Falcon: Fast Visuomotor Policies via Partial Denoising
Authors: Haojun Chen
Minghao Liu
Chengdong Ma
Xiaojian Ma
Zailin Ma
Huimin Wu
Yuanpei Chen
Yifan Zhong
Mingzhi Wang
Qing Li
Yaodong Yang
Venue: ICML 2025 poster (2025)
Keywords: Diffusion Models
Acceleration
Sampling
Robotics
Motion Planning
Primary area: applications->robotics

Abstract: Diffusion policies are widely adopted in complex visuomotor tasks for their ability to capture multimodal action distributions. However, the multiple sampling steps required for action generation significantly harm real-time inference efficiency, which limits their applicability in real-time decision-making scenarios. Existing acceleration techniques either require retraining or degrade performance under low sampling steps. Here we propose Falcon, which mitigates this speed-performance trade-off and achieves further acceleration. The core insight is that visuomotor tasks exhibit sequential dependencies between actions. Falcon leverages this by reusing partially denoised actions from historical information rather than sampling from Gaussian noise at each step. By integrating current observations, Falcon reduces sampling steps while preserving performance. Importantly, Falcon is a training-free algorithm that can be applied as a plug-in to further improve decision efficiency on top of existing acceleration techniques. We validated Falcon in 48 simulated environments and 2 real-world robot experiments. demonstrating a 2-7x speedup with negligible performance degradation, offering a promising direction for efficient visuomotor policy design.

Falcon: Fast Visuomotor Policies via Partial Denoising
Haojun Chen 12
Minghao Liu 3
Chengdong Ma Xiaojian Ma² 2 Zailin Ma* Huimin Wu? 2 Yuanpei Chen 15
Yifan Zhong
Mingzhi Wang
Qing Li Yaodong Yang'
Abstract
2023; Reuss et al., 2023; Ze et al., 2024b;a; Ravan et al., 2024; Yang al.), thanks to their ability model complex multimodal distributions and maintain stable training dy- namics. Essentially, diffusion policies rely the reverse sampling process of stochastic differential equation (SDE) (Song et al., b; Karras et al., 2022), where actions are itera- tively sampled starting from standard normal distribution. Each sampling step involves drawing a sample from Brow- nian motion distribution, incrementally denoising the initial sample generate the final action. However, the iterative sampling process required for action generation makes these methods computationally slow, particularly when applied to sequential decision-making in real-time environments (Chen et al., b; Janner et al., 2022: Wang et al., b; Yang et al., 2023; Hansen-Estruche al., 2023; Chen et al., a).
Diffusion policies are widely adopted in complex visuomotor tasks for their ability capture mul- timodal action distributions. However, the mul- tiple sampling steps required for action genera- tion significantly harm real-time inference effi- ciency, which limits their applicability in real- time decision making scenarios. Existing accel- eration techniques either require retraining or degrade performance under low sampling steps. Here propose Falcon, which mitigates this prreodperformance trade-off and achieves further acceleration. The core insight is that visuomo- tor tasks exhibit sequential dependencies between actions. Falcon leverages this by reusing par- tially denoised actions from historical informa- tion rather than sampling from Gaussian noise each step. By integrating current observations, Falcon reduces sampling steps while preserving performance. Importantly, Falcon is a training- free algorithm that can be applied as a plug-in to further improve decision efficiency on top of existing acceleration techniques. We validated Falcon in 48 simulated environments and real- world robot experiments. demonstrating a 2-7x speedup with negligible performance degradation, offering promising direction for efficient visuo- motor policy design. The code is available at
To address these challenges, existing work (Song al., a;b; Lu al., 2022; Zhang & Chen) reformulates the sampling process an ordinary differential equation (ODE) and uses numerical solvers to reduce the number of denoising steps (Song et al., a; Lu et al., 2022: Zhang & Chen). However, these solvers often suffer from approximation errors when using very few steps, degrading policy performance (Zhao et al., 2024; Wang et al., a). Alternatively, distillation-based approaches (Salimans Ho; Song et al., 2023; Kim al.; Prasad al., 2024; Wang et al., 2024) accelerate inference via one-step generation. However, they typically require task-specific retraining and may degrade multimodal expres- siveness (Prasad et al., 2024), making them less flexible and unsuitable as plug-ande and-pladd solutions for diverse tasks. An- other related approach is Streaming Diffusion Policy (SDP) (Høeg et al., 2024), which also uses partial denoising for acceleration but requires task-specific retraining and large noise-time buffer, limiting flexibility and memory efficiency.
1. Introduction
Diffusion policies have demonstrated remarkable success in addressing complex visuomotor tasks in robotics (Chi et al.,
In this work, we introduce Falcon (Fast visuomotor policies via partial denoising), a novel approach designed to bridge the gap between acceleration and performance preservation in diffusion-based visuomotor policies. The key insight be- hind Falcon is that sequential dependencies in visuomotor tasks can be exploited to accelerate action generation while maintaining multimodal expressiveness. To effectively lever- age this property, we first utilize the previous action predic- tion as the reference action, then introduce a thresholding
"qqual contribution 'nstitute for Artificial Intelligence, Peking University Nattioal Key Laboratory of General Artificial In- telligence, BIGAI 'School of Electronic Engineering and Com- puter Science, Peking University "School of Mathematical Sci- ences, Peking University 'PKU-PsiBot Joint Lab. Correspon- dence to: Qing cdylan.liqing @ irngiin@@maii..om... com,, Yaodong Yang <yaodong. yang @ pku.edu.cn..
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Falcon: Fast Visuomotor Policies via Partial Denoising
mechanism combined with one-step estimation evaluate which partial denoising actions are dependent on current timesteps in parallel. Using a temperature-ssaled softmax, we then select the most suitable partial denoising action to continue the sampling process, preserving both perfor- mance and efficiency. By avoiding the conventional practice of starting the denoising process from a standard normal distribution at each decision step. Falcon begins from partial denoised actions derived from historical observations, sig- nificantly reducing the number of sampling steps required. Importantly, Falcon is a training-free method, which allows it to be applied as plug-in in module, enhancing the efficiency of existing ceffusiio-bbsed policies without additional train- ing or extensive modifications. It integrates seamlessly with solvers like DDIM (Song et al., a) and DPMSolver (Lu et al., 2022) to further enhance acceleration.
is corrupted K timesteps, resulting in:
((0x mo) = N (aras v Vanato (1 Cxk oit) IT)
(1)
where ār = ITi=1 ag After K steps, 2K becomes nearly
Gaussian. The reverse process reconstructs an by iteratively denoising, modeled as
po (2 xxx) =
(2)
where μi is the predicted mean, of is fixed according to the forward process, ar is the variance schedule in the forward diffusion process.
Diffusion policy (Chi et al., 2023) extends diffusion models as a powerful policy for visuomo- tor tasks. At timestep i, diffusion policy takes the latest To steps of observation O, as input, predicts Tp action se- quence A, and executes To action sequence. The action sequence generation process is a conditional denoising diffu- sion process modeled by po (A,)O)), where A, RTT,DDD, D. and O, E R.fxDD,, D. and D. represent the action and observation's 's dimension respectively. Specifically, starting from a pure Gaussian noise sample, diffusion policy lever- age the noise prediction network Eg to predict and remove noise at each denoising step, iterating for K steps to gen- erate a clean sample AQ. The action sequence generation process is described as Eq. 3, where At is partial de- noising action in the timestep with noise level k, Z is standard Gaussian random variable.
Diffusion Policies.
We evaluate Falcon in both simulated and real-world envi- ronments. In simulation, we test Falcon across 48 tasks span- ning five widely used benchmarks, including RoboMimic (Mandlekar et al., 2022), RoboSuite Kitchen (Gupta et al., 2020), BlockPush (Shafiullah et al., 2022), MetaWorld (Yu al., 2020) and Maniskil12 (Gu al., 2023). For real-world experiments, we deploy Falcon on a dual-arm robot plat- form in two manipulation tasks dexterous grasping and high-precision insertion. In both settings, Falcon achieves a 2-7x acceleration in action sampling while preserving high task success rates, validating its effectiveness and practical applicability.
In summary, this work makes three main contributions: First, we introduce Falcon, which leverages sequential de- pendencies in visuomotor tasks to perform partial denoising, significantly reducing the number of sampling steps while preserving the ability to model multimodal action distribu- tions. Second, Falcon functions as a training-free plug-in policies, integrating
A K-
A
1 - ak
co (O..
+ o
Vas
VI ā
(3)
The training loss is
L(0) = Ex,A [||e ED (O,, A, k) [)]]"]
that enhances existing diffusion-basee seamlessly with solvers like DDIM and DPMSolver to fur- ther accelerate action generation. Third, demonstrate Falcon's 's effectiveness across 48 simulated environments and 2 real-world robot tasks, achieving 2-7x speedup in inference while maintaining high action quality.
(4)
A = V au A. + v1 V
Acceleration techniques in the diffusion-based mod- els. Some popular works (Song al., b: Karras et al., 2022: Song al., a; Lu et al., 2022) interpret the diffusion model as an ODE. Specifically, DDIM (Song et al., a) generalizes the Markov forward process in DDPM to non-Markov process, allowing the use of shorter Markov chains during sampling. The iterative steps of its sampling process can be rewritten in form similar to Euler integration, which is a discrete solution process for a specific ODE. DPMSolver (Lu al., 2022) accelerates sampling by analytically com- puting the linear part of the diffusion ODE solution and using an exponential integrator to approximate the nonlinear part. But these methods can reduce the quality degradation of few-step sampling (Shih et al., 2024). SDP (Høeg et al., 2024) improves the sampling speed by generating partial denoised actions with different noise levels, but relies on
2. Preliminaries
In this section, we briefly introduce the diffusion mod- els, how the diffusion models are used for diffusion poli- cies in visuomotor tasks, the acceleration techniques in the diffusion-based models, and terminology.
Diffusion Models. Diffusion models, such as DDPM (Ho et al., 2020), are generative models that learn data distri- butions by progressively corrupting data through noise in forward process and then reconstructing it in a reverse denoising process. In the forward process, data point æ0
2

