulation with visuo-tactile sensing. arXiv preprint arXiv:2410.2444 24091, 2024.
Chunpeng Jiang, Wenqiang Xu, Yutong Li, Zhenjun Yu, Longchun Wang. Xiaotong Hu, Zhengyi Xie, Qingkun Liu, Bin Yang, Xiaolin Wang, et al. Capturing forceful interaction with deformable objects using a deep learning poweene stretchable tactile array. Nature Communications, 15(1): 9513, 2024.
11

---

Published as a conference paper at ICLR 2026
Jie Jin, Shihang Wang. Zhongtan Zhang, Deqing Mei, and Yancheng Wang. Progress on flexible tactile sensors in robotic applications on objects properties recognition, manipulation and human- machine interactions. Soft Science, 3(1):N-A, 2023.
Weiwei Li and Emanuel Todorov. Iterative linear quadratic regulator design for nonlinear biological movement systems. In First International Conference on Informatics in Control, Automation and Robotics, volume 2. pp. 222-229. SciTePress, 2004.
Yi Li, Yuquan Deng, Jesse Zhang, Joel Jang, Marius Memmel, Raymond Yu, Caelan Reed Garrett, Fabio Ramos, Dieter Fox, Anqi Li, et al. Hamster: Hierarchical action models for open-word world robot manipulation. arXiv preprint arXiv:2502.05445,, 2025a.
Yunshuang Li, Yiyang Ling, Gaurav S. Sukhatme, and Daniel Seita. Dexnoma: Learning geometry- aware nonprehensile dexterous manipulation. In 3rd RSS Workshop on Dexterous Manipula- tion: Learning and Control with Diverse Data, 2025b. URL https:://ppenreiieee. .ne// forum? Cerum?id-ScRppaee ScppD-sS.
Changyi Lin, Han Zhang. Jikai Xu, Lei Wu, and Huazhe Xu. 9dtact: A compact vision-basse tactile sensor for accurate 3d shape reconstruction and generalizable 6d force estimation. IEEE Robolics and Automation Letters, 9(2):223-33, 2023.
Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, and Yang Gao. Data scaling laws in imitation learning for robotic manipulation. arXiv preprint arXiv:2410. 18647, 2024.
Pei Lin, Yuzhe Huang, Wanlin Li, Jianpeng Ma, Chenxi Xiao, and Ziyuan Jiao. Pa-tac.Paper picking using tactile feedback in dexterous robotic hands. In Robotics Science and Systems (RSS), 2025.
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling, 2023. URL htttps://arxiii org/ abs /2210 02747.
Hangxin Liu, Zeyu Zhang, Ziyuan Jiao, Zhenliang Zhang, Minchen Li, Chenfanfu Jiang, Yixin Zhu, and Song-Chun Zhu. A reconfigurable data glove for reconstructing physical and virtual grasps. Engineering, 32:202-216, 2024.
Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow, arXiv preprint (rXiv:2209.03000 2022.
Jiangran Lyu, Ziming Li, Xuesong Shi, Chaoyi Xu, Yizhou Wang, and He Wang. Dywa: Dynamics- adaptive world action model for generalizable non-prehensile manipulation. arXiv preprint arXiv:2503. 16806, 2025.
Matthew T Mason. Mechanics and planning of manipulator pushing operations. The International Journal of Robotics Research, 5(3):53-71, 1986.
Miquel Oller, Dmitry Berenson, and Nima Fazeli. Tactile-driven non- poe-pneee object manipulation via extrinsic contact mode control. arXiv preprini 240:::2255.//1144.. 2024.
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville. Film: Visual reasoning with a general conditioning layer, 2017. URL https://: /ttptppss//axii....// orw.orgg/aaooo abs/1709. 07871.
Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas. Pointnet++: deep hierarchical feature learning on point sets metric space. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS' 17, pp. 5105-5114, Red Hook, NY, USA, 2017. Curran Associates Inc. ISBN 9781510860964.
Haozhi Qi, Brent Yi, Sudharshan Suresh, Mike Lambeta, Yi Ma, Roberto Calandra, and Jitendra Malik. General in-hand object rotation with vision and touch. In Conference on Robot Learning, pp. 1549-2564, PMLR, 2023.
