FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
Authors: Yifei Su
Ning Liu
Dong Chen
Zhen Zhao
Kun Wu
Meng Li
Zhiyuan Xu
Zhengping Che
Jian Tang
Venue: NeurIPS 2025 poster ()
Keywords: Visuomotor policy; Robotic manipulation; Efficient AI; Deep learning acceleration;
Primary area: deep_learning

Abstract: Generative modeling-based visuomotor policies have been widely adopted in robotic manipulation, attributed to their ability to model multimodal action distributions. However, the high inference cost of multi-step sampling limits its applicability in real-time robotic systems.
Existing approaches accelerate sampling in generative modeling-based visuomotor policies by adapting techniques originally developed to speed up image generation. However, a major distinction exists: image generation typically produces independent samples without temporal dependencies, while robotic manipulation requires generating action trajectories with continuity and temporal coherence.
To this end, we propose FreqPolicy, a novel approach that first imposes frequency consistency constraints on flow-based visuomotor policies.
Our work enables the action model to capture temporal structure effectively while supporting efficient, high-quality one-step action generation.
Concretely, we introduce a frequency consistency constraint objective that enforces alignment of frequency-domain action features across different timesteps along the flow, thereby promoting convergence of one-step action generation toward the target distribution.
In addition, we design an adaptive consistency loss to capture structural temporal variations inherent in robotic manipulation tasks.
We assess FreqPolicy on $53$ tasks across $3$ simulation benchmarks, proving its superiority over existing one-step action generators.
We further integrate FreqPolicy into the vision-language-action (VLA) model and achieve acceleration without performance degradation on $40$ tasks of Libero.
Besides, we show efficiency and effectiveness in real-world robotic scenarios with an inference frequency of $93.5$ Hz.

Foeqcolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
Yifei Su S.2, Ning Liu!+t, Dong Chen', Zhen Zhao', Kun Wu
Meng Li', Zhiyuan Xu', Zhengping Che tt, .Jan Tang
'Beijing Innovation Center of Humanoid Robotics 2NLPR, MAIS, Institute of Automation Chinese Academy of Sciences
Abstract
Generative modeling-basse visuomotor policies have been widely adopted in robotic manipulation, attributed to their ability to model multimodal action dis- tributions. However, the high inference cost of multi-step sampling limits its applicability in real-time robotic systems. Existing approaches accelerate sam- pling in generative modeling- originally developed to speed up image generation. However, major distinction exists: image generation typically produces independent samples without temporal dependencies, while robotic manipulation requires generating action trajectories with continuity and temporal coherence. To this end, we propose FreqPolicy, a novel approach that first imposes frequency consistency constraints flow- bowebased visuomotor policies. Our work enables the action model to capture temporal struc- ture effectively while supporting efficient, high-quality one-step action generation. Concretely, we introduce a frequency consistency constraint objective that enforces alignment of Frequency-domain action features across different timesteps along the flow, thereby promoting convergence of one-step action generation toward the target distribution. In addition, we design an adaptive consistency loss to capture structural temporal variations inherent in robotic manipulation tasks. We assess FreqPolicy on 53 tasks across simulation benchmarks, proving its superiority over existing one-step action generators. We further integrate FreqPolicy into the lasioo-language-cttin
visuomotor policies by adapting techniques
(VLA) model and achieve acceleration without performance degradation on 40 tasks of Libero. Besides, we show efficiency and effectiveness in real-world robotic scenarios with an inference frequency of 93.5 Hz.
Introduction
Recently, the generative modeling based visuomotor policy framework extends the powerful capa- bilities of generative models in text-to-image to-image synthesis [58 17
9 21 to imitation learning 39 19 6). achieving significant breakthroughs. A prominent class
56
60
for robotic manipulation 27
of such policies is diffusion- aww approaches [11] 76 46
33 23 which are widely adopted for the ability to model complex multimodal distributions in high-dimensional action spaces [TT.
75
More recently, flow matching 44 38 13. (6) has emerged as generalization of diffusion mod- els, offering simpler optimization objectives and more stable training [68]. It has been applied to robotics [59 19 77 25 proving the efficacy of flow-based policies. Despite these advance-
ments, these generative modeling-baat visuomotor policies rely on an iterative sampling process to transform Gaussian noise into actions, resulting in high-latency inference. This presents significant
"Co-first authors: Yifei Su and Ning Liu.
I Project leaders: Ning Liu and and Zhengping Che. i *ooresponning authors: Jian Tang and Zhengping Che.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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bottleneck for real-tima inference in robotic manipulation, where stable and smooth action execution essential, particularly in long-horizon or dynamic tasks [53 42).
To mitigate this issue, recent efforts leverage acceleration methods from the image generation domain to accelerate the action generation by reducing the sampling steps [53]
42
28
65
[6] A pioneering
work, Consistency Policy (CP) [53], adapts the consistency distillation from the text-to-image
domain 662 31 43
the robotics domain. CP first trains a powerful diffusion-based teacher using the EDM model [29], and subsequently distills its knowledge into a student model via the
64
41]
Consistency Trajectory Model objective [II, enabling the diffusion- based policy one-step action generation. ManiCM 42 extends the consistency distillation to 3D diffusion-based policy [76] with 655 adopt distribution matching distillation originally introduced in image generation [52 66 7 [4], distilling pretrained biffusion-based policies into one-step action generators by aligning the action distributions. While the aforementioned methods primarily focus on diffusion-based policies, FlowPolicy (78) attempts to reduce the sampling step of flow-based policy. uses consistent constraints from Consistency- [68] originally developed for image generation, to enforce velocity consistency across different points along the flow without distillation, facilitating straight flows and enabling one-step generation.
point cloud imputs. SDM (28] and OneDP
In fact, key distinction between image generation and robotic manipulation lies in the presence of temporal dependencies. While image generation produces individual samples that are typically independent across time, robotic manipulation involves executing action trajectories that form a time- series process. Therefore, it requires modeling outputs that are continuous and temporally coherent. We argue that leveraging temporal characteristics is essential, as it provides richer contextual information that can significantly enhance action generation. one-step visuomotor policy, named FreqPolicy, that first imposes frequency consistency constraints on flow-based visuomotor policy, thus leveraging temporal knowledge and enabling efficient and high-quality one-step action generation. Specifically, we introduce frequency consistency objective that enforces alignment of frequency-doman
this work, we proposed novel
action features across timesteps, improving temporal coherence in generated actions. Furthermore, we design an adaptive frequency component loss to learn the structural temporal variations inherent in robotic manipulation tasks.
Frequency Consistency Objective. Inspired by the fields of time-series forecasting and speech 81 70 73), frequency representations capture better non-stationary and oscillatory patterns, where time-domain features often fail. Thus, frequency-domain features show high effective- ness for modeling temporal dynamics. robotic manipulation, for figh-frreuency sampled action chunks, frequency features provide finer discrimination of subtle variations in smooth trajectories. To exploit this advantage, we enforce consistency between velocities of action chunks across different timesteps in the frequency space, thereby promoting the convergence of action generation.
processing 82
Adaptive Frequency Component Loss. In robotic manipulation, the distribution of frequency com- ponents within each action chunk varies over time throughout task execution. This variability arises because robotic manipulation sequences typically alternate between stationary and -tasttiionary motion phases. During low-dynamic movements (e.g., reaching or moving between positions), only a subset of action dimensions exhibit noticeable variation, while others remain relatively smooth. Con- versely, during high-dynamic movements (e.g., transitions between skills or contact-rich interactions), high-frequency variations are more prominent and informative. To capture this structured temporal variation, we draw inspiration from the Focal Loss [55 and propose an adaptive weighting scheme that dynamically emphasizes frequency components with greater discrepancy.
Our contributions can be summarized as: 1) FreqPolicy is the first one-step visuomotor policy that imposes temporal knowledge for robotic manipulation. 2) Inspired by the time-series and speech processing field, we propose the frequency consistency constraint objective to enhance the regularization of two arbitrary action velocities. Moreover, an adaptive frequency component loss proposed to capture the structured temporal variation of the action sequence. 3) We conduct extensive experiments in both simulation and the real world to evaluate FreqPolicy, demonstrating its superiority over existing one-step action generators, e.g., achieving 78. 78.5% in Meta World. 4) We further integrate
FreqPolicy into a vision-language-cction (VLA) model [6] 32), achieving a significant improvement in inference speed (e.g.. 5x faster) without compromising overall task performance.
2