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Falcon: Fast Visuomotor Policics via Partial Denoising
handcrafted noise schedules and large buffers, limiting its flexibility. Consistency Policy (CP) (Prasad al., 2024) distills pretrained diffusion policy into a one-step sampler via self- consisteeen on its probability flow ODE. While fast, this requires retraining and may compromise multimodality.
We derive the posterior expectation, which serves as the one- step estimation of the partial denoised actions a tT, denoted by a -.r-I.+T
We write [a, b) to denote the set {a, a + 1.... . 1]. aash to denote the set {æ, : E [a, b)} and [K] to denote the set {1...., KK. We define A, [ T.+T. where a is the executed part and d1+Ta4T7, is the unexecuted part in timestep L.
d Tu TT = -la
Ta+T, O a a
(6)
This estimation is conditioned the current observation O,, formulated in Proposition 3.1. Here, < represents historical decision step and ht denotes the noise level of the partial denoised action.
3. Falcon: Fast Visuomotor Policies via Partial Denoising
The dependency between past and current actions is mea- sured by the Euclidean ||a"; +T, ll2 be- tween à - TT.r-T.,+T, +T, and the reference action at.c-T,+77 If this distance falls below predefined threshold e, the par- tial denoised action a is selected, as it is likely to converge to the desired action Out+TT
In this section, we will introduce the core principles of Fal- con. First, we present the way of leveraging previously gen- erated action sequences de:--T,+;; based on the model's s confidence in its prior predictions. Second, we describe the thresholding mechanism that determines which partial de- noised action from past timesteps serves as the initialization for the current denoising process. Lastly, we outline the implementation details of Falcon.
Proposition 3.1. (Tweedie's 's formula for denoising) Let be random variable with a probability distribution p(æ), and Let Dg == x + az, where z ~ N (0, Ip) and o O is a known scalar. Then, the best estimate @ of x mean squared error, given the noisy observation Fo, given by the formula:
3.1. Reference Actions
Falcon first leverages the unexecuted action sequence den-T.+Ty- predicted predicted from the previous observation O,-T,, as a reference for denoising the current action sequence ar:+T, at time step f. This approach is motivated by our observation that the Euclidean distance [ar.-T.+T,
to := Ep(rlln,) = E Øol = Io + + Vo, x.. log (xx)
Remark 3.2. In the context of DDPM (Ho et al., 2020) whose diffusion forward process is Dt N' (Vārzo (1 Tp). Tweedie's formula can be rewrit- ten as
ar:t-T.+T, TT,T,II between ā..t-T.+ +T, and ac:t-T,+7, exhibits high probability density near zero (see Fig. 2), indicating that TT.tT.,T, closely approximates Qua-T.+T, in most cases.
E Ioo
21] = (x: + (1 σ: V 2, logp /Vo.- (8)
Furthermore, as shown in Eq. 4, diffusion policies take the latest To observations as input and train to output the future T, action sequence A, = [Qr:+T. (U+II::4T.+Tppl This training paradigm provides confidence that the predicted action sequence T.=-T.+T, E A, is sufficiently accurate for use reference. Additionally, Falcon incorporates the score vector field V log p( (,)..). computed from the current observation O, to guide the denoising process of the partial denoised action at the current time step, ensuring more precise and stable action refinement.
Since VA logp (A} O,) = -i- (O..A
(Ho et al., 2020; Chi et al., 2023), we can obtain the posterior expectation by
Fla To T. Oz, ar: - TTTTT
a - T.+T, VI (O,, a -T.TTT.TT,- (9)
V
Finally, we define the set of candidate actions as S {ai. ||a - ār:r-T.TT, < e, < E [K]} and sample the starting point through the fol- lowing distribution
3.2. Thresholding Mechanism
To determine which partial denoised action from historical observations should be used as the initialization for denois- ing at the current timestep, we employ Tweedie's approach (Efron, 2011; I; Chung et al.; Kim & Ye, 2021). Given that the forward process of diffusion policy follows
ft -k;1s
(aittT)) /x)
cxp
P (A = a.r+tT))
(-kyls a ( /K()
exp
kE[K]
A = VorA + VI 0z2 2~ s(,
(5)
(10)
3