Zekun Qi. Wenyao Zhang, Yufei Ding, Runpei Dong, Xinqiang Yu, Jingwen Li, Lingyun Xu, Baoyu Li, Xialin He, Guofan Fan, Jiazhao Zhang, Jiawei He, Jiayuan Gu, Xin Jin, Kaisheng Ma, Zhizheng Zhang, He Wang, and Li Yi. Sofar: Language- grounded orientation bridges spatial reasoning and object manipulation. CoRR, abs/2502. 1315532.13144.. 13143, 2025. doi: 10.48550/ARXIVV2500.2 13143. URL https::////........ //otts:///o.... /ttto//.oo/.....//////////////// 005.1.0.855///1
250..13143.
12

---

Published as a conference paper at ICLR 2026
Nikhila Ravi, Valentin Gabeur, Juan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichten- hofer. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714, 2024.
URL httpt:tp::/.. // //rxivv vv org/ abs 2408. 00714.
Wonik Robotics
Allegro Hand, 2025.
URL https://www.alleggg
com/
2h-v4-maim
Yu She, Shaoxiong Wang, Siyuan Dong, Neha Sunil, Alberto Rodriguez, and Edward Adelson. Ca- ble manipulation with tactile-reactive gripper. The International Journal of Robotics Research, 40(12-14):1385-1444 2021.
Zilin Si and Wenzhen Yuan. Taxim: An example-base simulation model for gelsight tactile sensors.
IEEE Robotics and Automation Letters, 7(2):2361-2368, 2022.
Jochen Stüber, Claudio Zito, and Rustam Stolkin. Let's push things forward: A survey robot pushing. Frontiers in Robotics and AI, 7:8, 2020.
Yuhao Sun, Shixin Zhang, Zixi Chen, Zirong Shen, Fuchun Sun, Cesare Stefanini, Di Guo, Shan Luo, Jianwei Zhang, Jianhua Shan, et Soft contact simulation and manipulation learning of deformable objects with vision-based tactile sensor. IEEE Transactions on Automation Science and Engineering, 2025.
Sudharshan Suresh, Maria Bauza, Kuan-Ting Yu, Joshua Mangelson, Alberto Rodriguez, and Michael Kaess. Tactile slam: Real-time inference of shape and pose from planar pushing. In 2021 IEEE international conference on robotics and automation (ICRA), pp. 11322-11328, IEEE, 2021.
Sudharshan Suresh, Haozhi Qi, Tingfan Wu, Taosha Fan, Luis Pineda, Mike Lambeta, Jitendra Ma- lik, Mrinal Kalakrishnan, Roberto Calandra, Michael Kaess, et al. Neuralfeels with neural fields: Visuotactile perception for in-hand manipulation. Science Robotics, 9(6))eadd00628, 2024.
Sean Taylor, Kyungseo Park, Sankalp Yamsani, and Joohyung Kim. Fully 3d printable robot hand and soft tactile sensor based on air-pressure and capacitive proximity sensing. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 18100-18108 IEEE, 2024.
Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pp. 5026-5033. IEEE, 2012.
Yuhan Wang, Yu Li, Yaodong Yang, and Yuanpei Chen. Dexterous non-prehensile manipulation for ungraspable object via extrinsic dexterity, 2025. URL htt.attps//aaaav..ooraaab/////// 23120.
Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield, Foundationpose: Unified 6d pose estimation and tracking of novel objects, 2024. URL https: http//axivv////////v///aa///aava .orlvvorr//aaa abs 22/22212220 04344.
Fan Wu, Ziyuan Jiao, Wanlin Li. Zeyu Zhang, Hang Li. Jiahao Wu, Baoxiong Jia, and Shaopeng Dong. A vr-based robotic teleoperation system with haptic feedback and adaptive collision avoid- ance. IEEE Transactions on Consumer Electronics, 2025a.
Longyan Wu. Checheng Yu, Jieji Ren, Li Chen, Ran Huang, Guoying Gu, and Hongyang Li. Freetac- man: Robot-free visuo-tactile data collection system for contact-rich manipulation. arXiv preprint arXiv:2506.0194 2025b.
Chengyi Xing. Hao Li. Yi-Lin Wei, Tian-Ao Ren, Tianyu Tu, Yuhao Lin, Elizabeth Schumann, Wei- Shi Zheng, and Mark Cutkosky. Taccap: wearable fbg-dased tactile sensor for seamless human-to-robot skill transfer. arXiv preprint arXiv:2503.01
2025.
Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu. Jiefeng Li, and Cewu Lu. CPF: Learning contact potential field to model the hand-object interaction. In Proceedings of the IEEE/CVF International Conference Computer Vision (ICCV) pp. 11097-11 October 2021.
13

---

Published as a conference paper at ICLR 2026
Shiqi Yang. Ace: A cross- cross-platfomm visual-exoskeleton system for low-cost dexterous teleoperation. Master's s thesis, University of California, San Diego, 2025.
William Yang and Michael Posa. Dynamic on-palm manipulation via controlled sliding. 2024.
Han Zhang, Songbo Hu, Zhecheng Yuan, and Huazhe Xu. Doglove: Dexterous manipulation with low-cost open-source haptic force feedback glove. arXiv preprint arXiv:2502.07730, 2025.
Wenxuan Zhou, Bowen Jiang, Fan Yang, Chris Paxton, and David Held. Haeman: Learning hybrid actor-critic maps for 6d non-prehensile manipulation. 2023.
Junzhe Zhu, Yuanchen Ju, Junyi Zhang, Muhan Wang. Zhecheng Yuan, Kaizhe Hu, and Huazhe Xu. Densematcher: Learning 3d semantic correspondence for category-level manipulation from single demo. International Conference Learning Representations (ICLR) 2025, 2024.
Xinyue Zhu, Binghao Huang, and Yunzhu Li. Touch in the wild: Learning fine grained manipulation with a portable visuo- visuo-tactile gripper. arXiv preprint arXiv:2507.15063 2025.
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A APPENDIX
A.1 TACTILE SENSOR
Our system employs vision-based tactile sensors on both the manipulator (fingertips of the Allegro Hand (Robotics, 2025)) and the wearable data collection device (Fig. 8). In this section, we describe the design the proposed tactile sensors. The overall design is inspired by the open-sourced R- Tac Lin et al. (2025), which maps light intensity physical depth. Each sensor consists of four main components: a monochrome camera module, white illuminations, black-coated elastomer with white marker arrays, and the sensor shell.
Camera Module. The camera module is positioned at the base of the sensor. We use a CMOS OV9281 global shutter camera with 160 FoV lens capture the deformation of the curved clas- tomer surface under LED illumination. The camera exposure is manually fixed to ensure consistent readings. The module connects
desktop computer via USB and outputs single-channel MJPG data at resolution of 640 x 480 pixels and frame rate of 120 fps.
Illumination. Illumination is provided by white LED ring embedded in the black sensor shell. Light passes through dedicated pathways and is diffused by a frosted semi-transparent plate to achieve uniform illumination of the elastomer surface. The annular PCB hosts evenly spaced 2835 4000K white surface-mounted LEDs and 470 Q resistors, powered at 5 V.
Coated Elastomer. The elastomer comprises multiple silicone layers: transparent PDMS base (Dow Corning Sylgard 184) and semi-transparee layer (Smooth- On Ecoflex 00-10). All parts are fabricated using gel-casting techniques. The resulting curved elastomer provides a relatively uniform optical background when viewed from the camera. A black coating (Smooth-On Psycho Paint) is applied to block stray light. Compared Lin al. (2025), one improvement the addition of visual marker arrays the elastomer surface, enabling shear force detection. The white marker arrays are painted manually with a marker pen.
Sensor Shell. The sensor shell houses all components while forming internal light pathways. It is 3D printed black PLA material.
Fingertip Vision-based Tactile Sensors
Wearable and Portable Tactile Device
Elastomer Coating
Elastomer Silicone with Markers
Elastomer Clear Base
Illuminations Light Diffuser
Markers Motion
Depth Map
Illuminations LED Ring
Optical Flow
Mapping
66
Difference Image
Sensor Shell
Markers Position
66
Light Pathway
Inpaint
Threshold
Module
Non-contact
Contact
Sensor Shell
Reference
Frame
Base
Dexterous Robotic Hand
Figure 8: Monochrome vision-based tactile sensors integrated into dexterous robotic hand and wearable device. The sensors estimate normal forces from reconstructed depth maps and shear forces from marker dis- placements.
A.2 PROCESSING TACTILE SENSOR DATA
The sensor data processing pipeline consists two components: marker motion tracking and depth reconstruction.