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2 Related Work
Generative Modeling-based Visuomotor Policy. Learning from human demonstrations (a. k.a. imita-
tion learning (2) [I 20 Di has shown remarkable performance in robotic manipulation [8]
7 34 33
Compared to deterministic policies [50] 79 26 80
18 54), generative modeling- policies are
widely used for their ability to model multimodal action distributions, enhancing training stability in high-dimensional action spaces [[]. The pioneer work, Diffuusion Policy [[]], formulated visuomo- tor policy learning performance through a multi-step sampling inference process. Subsequent works [76] 30] extended diffusion-basee based policies to 3D point cloud inputs, enabling action generation directly from point cloud observations. [7] factorizes the policy into a high-level key waypoint predictor and low-level trajectory generator, achieving higher task success rates. Recently, another generative modeling paradigm, Flow Matching (FM) 40 38) has shown strong performance in domains including image
conditional denoising diffusion process, achieving impressive manipulation
generation 13 45 17 and super-resolution [4]. Compared to diffusion- generative models, flow matching (FM) directly defines probability paths via ordinary differential equations (ODEs) to transport the simple prior distribution to a target distribution, offering better numerical stability and fewer inference steps [36]. Hence, it has been applied to robotic manipulation [6 25 61 5 As
pioneer, Rouxel al. (59) extend flow matching to multi-support tasks and demonstrated strong performance. Subsequent works [19]22 [2] incorporate point cloud inputs, achieving manipulation in 3D scenery. Zhang al. (77 innovatively integrate spatial affordance prediction with flow matching for action prediction.
Accelerated Visuomotor Policy. Despite the impressive performance of generative- policies, they are limited in real-world deployment by the high inference cost of multi-step sampling [53] To address this issue, recent efforts have accelerated action sampling through four main mainstream as:
(1) Trajectory distillation. Consistency Policy [53] pioneered consistency distillation [62 43 31
in robot manipulation, enabling a few-step action generation by constraining denoising trajectories from different steps toward the same step. ManiCM (22] extended this approach to 3D scenarios
and achieved better one-step inference than 3D Diffusion Policy (76). FlowPolicy (78). inspired
by ConsistencyFM (68), applied constraints on the velocity field and transporting process in flow matching, enabling one-step action generation.
(2) Partial action denoising. Instead of denoising from Gaussian noise per step, SDP [22) outputs partially denoised action with variable levels of noise, where the noise-free part guides current action execution and the noisy portion serves as input for future rollouts. Falcon [10] proposed to select an action chunk from the historical denoised trajectory as the input. Both enhance the inference speed but still require multiple sampling steps. (3) Distribution matching. OneDP 655] and SDM (28) incorporated variational score distillation [66]
72 71 48 [4] into imitation learning and achieved strong one-step generation performance in 47 and 3D (74) input settings. Although effective, such approaches typically require
both 2D pretrained teacher model. In addition to the above methods, some works have introduced advanced Riemannian flow matching [15] and variance-based
adaptive sampling strategies (24] to achieve faster generation, respectively. This work aims to accelerate flow-based visuomotor policies using consistency-base paradigms, motivated by their potential to climinate the need for teacher models. Unlike existing works, we propose a novel perspective based on frequency consistency to enable more effective one-step action generation by leveraging temporal knowledge in robotic manipulation.
3 Methodology
3.1 Task Setting
We focus on the robotic manipulation task via the imitation learning paradigm: given dataset D containing n observations O = {o)= and corresponding expert action A {a]" the goal is to train a flow matching- based policy To (a1:nlo) O - A to map the observations o E O to action 01 with chunking size H, enabling the robot to replicate expert behaviors and generalize across diverse scenarios. Observations typically include proprioceptive states, RGB images from various viewpoints (e.g., eye-in-hand or hhird-person cameras), and point cloud data. The action space varies depending on the task and robot setup, and commonly includes either SE(3) motions of the end-effector or joint states. To thoroughly evaluate the effectiveness of the proposed method, we conduct extensive experiments under both 2D and 3D observation settings.
3

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Adaptive Frequency Component Loss
Froquency Condstency Constraint
(CT(s,)
Cen v1)
A(C...)
Peeception Frceeler
Grad.
A.
Reoright
(err(e.)
Robet Stade
A(GAA) = a, (e- als,
Naise
Prddetid Artien
(a) Training Frequency Consistrncy Objective
(b) One-Step Action Gemerator
Overview of FreqPolicy. a) For training, we use the frequency consistency constraint to align the velocity vectors across different time steps in the frequency space. Besides, we introduce an adaptive frequency component loss to accommodate the diverse frequency structures manipulation tasks. b) FreqPolicy takes 2D or 3D input and predicts the velocity vector of the action as output.
Figure 1:
3.2 Preliminaries
Flow Matching. Let Rd denote the data space with data points z Rd, and consider simple prior distribution 2g ~ Po(z), FM aims to learn a time-dependent vector field v(t, x) [0, 1] x R2 + Rd
that generates time-conditioned ordinary differential equation (ODE) follows:
dt x) vf,, (f,,))), (0002)) 2) ~ Po dt
(1)
+()) [0. 1] x Rd + Rd is the solution to the ODE, also known the flow. Based on this, the distribution po can be transported to more complex distribution pt via the push-forward equation:
θ Ox
Pc Po (v (t. x)) det
(t, x:)
(2)
To transform the prior distribution po into unknown target distribution pi via flow dh with the marginal distribution satisfying Pi=u Po and Pt=1 - Pt. flow matching seeks to optimize the vector field vtt, 2) by minimizing the following objective:
Lo = E.,p, [t, 2.)) - u(t, xxe)l2
where denotes the learnable parameters of the vector field, which in turn leads to deep parametric model of the flow vb u(t, vx)) is the corresponding target vector field. Despite the simplicity of the objective, directly optimizing in practice challenging due the lack of prior knowledge of the desired pt and w(t, v,). Therefore, Conditional Flow Matching (36) proposes to regress uy (4, xx) via a conditional vector field utt, xx(211 and conditional probability path Pe as:
Lo Et,pt (#)(1) (t,
(4)
Here Eq. 3 and Eq. 4 share the same gradient with respect to thus Eq, 3 can be estimated
2t, ( and p(t, (r) is tractable. The choice of u(t, (z) is not unique, and classic instance
obtained by linearly interpolating between He and æ 40 38 which owns the desirable property of generating straight probability paths:
a(t,)
(5)
Lo Ee [ooo(,, (tt, (01--10) (x)-1:)lla
(6)
3.3 FreqPolicy
Flow-based policies suffer from slow inference due to their iterative sampling process, limiting their use in high-frequency control scenarios and deployment on edge devices. propose FreqPolicy, novel approach that enables efficient one-step action generation by introducing temporal constraints from the frequency perspective, as shown in Fig. [] Inspired by the time-series and speech processing fields, propose the frequency consistency constraint objective to enhance the regularization of two

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arbitrary action velocities. Besides, the adaptive frequency component loss is proposed to effectively capture the structured temporal variation of the action sequence. We provide details on the model architecture in Sec. A.I
Basic Flow Matching Learning Objective. An intuitive way to apply flow matching in robotic manipulation is to regress a vector field mapping noise to action chunks, as adopted in prior works [19 We first equip FreqPolicy with this objective to enable multimodal action generation.
59 12
24 6].
Specifically, considering a simple noise dg ~ N0, I) and a corresponding expert action d1 ~ Pexpert we aim to learn the vector field (tt az) that maps the prior noise expert actions by approximating the real conditional velocity field u(t, at). Following flow matching based on optimal transport theory [40 [38, we instantiate the target velocity field u(t, az) constant vector along the optimal linear transport from Og to d1, namely:
u(t, a4) J/¹ u(2, u(2, ay)dz d1 do
Next, for any timestep E [0, 1]. we optimize the policy by minimizing the following objective: Lim 1.,11(011), (ag.ar)<D ||va az) (a1 ao)ll2
(8)
where at is defined as the linear interpolation between ag and a with respect to time t, a (1 ()- og + t 01 For inference, we sample an initial noise vector ag and deterninistically integrate the learned vector field vo(4, ae) from / = 0 to ( = 1 using ODE solver (e.g., Euler or Runge-Kutta), yielding the final action d1, While optimizing the objective in Eq. Biyilds a functional policy model, it still requires multiple sampling steps to generate desired action (e.g.. 10 steps in To [6] and steps in GROOT N1 (5).
Frequency Consistency Constraint Objective. In the fields of time-series analysis and speech processing {82 81 70 73,, frequency-domaii -omaan features show superior ability to model temporal dynamics, e.g., non-stationary [69] and oscillatory patterns, where time-domain signals often fall short. In robotic manipulation, frequency features provide finer discrimination of subtle variations in high-frequency sampled action chunks [49]. Therefore, differing from prior approaches [78] that treat multi-dimennional action chunks as static vectors, the proposed FreqPolicy regards each action chunk as temporal signal. We aim to enforce consistency in the frequency space between action velocity vectors across different timesteps of the flow, promoting straighter flow and one-step action generation. Concretely, given pair of initial noise and target actions (ao, a1) sampled from the prior and expert distributions, we select two arbitrary timesteps s, r E [o, 1], and then construct the interpolated state between ag and ai as:
ar (1 r) r) + 01
a = (1 - s) + 8
We enforce the velocities (s a.) and vo(r,a,) to be consistent across timesteps through the following training objectives:
Crro (r,,5 (ar,a))< [Sim(ve(s, as), vo(r, ax)1l
(10)
(r,,,, (D) [Sim(a, + (u 8) 8) to (s, az) ;a, + (u r) vo (r, ar)]
where Sim(-) is function measuring the consistency of two velocities, and satisfies s The first part of Eq. 10 directly ensures the consistency of the two velocity vectors, while the second part enforces the consistency of the vector field from trajectory perspective. Specifically, starting from arbitrary two points, a, and a., are expected to converge to the same point at time u, thereby providing a way directly define straight flows (68) These constraints enforce consistent and straight flow across different timesteps, thus facilitating effective one-step action generation.
To leverage the temporal characteristic of the action chunks, strengthen the temporal constraint with the frequency regularization. Namely, we propose to project the velocity vectors into the frequency domain using the type-Il Discrete Cosine Transform (DCT):
H-1
F(ee). = Σ (n) cos
π N
n 2
k:
for le 0, O.....
(11)
where F(U))x k denotes the spectral coefficient of the k-th frequency component of vo(t, at). The function Sim(-) in Eq. i0 then defined as the Eg norm between the two frequency coefficients as: Sim (u,, v.) = [[((,)) F(v))l22
(12)
5