---

Falcon: Fast Visuomotor Policies via Partial Denoising
Overview
Reference Action
Thresholding Mechanism
A
Uneseccuted action 0ee-2
Patimation
Desired action Opi
p(an O2 x,)
Mundoo Splal 2. vol
Pl O2)
( O,)
Unrsecatod action Aeg 121
Diffasoo Polly
Palcon
Partinl cleniseed action deaeig A
Partial denoised action o dier-12
Latent Thuffer
Current soore at lopp(A) O,)
expl -klo (af x 5.)
A51
drr Earo 8,t0 (122n
1))
Buffer
12.10
Figure 1. Method Description Falcon begins denoising from historically generated partial denoised actions rather than normal distribution, requiring less than k, steps to produce the action sequence A,. The process involves 2 steps: setting reference actions and retrieving partial denoised action sequence A from the latent buffer B to start denoising through thresholding mechanism. b) Reference Action. Falcon uses unexecuted actions te:-TT.+T, from the previous step t - Ta as the desired action Ges-T, selecting a partial denoised action from the starting point. c) Thresholding Mechanism. Falcon evaluates all actions in B in parallel, identifying those close to the reference action after one-step estimation, and samples the starting point A." based on the noise level.
In practice, can't 't store all the partial denoising action generated from historical observations because of the lim- ited GPU memory. Considering that earlier actions are generally less relevant to the current decision and that we aim to maximize efficiency, we designed a priority queue to implement the latent buffer. Partial denoising actions a with earlier timesteps т and higher noise level are given priority for removal from the queue. To prevent Falcon from repeating previous actions, we introduce kimin and filter out partial denoised actions with k < kmin dur- ing selection. This ensures that only actions with sufficient noise levels are used in the iterative sampling process.
Robomimic
400
Can
Lift
Squaro
300
Transport
Density
200
100
0
0.000
0.008
0,016
0.024
Euclidean distance
timestep = 1, where no prior information available, Falcon directly samples a from Gaussian distribu- tion (0, I). and iteratively samples conditional on O, by Eq. like DDPM (Ho et al., 2020) Line 7. During this process, Falcon iteratively performs denoising and stores resulting partial denoised actions a in a latent buffer B on Line 5. This latent buffer records all partial denoised actions generated during the sampling process.
Figure 2. Probability density estimation of [ +T
aus--T, +T, [|2- as.-TT,T, is nearly the same as dua- +T, since the majority of Fuclidean distances between Ter-Ta+T +T, and Qs:--Ta+ +T, are concentrated within the range of less than 0.015. Samples are collected by diffusion policy across four Robomimic environments (Can, Lift, Square, and Transport), where 200 trajec- tories generated for each environment.
where is the temperature scaling factor and Ls(+) is the indicator function of set S. Since exclusively sampling from the latent buffer may result in idle actions, we introduce exploration rate ö, inspired by the e-greedy method from Reinforcement Learning (Sutton& & Barto, 201 8). With prob- ability 6, we sample from the standard Gaussian distribution instead, ensuring more diverse behavior.
k-1
1
(oii-T,, -C) tOzz
a autTT,
Vax
C 1 1- akk
Cg( at at.
(1I)
V1 ak
z IO,I)
For timestep / > 1, Falcon utilizes one-step estimation according to Eq. 9 on all stored samples a k; a in the latent buffer B conditioned on the current observation O,, which is the most but can be efficiently parallelized. Line 13 obtains the partial
3.3. Implementation Details
In Algorithm 1, we present the pseudocode of Falcon, in- cluding threshold e, exploration rate and latent buffer B.
part of the algorithm

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Falcon: Fast Visuomotor Policies via Partial Denoising
Lift
Can
Square
Transport
Tool hang
Lift
Can
Square
Transport
ToolHang
ph
mb
ph
mh
ph
mh
mh
DDPM
1.00
0. 0.95
0.97
0.91 20.07
0.85
0.80
0.5240.49 0.65
DDPM+Falcon
1 1.00
0.97 1.
0.97
0.82 0.95
0.85
0.66
0.55
SDP(DDPM)
10.00
0.97 0.
0.94
0.80 0.88
40
0.81
0.46
0.58.10.50
DDIM
1.00 10000 00
0.99
0.
0.92
0.87 .3
0.7910.40
0.63
0.55+0.50
DDIM+Falcon
1. 1000
1.00 00
1.00
0.9810.14
0.91
0.85 36
0.83
0.63
0.54+
DPMSolver
0.
0.95
+.97
0.97
0.84 0.
36
0. 0.70
0.55
0.58++..99
DPMSolvar+Falcom
0.98
0.96 20
0.96
0.98
0.90 2.
0.7440.44
0.54
0.56:1.44
Table Success Rate in Robomimic. We present the success rate with 200 evaluation episodes in the format of (mean of success rate) + (standard deviation of success rate). Our results show that Falcon matches the original methods.
denoising action sequence, and Line 14 sets it as the starting point of the rest of the iterative sampling process.
Falcon can also be compatible with other diffusion policy acceleration techniques by replacing the solver in Line and Line 18 with other SDE/ODE solvers like DDIM (Song et al., a) and DPMSolver (Lu et al., 2022). The detailed pseudocode is in the Appendix B.
Consistency Policy
DDPM Falcon
Algorithm Falcon
Require: Diffusion model 6o with noise scheduler âs, variance 0g threshold e, exploration probability latest To observa- tions O,, latent buffer B, temperature scaling factor s.
Figure 3. Multimodal behavior. At the given symmetric config- uration in the PushT task, DDPM+Falcon (right) preserves mul- timodal behaviors by producing diverse reaching trajectories. In contrast, the Consisteney Policy (left) shows bias toward one mode. Each line represents rollout trajectory, plotted from 20 trajec- tories sampled at the same state.
1: for L 1.....,
2: i then
3:
a I)
4:
for K.....
5:
B- B-BU (a
6:
z ~~ ~~ if
else +- 0
4. Experiments
7:
Sample ax according Eq.
8:
end for
Our experiments aim to address four key questions: (1) Can Falcon accelerate diffusion policy, and does it further enhance speed when integrated with other acceleration al- gorithms (Section 4.3y? (2) Can Falcon effectively accel- erate diffusion policies in real-world robotic settings while preserving performance (Section 4.5)? (3) Does Falcon maintain its acceleration advantage in long-sequence tasks (Section E.3)? (4) Can Falcon retain the ability to express multimodality while achieving speed improvements (Sec- tion 4.6)?
end
10:
then Compute one-step estimation a Tur--Ta TT+ via Eq. 9.
11:
12:
S s (at.++T, =|a"
ā a <
13:
Sample a K. air+T, according to Eq. 10 2 4T for K k...... do
14:
a,4TT,
15:
16:
B BU (ouaar..a J. ~ N(O, I) if 1, else x - Sample k- 4, according to Eq. 11
17:
19:
end for
20:
end
4.1. Metrics
21:
end for
Our experiments are primarily evaluated by two metrics. The first the success rate, which measures the mean and
5