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Marker Motion Tracking. Grayscale video frames are first acquired from the camera. Adaptive thresholding is applied to segment the markers from the background. To estimate marker displace- ments over time, we employ the Farneback optical flow algorithm (Farnebäck, 2003), which com- pares the reference frame with the deformed frame to compute the flow field.
Depth Reconstruction. Prior to reconstruction, marker regions inpainted in both the reference and deformed images. Then, we map grayscale values to indentation depth, a calibration procedure performed. Following Lin al. (2023), 2 mm diameter spherical indenter used press the elastomer surface, producing a look-up table between grayscale differences and externally measured indentation depths. The final depth map is obtained by combining the relative indentation depth with the reference curved surface.
A.3 ADDITIONAL IMPLEMENTATION DETAILS OF SYSTEM DEPLOYMENT
A.3.1 SYSTEM SETUP
Camera Setup. The flow matching policy for establishing contact is con- ditioned on the object point cloud. To obtain this point cloud, we use three depth cameras (Realsense D435i). The system setting is shown in Fig. 9. One camera mounted near the el- bow of the robotic arm, while the other two are positioned on oppo- site sides of the experimental plat- form to reduce occlusions and pro- vide broader point cloud coverage. The side-mounted cameras are also used for object pose tracking. All cameras are calibrated with respect the robot base frame. An identi- cal configuration is applied in sim- ulation, where virtual cameras are placed with the same extrinsic param- eters to ensure geometric consistency between real and simulated environ- ments.
Camera
Franka
Hand
Camera
Desk
Figure 9: System Setting: Overview of the experimental scene, showing the relative positions of the three cameras, the desk, the Franka arm, and the hand.
Control of the Robotic Arm and Dexterous Hand. Both the robotic arm and the dexterous hand are controlled using position control through ROS. The robotic arm operates in Cartesian space, controlling the end-effector's 's position, while the hand is controlled in joint space. Join position control is implemented using PID controllers.
Algorithm Deployment. The policy is deployed on NVIDIA RTX 4090 GPU, with an average inference time of approximately 22ms for each action chunk during manipulation (DexMovePPolicy TaFo-Net). At each inference step, the algorithm processes the latest T,- frames of sensor data to predict Tp = 5 consecutive actions, which are executed at 30 Hz to control both the Franka FR3 robot arm and the hand.
A.3.2 DETAIL OF ESTABLISH CONTACT
At the start of each trial, the robotic arm was moved to a collision-free configuration with an un- obstructed field of view. The hand was returned to a flattened neutral "zero" pose, and the arm was positioned at resting location to prevent fingertip occlusion of the central camera, ensuring a clear view of the workspace. We then captured synchronized depth images from three calibrated cameras and reconstructed the scene point cloud using the known intrinsics and extrinsics. A target object mask was obtained in each RGB view using SAM2 (Ravi et al., 2024), and the intersection (log- ical AND) of these multi-view masks was applied to the fused point cloud to remove points not belonging the target object, effectively performing fhape-from-silhoeeee filtering.
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Establish Contact
DexMove Policy
Painthltte
Linwer
Lirwer
Past States
Global Target
FILM
FILM
Flow Mutching
X
X1
TaFo-Net
Linear
Linear
pull
Heagget
Timr +
tolken
Global tokin
Linear
Linear
Cross Fliger Attention
Tranaformer
KayWelue
Quanr
Memory
Memoly
Transforner Deceder
Decodar
Volecity Fild
Loss
X1-
Gut
Cousul
Figure 10: Pipeline Detail: the whole pipeline consists of establishing contact, DexMove-Policy and TaFo-Net. use yellow arrows to show the data flow.
This object point cloud, together with the target object pose, was used to compute a feasible con- tact pose. The outputs of this computation included A hand R wrist Twist where A hand controlled the hand, while R wast and Twisst specified the arm's end-effector pose in Cartesian space. this part, all of the objects' positions and finger joints' positions given in the arm base coordinate frame.
To minimize the risk of collision during contact establishment, the end-effector trajectory was ad- justed. Instead of moving directly from the initial configuration to the contact pose, the arm was first guided to a waypoint located above the target contact posc, and then descended to the final contact configuration.
Flow matching is parameterized by five-layer MLP with layer widths of 128, 128, 512, 1024, and 1024. The PointNet++ backbone outputs 1024-dimensional point-cloud feature. We train with batch size of 128 using A with a learning rate of x 10-2 for 1.3M optimization steps.