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This loss encourages the frequency spectral profiles of velocity signals at different timesteps to match, thereby aligning their temporal dynamics. As a result, the action chunks generated across different timesteps exhibit greater temporal consistency, which in turn guides the policy towards learning straighter and more stable flow trajectories suitable for one-step inference.
Adaptive Frequency Component Loss. In robotic manipulation, the distribution of frequency com- ponents within each action chunk varies over time throughout task execution. This variability arises because robotic manipulation sequences typically alternate between stationary and non-stationary motion phases. During low-dynamic movements (e.g., reaching or moving between positions), only a subset of dimensions in the action chunks exhibit noticeable variation, while others remain relatively smooth. Conversely, during high-dynamic movements (e.g.. transitions between skills or contact-rich interactions), gigh-frequency variations are more prominent and informative. We show some visualization examples Sec. A.2 To effectively capture this structured temporal variation, we draw inspiration from the Focal Loss [5 and propose an adaptive weighting scheme that emphasizes frequency components with greater discrepancy. Concretely, for each frequency band k, we com- pute frequency-doman weight wi based on the difference between the corresponding frequency components of two action velocity vectors:
exp
Wk Ej= exp (IIF(1)); F(2))s112)
(13)
where ( is the DCT coefficient of the k-th frequency component. We then define the adaptive frequency component loss as:
H
Sim (uy, v.)) = Σ wk 1IF((1F()r))
F(os)al
(14)
k=
This adaptive loss encourages the model to focus more on frequency bands that encode informative dynamic variation across time. As a result, it strengthens the model's ability align the temporal structure of action chunks across different timesteps. We investigate the effectiveness of the adaptivity capability of the proposed loss in Sec. 4.3
Overall Training Objective. Our training objective consists of two complementary components: a standard flow matching loss in Eq. supervising the flow from prior noise expert actions, and frequency-dome domain loss Eq. 14 that enforces temporal consistency across various timesteps. We jointly optimize these two objectives through the final loss:
Lioul
Lim + Lite
(15)
This unified formulation ensures that FreqPolicy learns both accurate and temporally consistent flows, ultimately enabling reliable one-step action generation across diverse manipulation tasks.
Experimental Results
4.1 Simulation Experiments
To thoroughly validate the FreqPolicy, we conduct simulation experiments under both 2D 47 and 3D input settings (74, 3 551 as shown in Fig. 2 and perform comprehensive comparisons. We further integrate FreqPolicy with existing vision-languaae-action (VLA) models to demonstrate its generalization ability.
Experiment with 2D Inputs. The FreqPolicy is evaluated on 5 tasks from the widely-used Robomimic [47 benchmark: Lift, Can, Square. Transport, and Tool Hang. For each task. we use proficient human demonstrations datasets with image-based observations, containing 200 demon- strations per task. We evaluate the average success rate across 3 random seeds for all tasks. For each seed, we evaluate each task over 50 different initializations and compute the mean success rate. The Number of Function Evaluations (NFE) metric is reported measure the number of generation steps.
Table compares our approach with existing policies on the Robomimic benchmark. FreqPolicy surpasses prior one-step methods, e.g., CP [53) and IMLE Policy [57] Moreover, FreqPolicy even outperforms a few classical multi-step policies. We further reproduced Consistency-FNe [68] that uses spatial-domain constraints for robotic manipulation with the same setting. FregPolicy

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LIFE
Robomimic
Meta World
Adroit
Real-world
Figure 2: Experimental benchmarks. We evaluate FreqPolicy on 5 benchmarks, including a total of 93 simulated tasks (left) and real robotics tasks (right).
Table Performance on the Robomimic dataset 47 We compare the FreqPolicy against a set of state-of-the-an multi-step generative visuomotor policies. We report the mean and standard deviation of success rates. Tasks marked with * are the result of our implementation.
Method
NFE
Lift
Can
Square
Transport
Toolhang
DDPM
15
1.00
0.98 + 011
0,91 + .01
0.80 + .04
0.52 + .05
DDiM
15
1.00
0.99 + 00
0.92 = .03
0.79 + .04
0.55 + .05
Reetified Flow 40
15
1.00
0.96 + .22
0.90 .02
0.84 00
0.90 + .02
ActionFlow
19
10
1.00
0.99
0.87 = .11
0.81 = .09
AdaFlow 24
1.00
1.00
0.98
0.92
0.88
Posiste
33
3
1.00
0.95 = .02
0.96 = .01
0.88 + .02
0.77 .03
DDiM 11
1
0.04
0.00 = .00
0,00 = .00
0.00 - .00
0.00 = .0
Consistency
53
1
1.00
0.98 00
0.92 = .02
0.78 :03
0.70 = .03
Consistent-FM
668
1
1.00
0.94
0.90 =
0.84 02
0.80 + .02
IMLE Policy (77
1
1.00
0.98
0.82
0.90
0.81
Ours
1.00
0.98 = .02
0.92 = .02
0.90 + .02
0.85 + .03
achieves superior performance, improving the Transport task by 6% and the Tool Hang task by 5%, demonstrating the effectiveness of the proposed frequency-domatt consistency.
Experiment with 3D Inputs. To implement fair comparisons, we follow the existing methods [76 28
and conduct experiments 53 tasks across two benchmarks: Adroit [55) and MerlW [74]
Specifically, we use reinforcement learning with VRL3 63 to collect expert demonstrations for Adroit, and scripted policies obtain demonstrations for MetaWorld. Training is conducted using 10 expert demonstrations per task. Following the evaluation protocol in previous works [76] (8). report the performance for each task across random seeds. For each seed, evaluate 20 segments every 200 training epochs and compute the average success rate of the top-5 trails, along with the average inference time per task.
Table presents the comparisons with previous methods and demonstrates consistently improved performance. Compared to the SDM (28] strategy using distribution matching distillation, FreqPol- icy achieves consistent gains in average success rate across benchmarks, e.g.. MetaWorld rises from 74.8% to 78.5%. Unlike SDM, FreqPolicy eliminates the dependence pretrained teachers. Moreover, against FlowPolicy [78 applying a direct consistency constraint the spatial domain, FregPolicy maintains 1.3% lead MetaWorld. This advantage is particularly pronounced on the Medium, Hard, and Very Hard splits of MetaWorld further validating the benefit of our frequency consistency supervision. Please refer to Sec. A.3 for details.
Experiment with VLA settings. We combine different policies with the classic Open model [33 and conduct systematic experiments the LIBERO simulation benchmark (37), covering task suites: LIBERO-Spatto
LIBERO-Objecl LIBERO-Goal, and LIBERO-Lons Each suite provides 500 expert demonstrations across 10 tasks, designed to evaluate policy generalization across varying spatial layouts, object types, goal specifications, and long-horize tasks. All models are evaluated under the same experimental protocol, with results averaged success rate (%) over 500 trials per suite (10 tasks x 50 episodes). Please refer to Sec. A.4 for more details.
We integrate FregPolicy, Diffusion Policy (DP) 11] and Flow Matching (FM) 59 19 with the
Open VLA. Table shows the comparisons of VI.A models with various policy heads. Our method
(Open WeA.VLLFFFrrglicc achieves the highest average success rate of 94.8%, outperforming both

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Table Performance on the benchmarks with 3D inputs [74] 15 challenging tasks with 3 random seeds, reporting the mean success rate (%) and standard deviation. Tasks marked with * are reproduced results.
We assess performance on 53
Meteworld Easy (28)
Metaworld Medium (11)
Metaworld Hard (6)
Metaworld Very Hard (5)
Method
NFE
Adroit (3)
Average
DP 11
10
31.7
83.6
31.1
9.0
26.6
55.5
3.6
DP3 76
10
74.3
89.0
72.7
38.0
75.8
76.
2.3
ManiCM 42
1
72.3
83.6
55.6
33.3
67.0
69.0
4.6
SDM 22
1
74.0
86.5
65.8
35.8
71.6
74.8 + 4.5
FlowPolicys
78
1
69.3
89.6
66.5
43.8
75.8
77.2 + 2.8
Ours
72.6
90.6
46.8
79.4
78.5
2.6
Table 3: Success rates and inference speed on LIBERO benchmark [37. Methods with * indicate implementations. FreqPolicy (NFE 1) outperforms Diffusion Policy (NFE 50) and Flow Matching Policy (NFE 10 & 1), achieving higher success rates and faster speed, demonstrating superior effectiveness.
Method
NFE
Spatial (%)
Object (%)
Goal (%)
Long (%)
Average (%) Speed (Hz)
Open VLAnVL VP
32
50
92.0
75.0
93.4
11.8
68.1
0.32
Open Flow Matchnng
1
95.0
97.6
96.0
85.2
93.5
5.92
Open FloMatchihn
10
96.0
97.2
97.8
83.6
93.7
1.26
Open OpeaVLA--FeexPPlcy (Ours)
97.0
98.6
96.0
87.6
94.8
6.05
OLenVLA-DP and tied for second-best. Compared Open VLA-DP, which has average success rate of 68. 1%, Open VL-FeiepPloycy shows a significant improvement, especially in the Long task suite (87.6% vs. 11.8%). Notably, LppeFVLL-FrePPolic with just NFE achieves higher average success rate than Open VLA Flto-FFlttathhing with 10 NEE (94.8% vs. 93.7%), while offering significantly faster inference speed (5 times faster).
in all categories except for the Goal suite, where
4.2 Real World Experiments
Task Design. We design long-horizon real-world tasks on different robotic arms as illustrated in Fig. to evaluate the FreqPolicy: 1) Fruit Sorting Task. The Franka robot is required to sequentially place a banana, an avocado, and a mango into a basket. This a compound pick-and-place task involving ordered object manipulation. 2) Toy Organization Task. The Franka robot must first pull open a drawer, place two toy dolls inside, and then close the cabinet door. This task integrates multiple skills, including pull, push, and pick-and-place 3) Trash Disposal Task. The UR robot needs to open a trash bin lid, place several pieces of food waste (e.g.. half a bun and half a piece of bread) into the bin, and then close the lid. This task requires combination of pressing and pick-and-place fine manipulation capabilities. More demonstrations can be found in Sec. A.5
Main Results. We primarily compare against Diffusion Policy [1] and Flow Matching Policy with 1-step and 10-step inference settings, respectively. For each evaluation, we perform the physical robot and report the mean success rate. We also measure and report the real-time inference frequency of cach policy using an NVIDIA RTX 4090. As shown in Fig. 3a and Fig. 3b FreqPolicy achieves success rates comparable to, and in some cases exceeding, those of multi-step policy models, while using only single-step inference. For example, Task 1, FreqPolicy achieves top success rate of 70% at inference speed of 93.5 Hz, outperforming multi-step Diffusion Policy (55% at 19.8 Hz) and Flow Matching Policy (60% at 20.2 Hz). Similar trends are observed across all tasks. These results demonstrate that FreqPolicy can match or surpass the performance of multi-step policy models with just one inference step, while significantly reducing computational overhead. Additional evaluation details provided in Sec. A.5
(59
12 19
20 trials with various initializations
4.3 Ablation Study
Table shows the effect various consistency constraints on one-step action prediction. Model #1 serves baseline, using vanilla flow matching for one-step action generation. Model #2 equips