---

Falcon: Fast Visuomotor Policics via Partial Denoising
Lift
Can
Square
Transport
ToolHang
ph
mh
ph
mh
ph
mh
ph
mh
ph
DDPM
100.0
100.010.0 100.0
100.0
100.040.0
100.020.0
100.0
100.0.o.0
100.0
DDPM+Falcon
12.
2..6
35.2 1.3
20.3
37 37.1
20.5
16.9
48.0 48.0
33. 33.61132
SDP(DDMM)
50.0=0.0
50.02o.0
50.0
50.020.0
50.0
50.0
50.0+0.0
50.0zo.0
50.0z0.0
DDIM
16.020.0
16.0
16.020.0
16.020.o
16.0
16.020.0
16.0
16.02o.o
16.0.0.0
DDIM+Falcon
7.5
7.7
6.5
7.6g2.1
7.6.1.0
7.2-1.2
10.5.2.0
9.0
10.1
DPMSolver
16.0.a.0
16.0
16.0.o.o
16.0
16.0
16.0.0.o
16.0
16.0go.o
16.020.0
DPMSolver-Falcon
6.4
10.4:2.0
14.7=0.8
10.9
14.4
7.8_0.9
11.0+1.2
12.8+1.4
12.141.8
Table NEE in Robomimic. We present the NFE with 200 evaluation episodes in the format of (mean NFE) + (standard deviation NFE). Our results show that Falcon drastically reduces the denoising steps compared with the original methods.
standard deviation of task completion across all trials. The second metric is generation time, quantified in terms of the Number of Function Evaluations (NFE) (Prasad et al., 2024). Since the inference cost for these models is primarily deter- mined by NFE, and given that the network architectures are kept constant across experiments, NFE serves as a reliable indicator of relative performance. This metric effectively captures the inference cost, unbiased by GPU imbalances, and allows for fair comparison across methods.
4.3. Falcon is Plug & Play Acceleration Algorithm
In the first experiment, we examine algorithm on the Robomimic benchmarks. We present the success rates, NFE, and speedup for both the original diffusion models, their Falcon-enhanced counterparts, and SDP in Table 1, Table
2, and Table 4, respectively. To better understand Falcon's 's impact, we categorize the tasks into simpler and more com- plex ones based on the level of fine-grained manipulation required. In the Robomimic benchmark, Lift and Can relatively simple tasks, while the remaining tasks, Square, Transport, and ToolHang, are more complex.
Ablation Epsilon
Ablation Exploration
1.00
0.8
3,0
6.0
Soare
0.98
Score Specdup
2.4
Sppeedlup
Score
0.6:
Score Specdup
Simple tasks. The Lift and Can tasks involve smooth and predictable movements. In Lift, the robotic arm moves downward to grasp a small cube and then upward lift it, with minimal variation in action within each phase. Can follows similar pattern, where the robot picks up coke can from a bin and places it in target bin, primarily involv- ing steady translational motion. Both tasks exhibit strong dependencies between consecutive actions, which makes them ideal for Falcon acceleration. As shown in Fig. 13 (left), Falcon starts denoising with low noise, significantly speeding up the process. Table 4 shows Falcon achieving 7x speedup in Lift and 2-3x in Can, demonstrating its efficiency in structured motion tasks
0.96
3.0
0.94
0.2
1.2
1.5
0.92
0,000
00005
0x05 Epsilon
0.030
0.100
0.00
01.25
0.30
0.75
Explontion Rote
Figure 4. Left: Ablation analysis threshold The effect of varying the threshold on both the score (red circles) and speedup (blue squares), demonstrating the trade-off between performance and speed as e changes. Right: Ablation analysis on exploration rate 8. The score (red circles) increases sharply with 8 and then stabilizes, while the speedup (blue squares) decreases as the ex- ploration rate rises. This study highlights the balance between exploration and exploitation.
Difficult tasks. Square, Transport, and ToolHang are more complex and involve finer, more precise movements. For example, in the Transport task, two robot arms must work together transfer a hammer from closed container on a shelf into target bin on another shelf. One arm retrieves the hammer from the container, while the other arm first clears the target bin by removing trash. Then, one arm hands over the hammer to the other, which must place it in the target bin. These actions are dynamic and entail larger changes between consecutive movements. As a result, Falcon can't 't't start denoising actions with a low noise level (see Fig. 13 (right)). Consequently, Falcon's speedup not as pronounced as in simpler tasks. However, even in these more challenging scenarios, as shown in Table 4, Falcon still
4.2. Simulation and Real- World Environments
We evaluate Falcon across diverse simulated and real-world enviromments to demonstrate its generality and effectiveness. Simulation benchmarks include RoboMimic (Mandlekar et al., 2022), Franka Kitchen (Gupta et al., 2020), Block- Push (Shafiullah et al., 2022), PushT (Chi et al.. 2023), MetaWorld (Yu al., 2020), and ManiSkil12 (Gu et al., 2023), covering short- and long-horing morizooizon tasks, well as multimodal behavior tasks. For real-world evaluation, we deploy Falcon on a dual-arm robot platform in two manipu- lation tasks: dexterous grasping and sigh-prreision insertion. Details of simulation settings, network architectures, and hardware platforms are provided in the Appendix A.
6