A.3.3 DETAIL OF DEXMOVE-POLICY
As shown in Fig. 10, we project the T, past frames into sequence past tokens, and map the global target (Pi Pargget: Oragge)) into single target token (global token) using linear layer. Concatenating these yields (Tp +1] tokens. We then append a continuous time embedding (Fourier features followed by an MLP) as an additional token, resulting in total of (T, +2) tokens. A Transformer encoder processes this sequence to produce memory M E R (T,+)2)x
We sample an interpolation time t~~ 1) and construct a noised hand state =,=tX, + (1 X) We linearly project both X, and the planned future target force G d-dimensional emheddings and fuse them via FiL.M (Perez et al., 2017) as query tokens. Each query token is clement-wise enriched by adding learnable positional embedding and learnable time (t) embed- ding before being fed into the Transformer decoder, which outputs the velocity field.
During object manipulation, the two side-mounted cameras continuously tracked the object pose. Once the object reached the target pose, the inference process was terminated, and the task transi- tioned the repositioning stage.
We first train the model for 200,000 iterations with batch size of 2,048. We then perform an additional 20,000 iterations of sampler to 10-step inference scheme.
A.4 OBJECT POSE TRACKING
Since the primary focus of this work is manipulation rather than perception, the object pose is ob- tained using marker-based tracking scheme. Multiple ArUco markers of known size affixed to the object's surface, ensuring that at least one marker remains visible to the cameras at times and enabling continuous pose estimation throughout the manipulation task. Using calibrated cameras,
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directly reconstruct each marker's world-space coordinates. Before starting, the object coordi- nate frame is defined by setting the origin at the centroid of all markers and aligning the orientation with the world frame. During operation, the object origin is computed from any detected marker by applying the stored marker-to-origin offset corresponding to its ID.
A.5 IMPLEMENTATION OF BASELINES
A.5.1 DYWA & CORN
We acknowledge the DyWA (Lyu et al., 2025) and CORN Cho et al. (2024) projects for publicly releasing their code bases, which a teacher network under full privileged information (object point cloud, task state, and associated physical parameters), and subsequently distills this policy into student restricted obtainable robot observations. For fair comparison, we expose the object pose to the student during distillation, which simplifies and stabilizes the learning process.
and compare. The DyWA framework first optimizes
CORN shares the same simulation environment as DyWA WA and also needs to track the object pose. To ensure fair comparison, we further enhanced CORN by replacing its shallow MLP-based point cloud encoder with the same vision backbone as ours (PointNet++ Qi et al. (2017)). During training, we utilize 323 objects from DyWA, plus the 264 objects (88 objects already existed in YCB and were excluded) from DexMove.
A.5.2 TELEOPERATION
In our ablation study, we conducted an experiment to con- trol the hand wrist (with all fingers locked) via teleoper- ation. This was achieved using exoskeleton (Fig. 11) with the same kinematic structure as the Franka robotic arm, scaled to half size (1:2). Each joint of the exoskele- ton is equipped with Dynamixel actuator that directly measures joint angles. These measurements are mapped to the corresponding joints of the Franka arm for execu- tion. Experimental evaluation shows that the telcopera- tion system can operate at frequencies exceeding 60 Hz. For consistency with the experimental settings in this pa- per, we conducted teleoperation at a control frequency of 30 Hz.
A.5.3 ABLATION STUDY: WRIST-ONLY
In the ablation study, evaluate wrist-only controller by locking all finger joints. Concretely, during trajectory data generation, once the force-aware trajectory is ob- tained, we exclude the finger joint angles from the op- timization variables and optimize only the wrist state (R wist Twistt) Although, for many trajectories, wrist- only motion cannot attain the optimal solution, we nevertheless use the resulting trajectories to retrain the DexMove-Policy and report the corresponding execution performance.
Figure 11: The exoskeleton designed at half scale (1:2) with the same kinematic structure the Franka arm.
A.6 OTHER SETTINGS AND RESULTS
Fig. 12 shows the six objects used in our experiments and the four tape types with distinct coefficients of friction used construct the Friction- BB tabletop. We show more detailed execution sequence in Fig. 13 and in the supplementary video.
A.7 DATASET
We provide additional details of the dataset through illustrative examples shown in Fig. 14 and in the supplementary video.