---

Task 1: Fruit Sorting
Task 2: Toy Organization
Task Trash Disposal
DP-1 step
DP-10 step
FM-1 step
step
Task
922
SEA
(a) Success rate (%) on three real-world tasks.
(b) Inference speed (Hz) on three real-world tasks.
Figure 3: Demonstrations of three real-world tasks (top) and the test results of different policies (bottom). We evaluated the success rate (%) and inference speed (Hz) for all methods. DP stands for Diffusion Policy, FM for Flow Matching Policy, and the number represents inference steps of the corresponding policy model. FreqPolicy consistently outperformed baselines in both success rate and inference speed, demonstrating its effectiveness on real-world robotic platforms.
Table 4: Ablations on Robomimic. We analyze the impact of various designs in transitioning from vanilla flow matching policy with one-step action generation to our FreqPolicy. Results on the Lift are omitted, all methods achieve saturated performance. In addition, we evaluate FreqPolicy under different spectral supervision settings to assess the importance of frequency-aware consistency.
Model
Consistency Objectives
Can
Square
Transport
Toolhang
#1 1
Vanilla Flow Matching (NFE=1)
0.94
0.90
0.84
0.76
# 2
w/ consistency constrain
0.98
0.92
0.90
0.78
3
w/ frequency consistency constrain high
0.96
0.92
0.88
0.80
# 4
w/ frequency consistency constrain low
0.95
0.90
0.88
0.82
5
w/ frequency consistency constrain full
0.96
0.92
0.92
0.82
# 6
w/ frequency consistency constrain adaptive
0.97
0.93
0.92
0.88
Model #1 with a consistency loss similar to Consistency-Fil [68 yielding a modest improvement (e.g., success rate increases 90% on the transport task).
To investigate the effect of supervising different frequency bands, Model #3 and Model #4 intro- duce wwwfirequency and high-frequency consistency constraints top of Model #1, respectively. These methods manually select specific frequency components for constraints and result minor improvements. We attribute this to the diverse and dynamic nature of frequency components in action chunks during robotic manipulation. Model #5 applics consistency constraints across the full frequency component, leading toolhang from 78% to 82%, thereby validating the efficacy of frequency consistency. Finally, Model #6 incorporates adaptive frequency loss, further boosting performance to state-of-the-arr levels and confirming the advantage of adaptive frequency constraints.
substantial gain, raising transport from 90% to 92% and
Limitation & Conclusion
In this work, we introduce FreqPolicy, novel one-step action generation policy that performs robustly under both 2D and 3D inputs. For the first time, FreqPolicy imposes a frequency-domanl consistency constraint on flow matching, encouraging consistent velocities across different timesteps, and incorporates an adaptive loss that emphasizes higher-variance frequency components. Through this training regimen, FreqPolicy learns straight-ling flow to produce single-step actions. We validate its effectiveness on 53 simulated tasks and demonstrate significant speed-up 40 Libero tasks for a VLA model. We hope that FreqPolicy will advance embodied vision-motion planning and VLA models in real-world applications. However, work presents certain limitations. We primarily validated the effectiveness of our proposed FreqPolicy on flow matching. In principle, the same core idea can be extended to diffusion based visual-motor policies, which we will systematically investigate in future work. Our method demands more computational resources, as it requires twice forward passes to process two random samples. In future research, will explore the computationally efficient one-step action generators.

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A Technical Appendices and Supplementary Material
In this appendix, we first present further details on the model architecture of FreqPolicy in Sec. A. Next, Sec. [A.2] provides additional visualizations and frequency-domait analyses of various action chunks. We then supplement the simulation experiments with additional information Sec. A.3 along with details on integrating FreqPolicy into VLA models in Sec. A.4 Finally, Sec. A.5 describes the setup and results of the real-world experiments.
A.1 More Model Configuration
As Visuomotor Policy.
Following previous works [1] 76 28 42 78
we adopt standard 1D
CNN-based U-Net architecture as the backbone of FreqPolicy to ensure a fair comparison with existing models. FregPolicy is designed to accept both 2D and 3D observations as input. For 2D images, we employ ResNet-18 as the visual encoder. For 3D input, we follow 76. 78 and use a lightweight MLP to encode the input point cloud.
As Head of the VLA Model. FreqPolicy can also serve as a policy head for existing VLA models, as long as the underlying VLA is capable of predicting action vector fields. In this work, we integrate FreqPolicy into Open VLA B3), following the setup in [32], where the predicted action vector field is obtained by applying a nonlinear mapping to the noise-conditioned latent features.
A.2 Extended Frequency Analysis for Robotic Manipulation
In Fig. we present visualizations of action chunks from different real-world and simulation scenarios, including the observed images, the corresponding multi-dimensional temporal action signals, and the transformed DCT frequency coefficients. In the simulation scenario shown in Fig. 4a the Franka robot transitions from "approaching the blue can" to "grasping the can",, during which the expected action chunk contains high-frequency variations associated with gripper motion. Similarly, in Fig. 4b] as the robot moves from "approaching the stove" to "placing down the kettle", the gripper opening action introduces high-frequency signals, while other control signals remain relatively smooth. In the real-world scenario shown in Fig. 4c. during the process of picking up the doll, the Franka robot exhibits relatively smooth motion transitions, with the action signal primarily dominated by low-frequency components. Hence, based the above observations of different action chunks and their frequency-donan domain characteristics, we justify the motivation of the adaptive frequency coefficient loss introduced in See 3 This loss enables the model to focus more effectively on the frequency components that exhibit meaningful variation across diverse action chunks.
A.3 More Details on Visuomotor Policy Simulation
A.3.1 More Details on Simulation with 2D Inputs
Implementation Details. We implement the RectifiedFlow 40 38 ConsistencyPolicy 55 and
Consistency-Fie (68) to comprehensively evaluate our proposed FreqPolicy. For RectifiedFlow, we adopt ID CNN-based U-Net to predict action vector fields from observations. Following (6]. sample time steps from a Beta distribution during training to interpolate intermediate states. For ConsistencyPolicy, we follow the procedure in (53), where EDM (29 teacher model is first trained in the initial stage, and then a student model is distilled for one-step action generation using the CTM objective 31 For Consistency-FM.
1D CNN-based U-Net predict action vector fields from observations. We then sample different time steps and from a uniform schedule and apply velocity field consistency constraints between them. To train all models, we use observations from the past 2 time steps as input and predict a 16-step action chunk, from which the first 8 steps are selected for execution. All models are trained using a batch size of 128 with the AdamW optimizer and learning rate of 1. 1.0e-4. Training is conducted for 1000 epochs on a single NVIDIA A100 GPU. For PonsistencyPolicy, the student model is trained for 450 epochs.
also
A.3.2 More Details on Simulation with 3D Inputs
Implementation Details. Following the prior works 76 78 the point cloud and use a ID CNN-based U-Net to predict the action vector field. To train the FreqPolicy model, we use batch size of 128 and the AdamW optimizer with learning rate of
we use lightweight ML. to encode
23

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(a) Visualization an action chunk from the Franka simulation dataset. When robol transitions from "reach the blue can" to "grasping it", the change in gripper introduces high-frequency signal variations. Other states exhibit no significant changes and tend to be smoother.
EST Madde
Ado Edr
(b) Visualization of an action chunk from the Franka simulation dataset. During the transition from "approaching the stove" to "placing down the kettle", the gripper exhibits seeeffreyeency variations, while other states remain nearly unchanged.
O
(c) Visualization of an action chunk from the Franka robot demonstrations, Once the robot successfully grasps the doll and begins placing into the drawer, the overall motion exhibits relatively slow changes under 30 Hz action sampling rate, dominated by low www-reuuency signals.
Figure 4: Visualization of the spectral and temporal signals across different action chunks.