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Falcon: Fast Visuomotor Policies via Partial Denoising
Square
Transport
ToolHang
pb
mb
ph
mh
ph
mb
ph
mh
ph
DPMSolver*
1.00 ou
0.94 2.
1.96
2. (L10.6%)
0.58 (135.66%
0.69 as (16.8%)
0.54
1.53
DPMSolver-Falcon
0.98 14
0.
0.96
0.98
0.94
0.90
0. 0.74
0.54
0.56
Table Reduced-step DPMSolver. We report the success rate across 200 evaluation rollouts in the format of (mean + std), DPMSolver* uses the same number of sampling steps as NEE of DPMSolver( 16 steps)+Falcon. Entries marked with & indicate performance drops compared to DPMSolver(16 Faapo). The percentage drop computed as (Falcon score DPMSolver* score) /Falcon score x 100%.
Lift
Can
Square
Transport mh
Tooll
ph
mh
ph
mh
ph mh
ph
DDPM+Falcon
7.78 19
4.07 20.23
2.84
2. 2.70 0.03
1.
2.14
2.10 16
2.86
SDP(DDPM)
2.00
2.00
2.00
2.00 2.00
2.00
2.00
2.00 2.00
DDIM+Falcon
20.0
2.08
o.11
2.15
2.15
2.27=0.33
1.57
1.88
1.61:0.220
DPMSolver+Fulcon
2.48
1.60
1.09
1.51
1.11
2.09
1.4840.17
1.27=0.13
1.3240.12
Table Speedup in Robomimic. We present the speed with 200 evaluation episodes in the format of (mean of speedup) + (standard deviation of speedup). Speed for X+Falcon is calculated by NFE of X / NFE of X+Falcon.
achieves around 2x speedup while maintaining performance close to that of the original models.
grasping to pick up the blue object placed table. Falcon is deployed on top of DDPM without any additional retrain- ing. We compare Falcon+DDPM against SDP(DDPM) and DDPM alone. As shown in Table 5, Falcon achieves a 3.07x speedup over DDPM and outperforms SDP in both runtime (0. 145s vs. 0.233s) and CPU memory usage (3730MB vs. 3808MB).
We further evaluate Falcon in MetaWorld environments us- ing 3D Diffusion Policy (Ze et al., 2024b). The architecture follows the same setup as 3D Diffusion Policy, with a DDIM scheduler using 10-step discretization. As shown in the Ap- pendix C, Falcon achieves 3-4x acceleration on top of DDIM, further demonstrating its effectiveness in accelerat- ing existing diffusion- based policies across different tasks.
High-Precisioe Insertion. The second task involves in- serting square stick into a tall cylindrical chip can. This task requires precise 3D alignment; even slight deviations in height or angle result in failure. Falcon is again applied as a plug-in to DDPM. We train both DDPM and SDP using 50 human demonstrations. As shown in Table 6, Falcon matches DDPM in 90% success rate while being 2.86x faster and more memory efficient.
4.4. Falcon mitigates performance degradation in reduced-step solvers
To further demonstrate Falcon's compatibility with exist- ing acceleration techniques, we compare Falcon-enhanced DPMSolver with a reduced-step DPMSolver baseline (de- noted as DPMSolver*), where both use the same number of denoising steps. As shown in Table 3, DPMSolver* exhibits significant performance drops in several tasks when the number of steps is aggressively reduced (c.g.. 10.6% in Square_ph -5.6% in Square_mhy mmh), while Falcon maintains high success rates with the same number of steps. These results highlight Falcon's 's ability to recover performance in low-step regimes and demonstrate its practical value as a aodg-and-pay solution to boost efficiency without sacrific- ing accuracy.
These results confirm that Falcon not only accelerates dif- fusion policy inference in simulation but also effectively transfers to real-world robotic applications.
4.6. Falcon retains the ability to express multimodality policy
We further evaluate Falcon in the PushT task under a sym- metric configuration, where multimodality can be visual- ized directly through trajectory diversity. As shown in Fig. 3, DDPM+Falcon produces diverse reaching trajec- tories, while Consistency Policy exhibits mode collapse, predominantly favoring a single direction. This qualitative difference demonstrates Falcon's 's ability to maintain diverse outputs even in symmetric settings, without retraining.
4.5.
Evaluation of Falcon
To validate the practicality of Falcon beyond simulation, we evaluate it in two real-world robotic manipulation tasks: Dexterous Grasping (Fig. 5) and Square Stick Insertion (Fig.
Dexterous Grasping. The first task involves dexterous 6).
7

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Falcon: Fast Visuomotor Policies via Partial Denoising
Figure Real-world Dexterous Grasping with DDPM+Falcon. Top row (red): head camera view (RealSense D435). Bottom row
(green): wrist camera view (RealSense D405C), The sequence shows the successful execution of grasping using Falcon- accelerated policy,
Method
Speedup
NFE
Run Time (6)
GPU MEMS (MB)
CPU MEMS (MB)
Success rate
DDPM
1.0x
100.00 1 0.00
0.43 1 0.01
3730.17 0.56
3737 06 1 2.63
100%
SDP(DDPM)
1.86x
52.16 = 2.18
0.23 = 0.01
3733.11 11 + 2.68
3808.67 + 7.68
100%
Falcon+DDPM
3.07x
30.98
1.33
0.14 = 0.01
3730.19 0.56
3746.67 + 3.03
100%
Table 5. Dexterous Grasping in Real-world Environment. Each entry is evaluated with 20 rollouts in the mean + standard deviation format. DDPM and SDP(DDPM) trained with 1000 epochs with 37 collected expert demonstrations. Our results show that Falcon outperforms SDP in runtime reduction with lower memory usage.
4.7. A Ablation analysis: sensitivity to the threshold
reference action is not perfectly aligned with the desired action A,, errors accumulate over time, causing Falcon to generate incorrect actions, resulting in significant drop in score. As increases, Falcon samples more diverse actions, improving score by reducing the accumulation of errors. However, further increasing ō results in decrease in speed, as the model becomes less reliant on the history of denoised actions and starts exploring more random samples from the normal distribution.
We explore how the threshold influences Falcon's 's perfor- mance in the Square ph task. The threshold e determines the confidence in reusing partial denoised actions toward the desired action a1:+T, guided by VA log (Af O,). evaluations using range of values: {10-4,5 55x 10-3, 8 x 10-3 10 2, 10-2, 10-2, ,,8 x 10 10-2, 2, As shown in Fig. (left), when is small, Falcon is highly selective about using partial denoised ac- tions, leading to slow denoising because Falcon frequently samples from the standard normal distribution. This re- sults in no significant improvement. When is large, Fal- con becomes overly confident in the partial denoised ac- tions, leading selecting many incorrect actions that do not align with dt,,, which causes sharp drop in Score. The optimal range of lies in the middle, where Falcon can achieve speedup without sacrificing task performance, demonstrating that a balanced e allows Falcon to maintain both efficiency and accuracy.
We conducted 50
4.9. Ablation analysis: sensitivity to selection mechanism
We evaluate Falcon's 's partial denoised action selection mech- anism. Falcon adaptively selects partial denoised actions based on e and the one-step estimation, estimating whether each action can be denoised to Aç. We compare this with the case where Falcon is fixed to always sample from ac- tions with noise levels of K/2 K/5 (represented by the red and green bars), without considering the adaptive selec- tion mechanism. Fig. 7 (left) shows a significant decrease in Score when Falcon is restricted to these fixed actions, demonstrating that not leveraging the selection mechanism compromises performance. This confirms that Falcon's 's Se- lection Mechanism is crucial for ensuring accurate action choices, allowing the model to maintain high performance while accelerating the denoising process.
4.8. Ablation analysis: sensitivity to exploration rate ò'
We assess the impact of the exploration rate 8 on Falcon by running 50 evaluations on the Transport ph task, using E {0.001, 0.05, 0.0625, 0.076, 0.1, 0.2, 0.33, 1). Exploration Rate 8 controls the probability that Falcon starts denoising from (0, I) rather than the partial denoised action. This exploration mechanism allows Falcon to fill the latent buffer with more partial denoised actions, increasing exploration and preventing over-reliance on the reference action. As shown in Fig. 4 (right), when is set to 0, Falcon primarily aligns with the reference action at each step. Since the
5. Related Work
Diffusson policies are widely used for modeling complex behaviors in robotics. 3D Diffusion Policy (Ze al., 2024b;
8