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Published as a conference paper at ICLR 2026
Large Can book
Small Can
A
B
Protein Powder
Mouse
C
D
Keyboard
Lego
Figure 12: Experimental Objects and Surface Friction: The left panel shows the six objects used in the experiments, while the right panel illustrates the four types tapes we used construct friction B.
(A) Structured Sorting
(B) Control via Language Instructions
(C) Desk Tidying
(D) Random Object geometry
Figure 13: Gallery Non-Pre Prehensile Manipulation: Illustration of the experimental scenarios, including (a) relocating different boxes to designated regions; (b) controlling the DexMove via a large language model; (c) tidying up objects on a desk; and (d) evaluating the DexMove on objects with random geometries.
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Published as a conference paper at ICLR 2026
Figure 14: Illustration of the two datasets collected this work. Top shows the non prehensile trajectory dataset, bottom depicts the human- demonsstemtttrteed tactile force dataset.
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Published as a conference paper at ICLR 2026
Table 5; Summary of notations used in this paper.
Notation
Description
P I23
Initial fingertip contact position in the world frame Fingertip trajectory sequence under no-slip assumption
0:7 E Tx3
Po E R
Object reference point (e.g., centroid) at time Object translation trajectory from start to goal Target yaw angle of the object
Pobj
0.7
Warget E R
obj
R (w) C (3)
Rotation matrix around z-axis with angle w
R wrist E (3)3)
Wrist rotation matrix
Twist E R33
Wrist translation
A hand E RR
Joint angles of the dexterous hand with joints Displacement vector from fingertip to nearest object surface point
Perturbed displacement vector with Gaussian noise
d
DIP tip displacement vector
Legion
Loss encouraging contact within sensor effective region
G
Fingertip normal force magnitude Indentation depth measured by tactile sensor
D
Deessor
Surface normal at the contact point
Ve
e
R
Tactile vector field (v = 33 markers, encoding shear + normal) Number of fingers (4 in Allegro Hand) Number of history frames used as input Number of future frames to predict
F
T,
T
CER
Contact point positions in fingertip local frames Normal force for each finger Historical tactile vector fields Predicted future tactile vector fields System state token at time (for flow matching) Velocity field learned by flow matching
GERF
-T,000
VI:T,
X
u(-)
Leontact
Contact policy loss Reconstruction loss of TaFo-Net
Lree
B NOTATION
The summary notions used in this paper is illustrated in Tab.
C DECLARATION OF LLM USAGE
During the preparation this paper, large language models (LLMs) were employed to assist with language polishing and improving the clarity of the manuscript. The models were not used for gen- erating novel research ideas, designing experiments, analyzing results, or drawing conclusions. All scientific contributions, including the formulation of methods, implementation of algorithms, exper- imental design, and analysis of results, were carried out entirely by the authors.
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Reviews:
[{"review_id": "EzSXYs8ln2", "reviewer": "Reviewer_9vkj", "summary": "This paper proposes DexMove, a tactile-guided non-prehensile manipulation framework for dexterous hands. It first generates force-aware wrist-finger trajectories in simulation, and then collects real tactile data via a glove-like exoskeleton system, where a human wears the same tactile fingertips as the robot. Using the simulated trajectories, the authors train a force-conditioned imitation learning policy. Then, using real-world trajectories, the system learns to predict the desired forces given a target object pose and uses these predictions to guide the imitation policy. The method achieves robust planar pushing across diverse tabletop objects and generalizes to higher-level, language-conditioned tasks such as sorting and tidying. Experiments show a 77.8% success rate, outperforming gripper-based baselines and ablations by a large margin.", "questions": "1. How many different policies are trained in total? Specifically, are the contact-establishment and DexMove-Policy networks trained separately or jointly?\n\n2. Are the contact and trajectory policies both trained entirely in simulation?\n\n3. How robust is perception to occlusion, given the reliance on three depth cameras? Does failure of one camera degrade policy performance?\n\n4. In Table 3, what exactly does “Wrist-only*” denote? Does that mean teleoperation-based wrist control with locked fingers (as opposed to a policy-based wrist-only controller)?\n\n5. Have the authors tested beyond planar pushing, such as rolling, sliding on uneven surfaces, or multi-object rearrangement?\n\n6. More importantly, comments on the sim-to-real gap: Is TaFo-Net essential for bridging this gap? If so, why? Is it because force information is critical, or simply because real-world tactile data are needed to fine-tune the network?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Comprehensive system design: The paper demonstrates an impressive integration of simulation, tactile sensing, human demonstration, and policy learning. The wearable tactile exoskeleton represents substantial engineering effort.