---

Table 5: We report the evaluation details of the 53 challenging tasks from Adroit [55 and Meta- World 74] under random seeds, and report the mean success rate (%) and standard deviation for each task. Tasks marked with an asterisk * indicate re-implemented versions. Compared to Man- iCM 42 a one-step action generation model using consistency distillation based on diffusion-based policies, our method achieves superior performance. Similarly, we achieve an overall improvement of 9.4% over SDM [8 that adopts variational score distillation [72 71 to achieve one-step diffusion policy. Finally, in comparison to the flow-based policy FlowPolicy 788,, which uses only spatial-domain consistency loss, FreqPolicy still leads by 2.8% gains, demonstrating the effectiveness of our frequency consistency constraint.
Adroit
Meta-World (Easy)
Alg Task
Adroit Hummer
Adroit Door
Adroit Pen
Button Press
Coffee Button
Plate Slide Back Side
Diffusion Policy BD Diffusion Policy
5
+
13
99 =
=
100 = 0
100 100
100
100 0
+ 0
ManiCM
+
4
100 0
100 0
SDM Policy
100
73 # 2
4
100
100::0
100 0
FlowPolicy*
97 +
62 6
4
100 =
100 0
100 0
Ours
98 +
68 5
+
100 +
100 +
100 +
Meta-World (Easy)
Alg / Task
Button Press Topdown
Button Press Topdown Wall Button Press Wall
Pog Unplug Side
Door Close
Door Lock
Diffusion Policy
98
96 3
=
+
100 +00+00
86 +
3D Diffusion Policy
99 =
96 3
100 +
93 2
100 =
96 1
ManiCM
100
96 + 2
71 15
100
98
SDM Policy
98 2
99 +
100 +
74 19
100 3:0
96 +
PlowPy
100
100
+
8
100
100 =
Ours
100 + 0
100 0
100 +
87 4
100 +
100 +
World (Easy) Faucel Close
Alg Task
Dour Open
Door Unluck Drawer Close
Drawer Open
Faucet Open Handle Press
Handle Pull
Diffusion Policy
98 1
98 1
100 : 0
93
100
100 1
1
a
3D Diffusion Policy
100 0
100
100 = 0
100
100
100+ +
100 s
52 8
ManiCM
100 0
82 + 16
100 0
100 =
100 0
100 +
100 +
10 10
SDM Policy
100
1
100
100
100
100
28
PlowPolicy
100 0
100 0
100 =
100=00
99 +
100 +
100 0
25 +
Ours
100
100
1
1
100
100
100
2
Meta-World (Fasy)
Alg Task
Handle Press Side
Handle Pull Side
Lever Pull
Plate Slide Plate Back
Dial Turn
Reach
Reach Wall
Diffusion Policy
1 0
23 17
49 5
1
63 10
18 1
59 9
3D Diffusion Policy
0
82 =
84 #
100 : 0
100
91=0
26
74
ManiCM
0
48
8
100
9
8422
5
SDM Policy
0
68 à 6
84
100 0
100 0:
88
34
80
Policy
100
81
29
Ours
100 =
59 =
8
9
100 =0
91
32
10
7
Meta- World (Easy) Window Close
Meta-Worid (Medium) Box Close
Alg Task
Plate Slide Side
Window Open
Basketball
Bin Picking
Coffee Pull
Coffee Push
Diffusion Policy
100
100 +
100 +
85 6
+
5
67 +
3D Diffasion Policy
100
100 =
=
96
ManiCM
100
100 0
80 26
4 14
49 17
2
68 : 18
96 2
SDM Policy
100 0
100 0
78 18
a. 26
55 4 13
61 1
72
97 2
FlowPolicys
100
100 +
100
7
4
56 4
94 94
Ours
100 100=00
100 0
100 +
77
40 7
61 +
84 =
95 +3
Meta- World (Medium)
Meta-World (Hard)
Alg Task
Hammer
Peg Insert Side
Push Wall
Snocer
Sweep
Sweep Into
Assembly
Hand Insert Pick Out of Hole
Diffusion Policy
+ 6
2
14
18
10
15
0 0
0 0
3D Diffusion Policy
100 0
78
4
100 1
28
4
ManiCM
98 2
75 8
3
27 43
54 16
37
8:3
28 + 15
30 +: 16
SDM Policy
25
9
100
FlowPolicy*
97 3
5
5
24
91
26
91+
23
33
Ours
7
64 11
24
14
100
18
40
World (Hard)
Metn-World (Very Hard) Stick Push
Alg Task
Pick Place
Push
Push Back
Shelf Place
Disassemble Stick Pull
Pick Place Wall
Average
Diflusion Policy
040
30
040
1113
43
1112
43
5
55.5 i 3.58
3D Diffusion Policy
0+00
56
0+0
4
91
6
1
74
76.1 2.32
ManiCM
0+0
55+2
0+0
48
87
63
100
16
69.0 4.66
SDM Policy
010
5720
100
51
86
68 1811 10
53
74.8
Flow Polivyy
5915
4.
74.55
6
100
92
77.2 à 2.84
Ours
5
3
82
7
90
2.61
25

---

1.0c-4, training for 3000 epochs. Evaluation is conducted every 200 epochs, and the best persfoerformnn checkpoint saved. All experiments are conducted on single NVIDIA A100 GPU.
Result Details. We report the detailed success rates of FreqPolicy on 53 tasks from the Adroit and MetaWorld benchmark in Table 5 The success rate of each task is averaged over experiments conducted with 3 random seeds.
A.4 More Details about VLA Settings
Implementation. For Diffusson Policy, we leverage the implementation of 'OpenVLA (fine-tuned) + PD&AC, Cont-Diffusiin" from OpenVL.A-OFT (OpenVLLA-DP this paper). For the Flow Matching Policy, we integrate it into the VLe pipeline (i.e., Open WLA--llooMatchiigi original implementation [40]. We train for 150K gradient steps for all models using batch size of 16 across 4 A100 GPUs, policies receive one third-person image, one wrist camera image, robot proprioceptive state, and language instruction as input. The detailed hyperparameters are shown in T Please note that our training settings are not consistent with the original paper. In the default settings of OwenVLA-
based on the
[32], they train for 150K steps, using a batch size of 64 across GPUs. However, in our experiments, considering the computational overhead and resource capabilities, did not follow the full training settings consistent with the original paper (for example, the results we show are all trained on 2.4M samples, while the original paper trained 9.6M samples but to show the compatibility and superiority with VLA in comparison with the multi-step policy baselines. In the test, we perform 50-steps inference for Open and 1-step and 10-steps inference for Open VLA-FlowMacchiing The inference speed is averaged over the first three episodes of LIBERO-spatial to estimate performance. We follow the default settings in OLenVLA-OFT for other configurations.
more),
Table 6: The hyperparameter settings for VLA experiments. These values are kept consistent across all methods.
Parameter
Values
use_film
True
use_proprio
True
num_images_iie in_ in_input
2
32
image_aus
True
mia__stes__bffoe_deee_
100,000
max_stepp
150,000
num_gpus batch size
4
4 5e-4
learning rate
num trials Siaals_per_____
50
num inference steps
1 or 10 50
is an improved version of Open VLA 33 that integrates parallel decoding, action chunking, continuous action representation, and an LI regression- based learning objective. It achieves state-of-the-art performance on the LIBERO simulation bench- mark, significantly improving the average success rate while increasing action generation throughput by 26 times. We reproduced VLeNVLA-OFF 32) under the same training and testing settings, and the results are shown Table Specifically, our method leads or is on par with Open in all categories except LIBERO-IO, and leads Open VLA-OFT in average success (94.8% vs. 94.2%). Moreover, even though FreqPolicy leverages an additional policy head, the inference speed remains competitive. (6.05Hz vs. 65Hz)..
Compare with VLA-VLA-OFT. OpenVLA-OFT
http:s///gttabbboo/////////////p
26

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Table Comparison with OpenVLA-OFT on LIBERO simulation benchmark.
Method
NFE Spatial (%)
Object (%)
Goal (%)
Long (%)
Average (%)
Speed (Hz)
OpenVLA
92.0
75.0
93.4
11.8
68.1
0.32
Open Floo-llatthhhhingg
95.0
97.6
96.0
85.2
93.5
5.92
OpenVLA-Flowaa Mlowaachhh
10
96.0
97.2
97.8
83.6
93.7
1.26
Open
96.6
98.6
91.0
90.6
94.2
6.15
OpenVLA-rrree Prrec
97.0
98.6
96.0
87.6
94.8
6.05
A.5 More Details about Real- World Tasks
The real-world experiments are based on the open-source LeRobot framework LeRobot is designed support real-world robotics research by providing models, datasets, and tools in PyTorch. It includes state-of-the-art methods, such as Diffusion Policy, which we use as baseline, that have demonstrated strong transfer capabilities real-world settings. with focus on imitation learning. We convert our collected real-world data into the LeRobot-supported format and integrate both the Flow Matching Policy and our proposed FreqPolicy into the framework. This enables training directly on real data, facilitating practical deployment and inference. Training was conducted on a single NVIDIA A100 GPU. To ensure a fair comparison, all policy models were trained with aligned hyperparameters provided in Table We also provide additional demonstration images for the three tasks Fig. 5 as well as demonstration videos the supplementary materials to facilitate better understanding.
Implementation.
Table 8: The hyperparameter settings for real-world experiments. These values are kept consistent across all methods.
Parameter
Values
input_imm iaaanimaaa____a Franka amput_images UR
[camera feamera_font, camera_ wrist]
[camera left, camera_ _rrst]]
use_robe sss_robot_stare state
True
image_sit size
480 x 480
vision backbone
ResNet- 18
n_obs_steps
2
horizon
48
Llaction_stepp
48
num num_inference_steg
1 or 10
batch_ size # of training steps optimizer learning_ree rate weight_ decay grad clip_ norm
64
100,000
Adam
le-4
le-6
10.0
Evaluation Criteria. The initialization of the task environment plays a critical role, as it directly affects whether the robot can complete task under the guidance of the policy model. Moreover, the conditions leading to task failure vary across different tasks. To facilitate a deeper understanding of the real-world experiments presented in this work, we provide target and object placement rules for the three tasks, along with detailed descriptions of the conditions under which task execution fails:
Fruit Sorting.
Target: Pick up bananas, avocados, and mangoes and put them into the basket in order. Object placement rules: During execution, the fruit basket remains stationary in fixed position. The spatial order of the three unchanged. However, in each trial, the exact placement of the fruits randomly initialized within the front half of the basket.
avocado, and mango, from right to left-remains
27

---

2
5
7
10
11
13
14
15
(a) stratioos of real-world Task 1. This task is carried out on the Franka platform. The robot arm is
tasked with sequentially picking up three different fruits truts--ananaa avocado, and mango and placing them into basket. The fruits are positioned one at time random locations front of basket, and the task is deemed successful only all three fruits are correctly picked up and placed in the basket.
2
10
11
12
13
14
15
(b) Demonstrations of real-world Task 2. This task carried out on Franka platform. The robot arm must first rotate to the appropriate angle, open its gripper. and insert it into the drawer handle to pull the drawer open. It then sequentially picks two plush toys placed randomly on the table and places them inside the drawer. Finally, it closes the gripper and pushes the handle to shut the drawer. This requires the robot accurately perceive the drawer handle's start and end positions and he robust to the random positions of the toys.
2
5
10
13
(c) Demonstrations real-world Task This carried out on the UR platform. The robot arm first closes its gripper and attempts to tap specific spot on the trash bin lid with the tip of the gripper to trigger to pop open. Then, with the gripper open, the arm sequentially picks up two pieces of soft food waste half-steamed bun and piece of bread bread-randomly placed the table. Finally, the gripper closes again, moves behind the lid, and pushes while tapping once more to fully close the bin. This process requires precise tapping actions and robustness to random placement the food waste.
Figure 5: Demonstrations of three long-horizon real-world tasks. Following consistent protocol, 300 episodes are collected for each task for training, after which the trained policies are evaluated. The numbers in each image indicate the sequential steps in the task execution process.
28