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Falcon: Fast Visuomotor Policies via Partial Denoising
Figure Real-word world Square Stick Insertion DDPM+Falcon, Top row (red): head camera view (RealSense D435). Bottom row
(green): wrist camera view (RealSense D405C), The sequence shows the successful execution of insertion using Falcon-accelerated policy.
Method
Speedup
NFE
Run Time (s)
GPU MEMS (MB)
Success
DDPM
1.0x
100.00
0.43
7644448
90%
SDP(DDPM)
1.95x
50.00+0.00
0.22+0.01
3743. 2843.2881122
85%
FaPMon+DDPM
2.86x
25.37 17.10
0.15
3731.50++500
90%
Table Square Stick Insertion in Real-world Environment. Each entry is evaluated with 20 rollouts the mean + standard deviation format. Falcon set with T₃ 32, To 16 1, 0.02, [3]
20.
Ablation of Sclection Mechanism
Model (Kim et al.), enabling pre-trained diffusion policy to generate actions within a few steps. ManiCM (Lu et al., 2024) extends this approach 3D robotic tasks. OneDP (Wang al., 2024) and SDM (Jia al., 2024) introduce score-based distillation reduce performance degradation. However, these methods require task-specific training, lim- iting adaptability. Streaming Diffusion Policy (SDP) (Hóeg et al., 2024) uses partial denoised action trajectories, similar to Falcon. However, SDP requires task-specific training and lacks compatibility with general acceleration techniques.
1.2
4.5
Falcon
Falcon
0.8
Score
Specdhp
1.5
0.0
Comparison
Speedup Comparison
Figure 7. Ablation analysis on selection mechanism. This figure compares Falcon's performance with diffcrent partial denoised action selection configurations. Restricting Falcon to choose ac- tions with fixed noise levels (K/2, K/5) significantly reduces score, highlighting that the selection mechanism ensures accurate action choices while accelerating denoising maintaining high perfor- mance.
In contrast, Falcon is a training-free acceleration framework that leverages sequential dependencies improve sampling speed while preserving multimodal expressiveness. It inte- grates seamlessly with DDIM and DPMSolver, making it flexible, plug-and sogg-an--pay solution across various robotic tasks.
6. Conclusion
Ke et al.) improves visuomotor performance by integrating 3D visual data from sparse point clouds. Large-scale models like Octo (Team et al., 2024) and RDT (Liu et al., 2024) scale diffusion policy parameters to build vision- Iasion-language-- action foundation models. However, their high computa- tional cost limits real-time applications, necessitating accel- eration techniques.
This paper introduces Falcon, the first diffusion policy ap- proach to accelerate action generation in complex visuomo- tor tasks by leveraging inter-step dependencies in decision- making. Empirical results confirm that Falcon outperforms strong baselines, such as DDIM and DPMSolver, in terms of speed without sacrificing accuracy or expressiveness. Over- all, Falcon offers simple yet effective solution for real-time robotic tasks, providing efficient action generation for com- plex visuomotor environments. However, one limitation of Falcon is that it does not use single set of parameters to accelerate all tasks, since different environments may require task-specific parameter tuning. Future work will ex- plore adaptive parameter selection strategies to enhance its generalizability across diverse robotic applications further.
To speed up inference, ODE-based sampling methods like DDIM (Song al., a) and DPMSolver (Lu et al., 2022) reduce denoising steps, while ParaDiGMS (Shih et al., 2024) leverages parallel computing to accelerate sampling on GPUs. Distillation- based methods further improve ef- ficiency by training models for single-step inference. CP (Prasad al., 2024) builds on the Consistency Trajectory

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Falcon: Fast Visuomotor Policics via Partial Denoising
Impact Statement
Hansen-Estruuch, P., Kostrikov, I., Janner, M., Kuba, J. G., and Levine, S. Idql: Implieit q-learning actor- critic method with diffusion policies. arXiv preprint arXiv:2304.1033 2023.
Falcon removes the efficiency bottleneck diffusion-basee -based visuomotor policies, making real-time action generation feasible for robotic manipulation, autonomous navigation, and interactive AL. By enabling fast, high-quality decision- making in long-horizon tasks, Falcon expands the practical use of diffusion policies in real-world robot applications.
Ho, J., Jain, A., and Abbeel, P. Denoising diffusion proba- bilistic models. Advances in neural information process- ing systems, 33:6840-6851, 2020.
Høeg, S. H., Du, Y., and Egeland, O. Streaming diffusion
Acknowledgements
policy: Fast policy synthesis with variable noise diffusion models. arXiv preprint 24Xx::2406008806 2024.
This work sponsored by National Natural Science Foun- dation of China (62376013). We sincerely thank Tianjie He for his invaluable support in setting up the hardware platform used in our real-world experiments.
Janner, M., Du, Y., Tenenbaum, J., and Levine, S. Planning with diffusion for flexible behavior synthesis. In Interna- tional Conference on Machine Learning, pp. 9902-9915. PMLR, 2022.
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Falcon: Fast Visuomotor Policies via Partial Denoising
A. Experimental Setup
Head Camera (RealSense D4435)
7-DoF' Robotic Arm (RealMan RM75-6F)
Wrist Camera (RealSense D405C)
6-DoF' Robotic Hand (PsiBot GU-R)
Operational Workspace
Figure The hardware platform. Left: Task Dexterous Grasping. Right: Task for inserting stick into a chip can
A.1. Simulation Environments
Robomimic (Mandlekar et al., 2022) is a large-scale benchmark designed to evaluate robotic manipulation algorithms using human demonstration datasets. Each task provides two types of human demonstrations: proficient human (PH) demonstrations and mixed proficient and nonproficient human (MH) demonstrations, Each environment uses an action prediction horizon Tр 16 and action execution horizon Ta 8, and all tasks use state-based observations. We construct CNN-based Diffusion Policy (Chi et al., 2023) with DDPM scheduler using 100 denoising steps and the DDIM/DPMSolver scheduler using 16 denoising steps. Models and are taken from the Diffusion Policy repository: http://dffffsiioooooiic..o wwwpuuiia..d///daaa/eeeeeeeeeeee
dim/.
multi- robotics tasks. The task involves action sequences of dimension 112 and an episode length of 1200, with an action prediction horizon of Tr = 16 and action execution horizon of Ta 8. We construct transformer-basee Diffusion Policy using DDPM scheduler with 100-step discretization.
Franka Kitchen (Gupta et al., 2020) is designed for evaluating algorithms on
Push-T is adapted from IBC (Florence et al., 2022) and involves pushing block in the shape of a T to a fixed target using a circular end effector. The task features randomized initial poses of the block and end-effector, requiring contact-rich, precise motion. use state-based observations.
Multimodel Block Pushing (Shafiullah al., 2022) tests the policy's 's ability to model multimodal action distributions by pushing two blocks into squares in any order. The demonstration data is generated by scripted oracle with access to groundtruth state info. This oracle randomly selects an initial block to push and moves it to randomly selected square. The remaining block is then pushed into the remaining square.
MetaWorld (Yu et al., 2020) is an open-source simulation benchmark for meta-reinnorceemet learning and multi-task learning. consists 50 different robotic manipulation tasks categorized into different difficulty levels ranging from simple to very challenging. We follow the setup and model training in the official 3D Diffusion Policy codebase (7e al., 2024b): http://ihbbb..oommaaaaj/////-D// Diffussio--Polcccy.
ManiSkil12 (Gu et al., 2023) is a benchmark for robotic manipulation with 20 tasks, 2000+ objects, and 4M+ demonstrations, It supports rigid/soft-body tasks and fast visual learning (2000 FPS).
A.2. Real-world Experiments
We conduct two real-world manipulation tasks dexterous grasping and high-precision insertion. As shown in Figure our robot consists of 7-DoF RealMan RM75-6F arm and 6-DoF PsiBot hand. It is equipped with wrist-mounted RealSense D405C camera and head-mounted RealSense D435 camera. Objects are placed on table in front of the robot for manipulation. For each real-world task, we collect 50 human demonstrations and use the open-source Diffusion Policy implementation https://github.com//// wwwp:/u..coo..oaaoeee/... wtw.///aasssaaffrdriffffuuuseeee stwnford/diiffuiionnnnooooo policy and SDP implementations https://ithub..om// Streaming- Diffusion- Dwffussoon-ooliyy/iiioi Policy/streaming diffusion policy.
12