\n\nNovel hybrid data pipeline: The combination of simulated trajectories and tactile demonstrations effectively mitigates the lack of large-scale real-world tactile data and the sim-to-real gap.\n\nCompelling demonstrations: Real-world experiments, including language-guided and long-horizon tasks, highlight its potential as a general dexterous manipulation system.\n\nConnection to VLMs: Integration with vision-language models for goal specification expands the scope of tactile manipulation toward \nmultimodal reasoning and autonomy.", "weaknesses": "Task simplicity: Despite the complex hand control, the main tasks remain planar pushing on a tabletop. The method’s advantage over simpler mechanisms (like parallel-jaw pushers) is somewhat limited by the task design.\n\nPose tracking with markers: Object pose estimation relies on markers, reducing realism and preventing deployment in unstructured environments.\n\nLimited wrist motion: The dataset penalizes wrist rotations beyond ~90°, which may restrict generalization to more complex manipulations.\n\nEvaluation diversity: The test objects and surfaces, while varied, do not yet cover dynamic or multi-object interactions; thus, generalization remains partially demonstrated.\n\nSystem scalability: The wearable system and tactile glove require manual calibration and mounting. The scalability and robustness of data collection remain uncertain.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "", "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": "qG7zRPEbQ5", "reviewer": "Reviewer_byvP", "summary": "This paper presents a tactile-guided framework for non-prehensile manipulation with dexterous hands. The authors propose a complete pipeline that combines large-scale simulation-generated wrist–finger trajectories with human tactile demos to learn stable contact and motion strategies. The system integrates three core components: ContactFlow Matching for establishing robust contact using flow matching; DexMove-Policy, a goal- and force-conditioned flow-based policy that generates motion trajectories; and TaFo-Net, a tactile force planner modeling inter-finger coordination over time. DexMove enables the robot to manipulate various objects through pushing, rolling, and sliding without grasping, achieving high success rates and efficiency in real-world tests.", "questions": "1. How sensitive is the learned policy to tactile sensor calibration or noise? Since the method heavily relies on tactile feedback, it would be useful to know how performance changes under slight calibration errors or sensor drift over time Some sensitivity analysis is recommended.\n2. Could the proposed approach be extended to handle dynamic or moving objects? I'm just curious about whether DexMove can adapt its tactile-guided policy to scenarios where the object itself is in motion (e.g., sliding or being perturbed), and what modifications would be required to handle such cases.\n3. Plz see weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper tackles the underexplored problem of tactile-guided non-prehensile manipulation using dexterous hands, a setting rarely addressed in prior work that mostly focuses on grasping or gripper-based pushing.  The combination of large-scale simulation-based wrist–finger trajectory generation with human tactile demonstrations is both conceptually novel and practically valuable, enabling scalable yet physically grounded learning.\n2. The technical design is solid and coherent: three modules are integrated through a consistent flow-matching formulation, supported by clear ablations and real-world evaluations.\n3. The paper presents its ideas with clear structure and illustrative figures, making complex mechanisms understandable.", "weaknesses": "1. Limited Evaluation Scope and Generalization Evidence – Although real-world experiments demonstrate promising success rates, the object set remains small and relatively homogeneous (rigid, medium-sized items). The absence of tests on soft, slippery, or deformable objects limits claims of generalization.\n2. The experimental evaluation mainly covers lateral pushing, sliding, and simple rotational tasks, but lacks richer contact interaction types such as pressing, squeezing, or compliant surface exploration, which are essential to demonstrate the full potential of tactile-guided dexterous control. Expanding the benchmark to include pressing or deformable-object manipulation would provide stronger evidence of versatility.\n3. Several parts of the paper, especially the method section, are densely written without clear intuitive explanation. The logical flow among the three modules (ContactFlow, DexMove-Policy, and TaFo-Net) could be clearer. A cleaner, layered schematic emphasizing data flow and module dependency would make the overall system architecture much easier to understand. Some parts of the article could be polished.