---

Cases of task execution failed: any fruit falls while picking up, it will be considered a failure case.
If the same fruit fails to be picked up more than 3 times in total, it will be considered failure case.
If all fruits are not completely put into the basket, it will be considered failure case.
2) Toy Organization.
Open the drawer, then put the two dolls into the drawer one by one, and close the drawer. Object placement rules: During each execution, the cabinet remains in fixed position, while two dolls are reinitialized with their relative spatial positions preserved.
Target:
Cases of task execution failed:
If the drawer is not successfully opened, it is considered a failure case.
the drawer is not closed after all the dolls are placed, it considered a failure case.
If all the dolls are not placed in the drawer, it is considered failure case.
If the cumulative number of failures pick up the same doll is 3 or more, it is considered
failure case.
3) Trash Disposal.
Target: Tap the trash can lid to make it pop open, put the food waste (steamed buns and bread) into the trash can one by one, and tap the lid again to close it.
Object placement rules: During the execution, the position of the trash can is fixed, the relative spatial positions of the two types of food waste remain unchanged, and the trash positions are reinitialized for each execution.
Cases of task execution failed:
- If the lid is not closed after picking up the food waste, it is considered failure case.
the number failed attempts to pick up the same food waste exceeds 3 times, the machine will be deemed as a failed case.
lid closed before the food wastes are put into the trash can, will considered
failure case.
the lid is not opened and food waste taken, it will be deemed as a failure case.
29