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Falcon: Fast Visuomotor Policies via Partial Denoising
B. Pseudocode of Falcon with other sampling solvers
In this section, we provide the pseudocode for Falcon when integrated with alternative sampling solvers, such as DDIM (Song et al., a) and DPMSolver (Lu al., 2022).
B.1. Falcon with DDIM
DDIM's sampling process follows Eq. 12, where 2 represents the sample at noise level k, og is the noise scheduler and eø is the noise prediction network. To integrate Falcon with DDIM, we replace ay. with partial denoised action sequence d at time step / with noise level k and substitute (æx, k) with (O,, aia,,, where O, is the latest T, observations. This results in the modified sampling process for Falcon-enhanced diffusion policy (Eq. 13). The corresponding pseudocode provided in Algorithm 2.
Ch VI aRed (0
k)
/1 0k-1 - of ca (0k, + OKEK. e ~ (O,
2k-1 vak-1
(12)
k ailTT, - VI apea (Or a (( + 11 ax-1 -
k-1 ait,,
.b.00 co (or: a ((et,, k) + 0x (13)
Vak-1
vo
Algorithm 2 Falcon: Fast Visuomotor Policies via Partial Denoising (DDIM)
Require: Diffusion model with noise scheduler ãs, variance of threshold exploration prohability 6, latest T. observations O., latent buffer B, M denoising steps [kx)).o.
for I..... do
2: if K
then Qi:+-T, ~ ~N(0,
3:
4:
for i M,...,
5:
B BU (1,
6:
z~ N(0, 1) ks;
1. else +
GutTT "T-oy, (one + v1- - -hh, .co (Or, ,xt + +0,, Vou.
7:
8:-1 autTT
+ Vaku-1
8:
end for
9:
end
10:
ift
then
11:
To
Vez (a (o -.r ki))
Va - E
12:
S (at 11a TatT, -T.t-T. 6,, <t, k E 3
Sample a to tT, according to Eq. 10
14:
a t- 4T544T for 8,..., do
15:
16:
BU fa J. W~ N(O, if clse +0
17:
"itt, - -0 ) I an, oh, c (0,, a0, + ou, =
18:
aectT, Vau-i
4-1
19:
end for
20:
end
21:
end for
13

---

Falcon: Fast Visuomotor Policies via Partial Denoising
B.2. Falcon with DPMSolver
Given noise prediction network eæ, denoising steps ( DPMSolver's sampling process follows Eq. 14, where hG- log _ log a* To integrate Falcon, replace æ with a E.TTT and substitute ca (ark, k2) with co O a dut+r, Ris - oR, where O, represents the latest To observations. This yields the sampling process in diffusion policy with DPMSolver, as expressed in Eq. 15. The corresponding pseudocode is provided in Algorithm 3.
-k:-1 akk
O-k-1 Dk;
Oki-1 (e 1) co (IKk ki)
(14)
hi-1 a
Olky 1 hi 1 ohi-1 (c - 1) cB Ozs, Tuit ki)
(15)
Ok;
Algorithm 3 Falcon: Fast Visuomotor Policies via Partial Denoising (DPMSolver)
Require: Diffusion model сą with noise scheduler dg, variance of. threshold c, exploration probability 6, latest T, observations O,, latent buffer B and M + denoising steps [ki (k)) 1,..., T do 2: = then
1: for f
3: au:tT, N00, I)
4:
K aui+T, t Q2:14Tp
AM
5:
for M,... do
6:
B BU (aiit,,))
7:
hi-1 < log a
oRi log ok.
8:
autTTp
1:-1
ake ki
ane aEITTH ohe- ( - (0z, aie4T
9:
end for end if
10:
11:
if > 1 then
12:
ah: -TT-To+T,
Van, (at (
oz, ot a ki)) Va - B
13:
S (a |la T.tT,
2k -.r- TT, 1|2 e, VT < t
14:
Sample TT, P(a
exp in
E exphi k
15:
aoiTT, (- a arr+tT for 8,...:
16:
17:
B B ( Ok
18:
1i-11 log oN,-1
log oke
ons
19:
BuetT op. bi The1 ( - 1) c0 azi
Ki-1 am:-1
20:
end for
21:
end if
22:
end for
14