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "", "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": "2SSzm3ViKo", "reviewer": "Reviewer_xjea", "summary": "This paper presents DexMove, a novel data-driven framework for learning tactile-guided non-prehensile manipulation using dexterous hands. It proposes a hybrid data collection pipeline that combines large-scale simulation of wrist–finger trajectories with real-world human demonstrations collected via a wearable tactile device. Based on the data collection pipeline, it further introduces a three-part policy structure that is able to establish contact, predict future tactile force, and generate tactile-informed trajectories to handle a set of non-prehensile manipulation tasks. Experiments show that DexMove achieves higher performance than existing baselines, and generalizes to long-horizon, language-conditioned tasks.", "questions": "Please refer to the Weaknesses part.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "This paper is well motivated. The proposed hybrid data pipeline that fuses large-scale simulation with human tactile demonstrations shows potential to provide high-fidelity datasets for the community. Real-world experiment results demonstrate DexMove's superiority over single-contact and ablated baselines, showing its generation ability towards generalizing to long-horizon, language-conditioned tasks.", "weaknesses": "1. The proposed method relies on strong assumptions: it employs three calibrated depth cameras and visual markers to track object pose and obtain visual observations.\n\n2. The paper lacks a detailed discussion of common failure cases. Understanding how and why the method fails (e.g., specific object geometries, friction conditions, or loss of contact) is critical for the audience.\n\n3. The demonstration of language-conditioned tasks like \"tidying\" is presented as a qualitative strength, but it lacks quantitative evaluation. It is unclear how robust this capability is, what the success rate is for these more complex tasks, and how much the language instruction actually guides a complex rearrangement versus simply triggering a pre-learned pushing skill.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "", "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": "KVPOwvFCsv", "reviewer": "Reviewer_Y6vV", "summary": "The authors introduce DexMove, a framework for tactile-guided, non-prehensile dexterous manipulation. The core problem addressed is the lack of large-scale datasets and coordinated wrist-finger control policies for such tasks. The authors propose a hybrid data acquisition pipeline that combines a simulation engine for generating wrist-finger trajectories with a wearable device for capturing real-world tactile force data from human demonstrations. This data is used to train a flow-matching model to establish initial contact, a network called TaFo-Net to predict future contact forces, and the main DexMove-Policy, which generates goal-conditioned wrist-finger motions. DexMove surpasses a set of baselines across six objects and demonstrates generalization to language-conditioned long-horizon tasks in the real-world experiments.", "questions": "See weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The hybrid data acquisition strategy is clever and effective. The hardware design is thoughtful, and the scale of the generated datasets is substantial.\n- The experimental evaluation is comprehensive and rigorous. The quantitative comparison against learning-based baselines (CORN and DyWA) is strong. The supplementary video demo is very helpful and provides qualitative examples\n- The demonstration of downstream applications, including structured sorting and language-driven collaboration, showcases the practical utility and downstream usages.", "weaknesses": "- The presentation is a bit hard to chew on. There are a few consistency and clarity issues throughout Sections 3 and 4. For example, $d^{\\mathrm{tip}}$ in Eq. 2 is undefined. In Sec. 3.2, the author defines $V \\in \\mathbb{R}^{v \\times 4}$, but in Sec. 4.3, the historical tactile data is denoted as $V_{-T_p : 0} \\in \\mathbb{R}^{T_p F \\times vC}$. This reuse of the same symbol with different dimensions could be confusing. $P^{\\mathrm{hand}}$ and $A^{\\mathrm{hand}}$ seem to be redundant as one can always derive $P^{\\mathrm{hand}}$ with FK.\n- The overall framework is composed of three separate neural networks (Figure 4). Although it is validated through system-level evaluation, the authors do not provide a clear justification for this modular decomposition over a more integrated architecture. Moreover, the DexMove-Policy is conditioned on a planned force schedule $G_{1:T_f}$ predicted by TaFo-Net. This could lead to compounding errors, yet no evaluation of the system's sensitivity is reported. The choice of flow matching is also presented without justification over the cited diffusion policy. A brief discussion on why flow matching is preferred would be greatly appreciated.\n- A disentanglement of the data contributions would further enhance the paper's quality. An experiment showing performance when training only on simulation data (e.g., with heuristic forces) is a critical missing baseline to validate this hypothesis. The claim that the isomorphic sensor design \"minimizes the domain gap\" is also not validated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "", "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:

Decision: Accept (Poster)