Reviews:
[{"review_id": "RK4eHYvOVN", "reviewer": "Reviewer_kyT9", "summary": "The authors investigate methods for improving generative policies in robotic control. The problem they identify is the use of iterative generative models when generating actions, which reduce action sampling rates. They suggest that a solution is to introduce temporal consistency loss functions as a means of enhancing single-step prediction policies. The authors apply this loss function to a flow-matching policy and demonstrate the efficacy of this approach through extensive simulation and real-world experiments, showing improvements in robot control while increasing the action frequency rate.", "questions": "- Related Work line 95: How does the author’s work differ from the various previous works on generative models in visuomotor policies? \n- Line 176 - Isn’t this technically preliminary work if you’re using the same loss as other papers?\n- Equation 8 - Should the target be a_1 - a_t? As written, my understanding is that v_\\theta should always predict the whole duration from a_0 to a_1 regardless of the time or a_t value given. \n- Equation 10 line 204:  If r <s < u, wouldn’t it be better to explicitly show this in the notation when sampling? I.e. r \\sim U(0, 1), s \\sim U(r, 1), u \\sim U(s, 1)? Or something similar? \n- Equation 11, 13, what is “H” here?\n- What research questions are the experiments addressing? \n- Table 1: are the reported improvements statistically significant by an appropriate hypothesis test?\n - How many runs were used in the ablation experiments shown in Table 4? It seems strange there aren’t standard deviations reported.", "limitations": "No\nThe limitations section (lines 335 - 340) doesn’t list any real limitations: “Our work focuses on accelerating flow matching through frequency domain modelling.” which is followed by a vague comment that the authors might or might not try to enforce other things on temporal motions. The authors should consider the potential problems or pitfalls of using their proposed loss function more deeply. An obvious limitation in our assessment is that the performance gains of the full loss function may depend on task complexity (e.g., Table 4 - Can or square results show minimal gains, whereas Transport and Toolhang show an 8% and 12% improvement in success rate, respectively). As we mention above, if only a single model is trained and evaluated on, this is definitely an important limitation.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- The authors' proposed method seems to address issues of iterative sampling from prior generative policies, showing similar or better success rates while requiring fewer model evaluations to generate action sequences\n- The evaluation is extensive, utilizing 53 tasks across three simulation benchmarks to assess the efficacy of this platform.\n- Real robot experiments are performed, which is always an important demonstration for robotic-focused applications.\n\nWeaknesses\n\nOur biggest concern is distinguishing the author’s contribution from prior works. The authors can address this issue by refining their writing, which we found to be verbose in certain sections. We discuss suggestions on this in the “Paper Formatting” section.\n\n Some of the reported results could benefit from additional analysis, such as performing appropriate hypothesis testing to verify that any improvements shown on average are also statistically significant. It is surprising that standard deviations are excluded in some sections for evaluation, suggesting that the authors did not perform multiple runs of the experiments during model training. If the author’s experiments report results from a single trained model for each testing scenario rather than several models trained with different seeds, this is a weakness of the paper. In this case, the authors should clearly state this as a limitation of their results. We suspect it’s unlikely, as this is behavior cloning, but perhaps different policies would lead to measurably different performance results. Additionally, as we stated, this makes it hard to perform a statistical assessment of their model.", "was_revised": true, "final_justification": "I am maintaining my score, which was to accept the paper. Many of my concerns were minor and the author's have addressed them in the rebuttal process.", "rebuttal": "We sincerely thank you for the thorough review of our work, as well as for the constructive suggestions on the paper formatting. Please find our responses below:\n\n------\n**Weakness & Q1:** *Difference between FreqPolicy and various previous works on generative models in visuomotor policies.*\n\n**A:** Our work is built upon the existing flow-based policy workflow. However, existing generative models in visuomotor policies directly utilize generative models from the image generation field, without considering the temporal characteristics of robotic manipulation. In our work, we leverage the temporal information to improve the action generation for a flow-based policy. Moreover, we also reveal that in robotic manipulation, the distribution of frequency components within each action chunk varies over time throughout task execution. This variability arises because robotic manipulation sequences typically alternate between stationary and non-stationary motion phases. We propose an adaptive weighting scheme to capture this structured temporal variation. \n\nWe will emphasize the difference in our revision.\n\n\n------\n**Q2:** *Line 176 - Isn’t this technically preliminary work if you’re using the same loss as other papers?*\n\n**A:** Yes, it is a common objective used in the prior flow‑based approaches, which we cite in Line 178. In FreqPolicy, we adopt it as the base objective and then impose our frequency consistency constraint based on it. For rigor, we have retained the full formulation of this loss in the paper.\n\n\n------\n**Q3:** *Equation 8 - Should the target be a_1 - a_t?*\n\n**A:** The velocity network $v_\\theta$ is meant to match the constant velocity over the interval [0,1], so the target is $a_1−a_0$. This basic formulation is consistent with prior flow‑matching works (e.g., PI0 [1], GR00T-N1 [2]).  We will detail the description of Eq.8 in the revision.\n\n\n\n------\n**Q4:** *Equation 10 line 204: If r <s < u, wouldn’t it be better to explicitly show this in the notation when sampling? I.e. r \\sim U(0, 1), s \\sim U(r, 1), u \\sim U(s, 1)? Or something similar?*\n\n**A:** Good suggestion. In the revision, we will explicitly specify the sampling range for each temporal variable to clarify their interrelationships.\n\n\n------\n**Q5:** *Equation 11, 13, what is “H” here?*\n\n**A:** As noted at Line 160, $H$ denotes the action chunk length, e.g., the number of timesteps for each action dimension.\n\n\n------\n**Q6:** *What research questions are the experiments addressing?*\n\n**A:** The experiments were mainly to validate the effectiveness of our method, which shows that the temporal information is crucial in robotic manipulation tasks. And it outperforms existing works that directly implement generative models from image generation without considering the temporal characteristics of robotic manipulation. \n\nSpeccifically, Our experimental suite is designed to answer 3 questions: (1) whether enforcing consistency in the frequency improves performance over existing multi‑step diffusion and flow‑matching baselines (as shown in Tables 1); 2) whether our frequency consistency loss can be fine‑tuned on large pre‑trained VLA models to yield tangible gains (Table 3); 3) what the individual contributions of fixed frequency consistency and the adaptive weighting are relative to a vanilla one‑step flow‑matching objective (Table 4).\n\n\n------\n**Weakness & Q7:** *Table 1: are the reported improvements statistically significant by an appropriate hypothesis test?*\n\n**A:** Yes. To ensure statistical validity, we followed the protocol of Consistency Policy [3] and evaluated on the RoboMimic benchmark via 3 random seeds. We then report the mean and standard deviation metric for each task.\n\n\n------\n**Q8:** *How many runs were used in the ablation experiments shown in Table 4?*\n\n**A:** We apologize for the omission. For the ablation experiments in Table 4, we ran each variant with three independent random seeds and reported the mean success rate. We will include the corresponding standard deviations in the revision.\n\n\n------\n**Q9:** *No The limitations section (lines 335 - 340) doesn’t list any real limitations.*\n\n**A:** Thanks for the constructive advice. Indeed, regarding the task complexity, in RoboMimic, the Can, Lift, and Square tasks are relatively simple, so most existing methods show similar performance on these simple tasks, whereas ToolHang and Transport are more challenging and offer greater room for improvement. We will explicitly state in the revision that our ablation studies should be conducted across a broader range of benchmarks and clarify the performance gains observed for tasks of varying difficulty.\n\nWe will also consider the potential problems and include them in our limitations, such as our work potentially can be used for diffusion-based policy as well, yet we do not comprehensively study this in this work. And as our work is based on the consistency constraints principle, it consumes more memory during training since it requires twice the forward for two random samples.\n\n\n------\n**Q10:** *Paper Formatting*\n\n**A:**  We will address all formatting suggestions as follows:\n\n1. Shorten the abstract to approximately 20 lines.\n2. Remove any undefined acronyms from the abstract and verify that all acronyms are defined at first use throughout the paper.\n3. Compress Lines 40–53 to retain only the content directly relevant to introducing FreqPolicy.\n4. Remove the redundant sentences in Lines 68–83 of the Method section.\n5. Prune the Related Work to focus on the most pertinent prior studies, eliminating less directly relevant references.\n6. Remove the “3.2 Task Formulation” and “3.3.1 Training Strategy” headings and either decouple or reformat the Task Formulation content for clarity.\n7. Revise Equation 10 to improve its readability.\n8. Redesign Figure 3’s bar plots to center each task for clearer comparison.\n9. Rename “task_1” to “task 1” for improved presentation.\n\nThanks again for this careful and comprehensive review. We will incorporate these changes in the revision.\n\n\n\n[1] Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo\nFusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. pi0: A vision-language-action flow\nmodel for general robot control. arXiv preprint arXiv:2410.24164, 2024.\n\n[2] Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan,\nYu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al. Gr00t n1: An open foundation model\nfor generalist humanoid robots. arXiv preprint arXiv:2503.14734, 2025.\n\n[3] Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou, and Jeannette Bohg. Consistency policy: Accelerated visuomotor policies via consistency distillation. In Robotics: Science and Systems, 2024."}, {"review_id": "ql7Ty7fs4z", "reviewer": "Reviewer_nYM5", "summary": "FreqPolicy accelerates flow-based visuomotor policies by adding a frequency-domain consistency loss. Velocities at different flow timesteps are projected with a DCT and forced to share similar spectra, while an adaptive weighting scheme emphasizes the most mismatched bands. Combined with the standard flow-matching loss, this yields a one-step policy that matches or exceeds multi-step diffusion/flow baselines on various simulated and real-robot benchmarks.", "questions": "1. How much gain comes specifically from frequency projection versus plain time-domain consistency?\n2. Can the loss be applied to diffusion policies in general?\n3. Does the adaptive weighting ever over-focus noisy high-frequency band? how is stability ensured?\n4. In the OpenVLA experiment, did you fine-tune or retrain the policy head from scratch?", "limitations": "The focus is on flow models; generality to other generators and combination with compression techniques are future work. Societal impact is minimal. Limitations are adequately acknowledged.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths. The work is the first to exploit frequency consistency for robot policies, offering a fresh angle on the speed-versus-quality trade-off. Mathematics and implementation details are clear, and experiments are thorough. Simulation tasks plus real-world trials show the one-step model outperforming Consistency Policy, SDM, OneDP, and FlowPolicy while being 5–20× faster than diffusion. The method removes the need for a pretrained teacher and integrates cleanly into a large vision-language-action model, demonstrating practical relevance.\n\nWeaknesses. The contribution, though clever, is incremental on top of existing consistency/flow frameworks; its novelty lies mainly in switching to the frequency domain. Applicability is demonstrated only for flow-matching; it is unclear whether diffusion or discrete-action policies would benefit. Ablations do not fully isolate how much improvement comes from the spectral projection itself, and hyper-parameter sensitivity (e.g., DCT length, loss weighting) is not discussed.", "was_revised": true, "final_justification": "The rebuttal has addressed most of my questions, and the discussions from other reviewers clearly suggest that the paper is above the acceptance threshold. Therefore, I will maintain my rating as weak accept.", "rebuttal": "We appreciate your review of our paper. Please find our responses to the concerns below:\n\n------\n**Weakness:** *The contribution, though clever, is incremental on top of existing consistency/flow frameworks; its novelty lies mainly in switching to the frequency domain.*\n\n**A:** In essence, FreqPolicy remains a flow-based approach for robotic visuomotor policies and is one of a series of methods that leverage consistency principles to accelerate inference. We would like to point out that existing generative-based methods directly leverage existing generative models, such as diffusion and flow-matching from image generation fields, without considering the temporal characteristics of robotic manipulation. Moreover, we also reveal that in robotic manipulation, the distribution of frequency components within each action chunk varies over time throughout task execution. This variability arises because robotic manipulation sequences typically alternate between stationary and non-stationary motion phases. We propose an adaptive weighting scheme to capture this structured temporal variation.  \n\nWe believe that our findings offer the community a new perspective on the temporal characteristics of action generation.\n\n\n\n------\n**Weakness & Q2:** *The applicable of FreqPolicy for diffuion-based polices.*\n\n**A:** Thanks for this valuable question. FreqPolicy was originally developed for flow-based visuomotor policies, using frequency consistency constraints to achieve stronger one-step inference performance. In principle, the same frequency-domain consistency idea can be applied to diffusion-based visuomotor policies, with an appropriately adapted formulation. \n\nTo test its feasibility, we integrated frequency-domain consistency into a diffusion-based policy. Specifically, we built on the Consistency Policy [1], which leverages the classic diffusion model EDM [2]. We enhanced the second training process of Consistency Policy by enforcing a frequency-domain consistency constraint: the spectral representations of transitions from any two timesteps to a common third one must match.\n\nThus, we compare two settings: \n\nA. Original Consistency Policy (a diffusion-based policy work).\n\nB. Consistency Policy with Freqpolicy adaptation, as detailed above. \n\nConcretely, we conducted experiments using the RoboMimic benchmark—leveraging the standard experimental protocol established by Consistency Policy. We run each variant across three random seeds and report the average results below: \n\n| Variant                             | Lift |    Can    |   Square  | Transport |  ToolHang |\n| ----------------------------------- | :--: | :-------: | :-------: | :-------: | :-------: |\n| A | 1.00 | 0.98±0.01 | 0.92±0.02 | 0.84±0.04 | 0.71±0.03 |\n| B | 1.00 | 0.99±0.01 | 0.91±0.02 | 0.87±0.03 | 0.75±0.03 |\n\nThese results demonstrate that adding frequency-domain consistency to Consistency Policy yields clear improvements over the original time-domain loss, particularly on harder tasks (Transport: +0.03; ToolHang: +0.04). This confirms that our frequency-domain paradigm effectively extracts structured temporal information in diffusion-based visuomotor policies.\n\n\n------\n**Weakness & Q1:** *Gain comes specifically from frequency projection versus plain time-domain consistency.* \n\n**A:** Sorry for the confusion. In our paper’s experiments, we demonstrated that frequency-domain consistency alone yields performance improvements over time-domain consistency. \n\nIn Table 4, we conducted ablation studies to validate the effect of frequency-domain consistency, and the relevant data are as follows:\n\n| Variant  | Can  | Square | Transport | ToolHang |\n| -------- | ---- | ------ | --------- | -------- |\n| Model #2 | 0.98 | 0.92   | 0.84      | 0.76     |\n| Model #5 | 0.97 | 0.93   | 0.92      | 0.88     |\n\nModel #2 was trained using only standard time-domain consistency, while Model #5 employed a fixed frequency-domain consistency loss without adaptive weighting. Model #5 outperforms Model #2 on the harder tasks, demonstrating that this gain comes solely from frequency projection. \n\nTo further validate this result, we evaluated both models on all 11 tasks from the Hard and Very Hard splits of MetaWorld, using three independent seeds per task and reporting the mean success rates below. \n\n|                 | Model #2      | Model #5      |\n| --------------- | ------------- | ------------- |\n| Avg. Results | 0.5935±0.0422 | 0.6101±0.0454 |\n\n\nIndeed, this comparison is crucial to reveal the importance of frequency transformation. We will further emphasize these comparisons in the revision.\n\n\n\n------\n**Weakness:** *hyper-parameter sensitivity, e.g., DCT length, loss weighting, etc.*\n\n**A**: For the DCT transform, we apply a DCT-II to the time-series signal of each action dimension, whose length equals the action chunk (denoted by H in the paper). By default, this produces frequency-domain coefficients of the same length. To account for the influence of all frequency components, we do not filter or truncate any coefficients.  Instead, we use the full set of coefficients to compute the losses in Equations 12 and 14. This is a potential investigation into whether the length of DCT can be compressed; we will further study it.\n\nRegarding loss weighting, we also performed ablation studies on the key hyperparameter $\\lambda$. Assuming our learning objective $\\mathcal{L}\\_{\\text{total}} = \\mathcal{L}\\_{\\text{freq}} + \\lambda\\ \\mathcal{L}\\_{\\text{fm}}$, we set $\\lambda=1$ in the results reported in our original paper. We then ran a series of experiments varying $\\lambda$, and the detailed results are shown below:\n\n|                 | $\\lambda=0.03$ | $\\lambda=0.1$ | $\\lambda=0.3$ | $\\lambda=0.6$ | $\\lambda=1$   | $\\lambda=1.2$ | $\\lambda=10$ |\n| :-------------: | :------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------ |\n| Avg. Results | 0.5915±0.0348  | 0.6013±0.0326 | 0.6110±0.0357 | 0.617±0.0301  | 0.6148±0.0382 | 0.6091±0.0381 | 0.6148±0.044 |\n\nThese experiments were conducted on 11 tasks from the Hard and Very Hard splits of MetaWorld, using three random seeds per task and reporting the average performance. We found that, for $\\lambda$ within a typical range, performance fluctuates only marginally. \n\nWe will include these details in the revision to address concerns about hyperparameter sensitivity.\n\n\n\n------\n**Q3:** *Does adaptive weighting ever over-focus noisy high-frequency bands? how is stability ensured?*\n\n**A:** We are not sure whether the reviewer's mention of \"noisy high-frequency bands\" refers to 1) early-stage noise during generation or 2) high-frequency noise inherent in the training data.\n\nFor case 1:\nThe goal of our adaptive weighting is to accelerate convergence during training by emphasizing frequency components with greater discrepancies. Early high-frequency noise is amplified in the initial stages of training through our method, but this noise gradually diminishes as training progresses, with its weighting decreasing as the discrepancies lessen. As a result, the discrepancy reduces over time, and the weighting for these components decreases, ensuring stability.\n\nFor case 2:\nIf the training data contains inherent high-frequency noise, our method may be affected. Excessive noise can indeed impact the final performance. However, I believe that such noise will influence all imitation learning methods to some extent. We will acknowledge this limitation in the paper and consider potential strategies to mitigate such noise in future work, possibly through filtering techniques.\n\n\n------\n**Q4:** *In the OpenVLA experiment, did you fine-tune or retrain the policy head from scratch?*\n\n**A:** We fine-tuned the pre‑trained OpenVLA‑7B checkpoint rather than training the policy head from scratch. Although OpenVLA’s original large‑scale pre‑training uses an autoregressive detokenization objective, OpenVLA‑OFT has demonstrated that one can successfully adapt its weights for simple regression and diffusion heads. Following this precedent, we directly fine‑tuned OpenVLA‑7B for both flow matching and FreqPolicy, and our experimental results confirm that this approach is effective. We will clarify these details more explicitly in the revision.\n\n\n\n[1] Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou, and Jeannette Bohg. Consistency policy: Accelerated visuomotor policies via consistency distillation. In Robotics: Science and Systems, 2024."}, {"review_id": "3RVsU8PyeZ", "reviewer": "Reviewer_fS2J", "summary": "This paper presents FreqPolicy, a consistency flow matching method for efficient generation of visuomotor control actions. FreqPolicy considers the setup where we train a rectified flow matching policy on some demonstration data, and would like to run efficient one-step inference with good task performance. Some background works such as Consistency Flow Matching considers velocity consistency loss given the same endpoint, and FAST considers action representation in the frequency domain with DCT. For efficient inference, FreqPolicy proposes training a flow matching policy, with consistency loss in the frequency domain with focal loss-like adaptive weighting. Experiments in various sim and real environments show that this approach gives us efficient one-step flow matching policy inference with good performance. Ablation studies validate the necessity of each design component in FreqPolicy.", "questions": "- Would this be equivalent to taking a DCT on the action chunks, then taking a regular consistency loss during training?", "limitations": "- Yes, the paper has adequately addressed its limitations.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "- The idea of frequency domain consistency matching for efficient flow matching inference, as well as adaptive weighting, is well-motivated.\n- Various experiments in real and simulated environments show that FreqPolicy achieves good results with efficient one-step inference.\n- The paper compares with a comprehensive set of baselines such as iterative generation with DDPM, DDIM, flow matching policies, as well as consistency models and IMLE.\n- Ablation studies show that the core ideas of frequency domain consistency loss, as well as adaptive weighting, improves performance over vanilla velocity consistency or no weighting.\n- The paper combines two existing ideas (DCT for action representation, and consistency flow matching).", "was_revised": true, "final_justification": "Overall I remain positive about this paper. I agree with the authors that saving an inverse DCT for downstream deployment is a good reason to absorb the DCT into the loss. The additional experiments provided in the rebuttal period make sense.", "rebuttal": "We appreciate your insightful review and feedback on our paper. Below is our response:\n\n------\n**Q1:** *Would this be equivalent to taking a DCT on the action chunks, then taking a regular consistency loss during training?*\n\n**A**: Thank you for raising this insightful question. To explore the differences between these paradigms, we conducted a series of comparative experiments with the following settings:\n\n(A) Directly using a regular consistency loss on the time-domain signals (without any DCT transform).\n\n(B) Applying a DCT on the action chunks followed by a regular consistency loss (per your suggestion). The consistency loss comprises two components: 1) enforcing that velocities at different timesteps match, and 2) enforcing that transitions from any two timesteps to a third remain identical.\n\n(C) FreqPolicy without adaptive weighting (for a fair comparison with setting B).\n\n(D) The complete FreqPolicy (Setting C with the frequency adaptive weighting strategy).\n\nTo maintain experimental rigor within the time constraints, we evaluated this setup on all 11 tasks from the Hard and Very Hard splits of MetaWorld using three independent random seeds (consistent with our main paper) and report the mean success rates across seeds below.\n\n\n\n\n|   Setting    |       A       |       B       |       C       |       D       |\n|:------------:|:-------------:|:-------------:|:-------------:|:-------------:|\n| Avg. Results | 0.5935±0.0422 | 0.6080±0.0454 | 0.6101±0.0454 | 0.6148±0.0382 |\n\n\nThe results show that: \n\n(1) Settings B and C yield nearly comparable performance, showing that both directly applying DCT on the action chunks and applying DCT during consistency loss (our method) are similar, as both methods effectively leverage temporal information.\n\n(2) Both setting B and C outperform setting A, confirming that frequency-domain transforms enhance the extraction of temporal features compared to time-domain consistency alone. \n\n(3) Comparing setting B and C with setting D highlights the effectiveness of the adaptive weighting strategy in FreqPolicy.\n\nAdditionally, we originally chose to use the FreqPolicy auxiliary loss because it adds no extra inference time for inverting frequency-domain actions to time-domain actions compared to directly applying DCT on the action chunks. \n\nWe will incorporate this detailed comparison and discussion in the revision."}, {"review_id": "VGiVavaJYt", "reviewer": "Reviewer_4VTk", "summary": "The paper proposes a one-step visuomotor policy that incorporates temporal knowledge for robotic manipulation. The authors introduce two key innovations: (1) a Frequency Consistency Objective, which enforces consistency between the velocities of action segments across different timesteps in the frequency domain; and (2) an Adaptive Frequency Component Loss, which dynamically emphasizes frequency components with greater discrepancies through adaptive weighting. The writing is clear, and the effectiveness of the approach is validated on MetaWorld, demonstrating superiority over existing one-step visuomotor policies and achieving significant improvements in inference speed for VLA models.", "questions": "Methodology\n\n- Equation 11 employs the type-II DCT. Have the authors conducted ablation studies using alternative similarity functions?\n- Equation 1 enforces the velocity field as a straight trajectory. How sensitive is the proposed objective function to the hyperparameters, like the ratio with respect to L_fm ?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Quality\n\nThe paper presents a well-motivated idea, supported by comprehensive experimental results. The writing is detailed, and the visualizations are clear. The experiments are thorough and effectively demonstrate the validity of the proposed innovations.\n\nClarity\n\nThe paper is clearly written and easy to follow. The figures are well-presented, and the experimental results and ablation studies are clearly designed to validate the proposed innovations.\n\n\nSignificance\n\nThe experimental results demonstrate the effectiveness of the proposed approach. The flow-matching-based policy model is of interest to both academia and industry, and the proposed objective function is broadly applicable to sensorimotor policy networks and VLA models.\n\nIt is strong due to its comprehensive experiments including real-world experiments and reasonable innovation design.\n\nOriginality\n\nThe paper demonstrates strong originality, and the core idea is precise and impactful.", "was_revised": true, "final_justification": "Thanks for your detailed response. I have no further questions.", "rebuttal": "We sincerely thank you for the thorough review and comments on our work. Please find our responses below:\n\n\n------\n**Q1:** *Have the authors conducted ablation studies using alternative similarity functions like DCT-II?*\n\n**A:** Thanks for your valuable question. In the version of FreqPolicy we reported, we initially applied the classic DCT‑II transform to project the time‑series action signals into the frequency domain, which is a common effective approach. Per your suggestion, we further investigated the impact of different frequency transforms on FreqPolicy’s performance by selecting two additional methods: the FFT transform and the DCT‑I transform. \n\nFormally, using a DCT-I transform differs little from DCT-II: one needs only swap the transform operator. With an FFT, the output is a complex spectrum rather than the real coefficients of a DCT. However, since FreqPolicy cares only about the distribution of frequency components and not signal reconstruction, we compute the consistency constraint using only the real parts of the FFT coefficients.\n\nTo balance time constraints with experimental rigor, we conducted ablation studies on a subset of MetaWorld tasks. Specifically, we evaluated all 11 tasks from the most challenging Hard and Very Hard splits. For each task, we ran experiments with 3 random seeds (consistent with our main paper's evaluation protocol). We validated every 200 training epochs and averaged the top‑5 checkpoint success rates per seed. Finally, we report the overall mean success rate and standard deviation across all tasks for each method in the table below.\n\n|                 | Vanilla       | FFT         | DCT‑I        | DCT‑II (Ours)        |\n| :-------------: | ------------- | ----------- | ------------ | ------------- |\n| Avg. Results | 0.5935±0.0422 | 0.619±0.035 | 0.6142±0.039 | 0.6148±0.0382 |\n\nFrom these results, we observe that FreqPolicy is not sensitive to the choice of frequency-domain transform: whether using the classic FFT or various DCT variants, the final performance has changed very little. This is unsurprising given that these transforms are essentially very similar. \n\nMeanwhile, the results of FFT, DCT-I, and DCT-II are better than the vanilla baseline. This result indicates that regardless of the method used to process the time-domain signals, there is a consistent improvement in manipulation tasks. It supports our motivation that leveraging robotic learning with time-domain information is effective.\n\nWe will include these additional experiments and explanations in the revision. \n\n------\n**Q2:** *How sensitive is the proposed objective function to hyperparameters, like the ratio with respect to $\\mathcal{L}_{\\text{fm}}$ ?*\n\n**A:** Following your suggestion, we performed a hyperparameter sensitivity analysis on the loss defined in Equation 15.  Originally, our total loss is $\\mathcal{L}\\_{\\text{total}} = \\mathcal{L}\\_{\\text{freq}} + \\mathcal{L}\\_{\\text{fm}}$. We introduced a hyperparameter $\\lambda$ for $\\mathcal{L}\\_{\\text{fm}}$, yielding the final objective $\\mathcal{L}\\_{\\text{total}} = \\mathcal{L}\\_{\\text{freq}} + \\lambda\\ \\mathcal{L}\\_{\\text{fm}}$. \n\nSpecifically, we tested a range of $\\lambda$ values, 0.03, 0.1, 0.3, 0.5, 1.0, 1.2, and 10, using the same experimental setup and evaluation protocol described above. The results are shown below:\n\n|                 | $\\lambda=0.03$ | $\\lambda=0.1$ | $\\lambda=0.3$ | $\\lambda=0.6$ | $\\lambda=1$   | $\\lambda=1.2$ | $\\lambda=10$ |\n| :-------------: | :------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------ |\n| Avg. Results | 0.5915±0.0348  | 0.6013±0.0326 | 0.6110±0.0357 | 0.617±0.0301  | 0.6148±0.0382 | 0.6091±0.0381 | 0.6148±0.044 |\n\nAs shown in the table, only when $\\lambda = 0.03$ did the performance slightly degrade. The results reveal that across a range ($\\lambda\\in[0.1, 10]$), the success rates vary minimally, demonstrating that FreqPolicy is largely insensitive to $\\lambda$ and does not require meticulous hyperparameter tuning."}]

Author rebuttal:

Meta-review: This paper investigate methods to improve generative policies for robotic control. Iterative generative models in action generation reduce sampling rates, and then the author propose temporal consistency loss to enhance single-step action prediction policies. Applying this loss to a flow-matching policy, they validate it via extensive simulation and real-world experiments.

All reviewers vote for acceptance , and the AC accordingly recommends accepting this submission.
Decision: Accept (poster)