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Falcon: Fast Visuomotor Policies via Partial Denoising
C. Detail performance of Falcon with 3D Diffusion Policy
In this section, we provide a detailed results of Falcon's acceleration performance when applied to 3D Diffusion Policy (Ze et al., 2024b) in MetaWorld environments. Falcon is integrated with DDIM using 10-step discretization(we call 3D FaI)onDDIM), following the original 3D Diffusion Policy architecture ensure fair comparison.
Tables 7, 8 and 9 present the success rates, NFE and speedup respectively, for both 3D Diffusion Policy and 3D FalconDDIM. These results further validate Falcon's is effectiveness in reducing inference time while maintaining task performance across different robotic manipulation tasks.
Table Detailed results for 39 simulated tasks with success rates. We evaluated 39 challenging tasks using 50 random seeds and reported the average success rate (%) and standard deviation for each task individually. The 3D FalconDDIM algorithm demonstrates nearly no performance drop.
Meta-World (Easy)
Alg \ Task
Button Press
Coffee Button
Plate Slide Back Side
Plate Slide Side
Window Close
Window Open
3D Diffusion Policy
100 + 0
100 + 0
100 + 0
100 + 0
100 + 0
100 + 0
3D FalConDDIM
100 + 0
100 - 0
100 - 0
100 + 0
100 + 0
100 + 0
Meta-Wordh (Easy)
Alg Task
Button Press Topdown
Button Press Topdown Wall
Button Press Wall
Peg Unplug Side
Door Close
Door Lock
3D Diffusion Policy
100
75 1:
3D FaIMonDDIM
100 + 0
100 + 0
100 + O
75 + 43
100 + 0
96 + 19
Meta-World (Easy)
Alg \\ Task
Door Open
Door Unlock
Drawer Close
Drawer Open
Faucet Close
Faucet Open
Handle Press
3D Diffusion Policy
99
100 +
100 + 0
100
100
100 + 0
100
3D FalconDDIM
100 + 0
100 + 0
100 + 0
100 +
100
100 + 0
100 +
Meta-World (Easy)
Alg Task
Handle Pull Side
Lever Pull
Plate Slide
Plate Slide Back
Dial Turn
Reach
3D Diffusion Policy
85 + 3
79 + 8
100 + 1
99 + 0 100 - 0
92 + 27
68 + 16
3D FalconDDIM
87 + 33
81 + 39
86 + 34
89 + 31 68 + 46
Meta- World (Medium)
Meta-World (Hard)
Alg Task
Hammer
Basketball
Push Wall
Box Close
Sweep
Sweep Into
Assembly
Hand Insert
3D Diffusion Policy
88 + 32 83 37
98 + 2
88 + 32
56 + 49
96 + 3
15 + 5
99 +
12 + 32
3D FalconDDIM
100 + 0
88 + 32
55 + 49
100 +
13 - 33
99 +
26 + 43
Meta-World (Hard)
Meta-World (Very Hard)
Alg \ Task
Push
Shelf Place
Disassemble
Stick Pull
Stick Push
Pick Place Wall
3D Diffusion Policy
51 + 3
52 + 49
72 + 44
68 + 16
97 + 4
80 + 40
3D FalConDDIM
53 + 49
47 + 49
75 + 43
68 46
100
87 33
15

---

Falcon: Fast Visuomotor Policies via Partial Denoising
Table Detailed results simulated tasks with NFE. We evaluated 39 challenging tasks using random seeds reported the average Number of Function Evaluations (nfé) per action generation and standard deviation for each domain individually. The 3D FalconDDIM algorithm reduces the to range 2-4 compared to the 3D Diffusion Policy
Meta-World (Easy)
Alg \ Task
Button Press
Coffee Button
Plate Slide Back Side
Plate Slide Side
Window Close
Window Open
Diffusion Policy
10 +
10 +
10 +
10
10
10 =
3D FalConDDIM
2.12 - 0.05
2.47 + 0.09
3.91 + 0.22
3.27 = 0.68
2.54 = 0.18
3.58 + 0.90
Meta-World (Easy)
Alg \ Task
Button Press Topdown
Button Press Topdown Wall
Button Press Wall
Peg Unplug Side
Door Close
Door Lock
3D Diffusion Policy
10 + 0
10 10+00
10 +
10 0
10 + 0
10 + 0
FalconDDIM
2.48 0.20
2.63 0.40
3.36 0.61
3.25 + 0.38
2.81 + 0.05
4.06 1.00
Meta-World (Easy)
Alg Task
Door Open
Door Unlock
Drawer Close
Drawer Open
Faucet Close
Faucet Open
Handle Press
3D Diffusion Policy
+
3D FalMonDDIM
3.04 + 0.26
4.70 + 0.86
2.91 + 0.47
3.90 0.18
4.06 + 0.62
4.94 + 0.78
3.52 = 0.43
Meta-World (Easy)
Alg \ Task
Handle Pull Side
Lever Pull
Plate Slide
Plate Slide Back
Dial Turn
Reach
Diffusion Policy
10
10 +
10 0
10
10
10 = 2.85 + 0.05
3D FalConDDIM
3.59 + 0.51
3.79 + 0.77
3.11 + 0.15
3.49 + 0.16
4.26 + 1.16
Meta-World (Medium)
HorldWorld (Hard)
Alg Task
Hammer
Basketball
Push Wall
Sweep
Sweep Into
Assembly
Hand Insert
3D Diffusion Policy
10 + 0
10 + 0
10 +
10 1
10 + 0
10 1 0
10 = 0
10 =
3D FalconDDIM
3.25 0.60
2.97 + 0.14
3.35 + 0.22
4.51 = 1.12
3.09 + 0.18
3.59 + 0.19
2.44 0.23
3.29 : 0.19
Meta- Merld (Hard)
Meta- (Very Hard)
Alg \ Task
Push
Shelf Place
Disassemble
Stick
Stick Push
Pick Place Wall
3D Diffusion Policy
10 = 0
10 = 0
10 + 0
10 + 0
10 - 0
10 + 0
3D FalconDDIM
2.88 + 0.38
4.28 + 2.33
3.25 - 0.37
3.78 0.73
3.41 + 0.32
3.04 + 0.15
16

---

Falcon: Fast Visuomotor Policies via Partial Denoising
Table Detailed results for 39 simulated tasks with speedup. evaluated 39 challenging tasks using random seeds and reported the average speedup and standard deviation for each task individually. The 3D FalconDDIM algorithm reduces the NFE to range of 2-4 compared to the Diffusion Policy
Meta-World (Easy)
Alg Task
Button Press
Coffee Button
Plate Slide Back Side
Plate Slide Side
Window Close
Window Open
3D DMIMonDDIM
4.71 + 0.12
4.03 + 0.14
2.55 + 0.14
3.05 + 0.60
3.93 + 0.28
2.79 + 0.66
Meta-Worid (Easy)
Alg Task
Button Press Topdown
Button Press Topdown
Button Press
Peg Unplug Side
Door Close
Door Lock
3D DIIM
4.02 0.32
3.80 + 0.48
2.97 0.47
3.07 + 0.34
3.55 + 0.06
2.46 0.51
Meta-World (Easy)
Alg \ Task
Door Open
Door Unlock
Drawer Close
Drawer Open
Faucet Close
Faucet Open
Handle Press
3D FaMMonDDIM
3.28 + 0.26
2.12 + 0.32
3.42 0.36
2.55 0.12
2.45 0.35
2.02 = 0.35
2.83 + 0.30
Meta-World (Easy)
Alg Task
Handle Pull Side
Lever Pull
Plate Slide
Plate Slide Back
Dial Tum
Reach
3D FaIMonDDIM
2.77 + 0.36
2.63 + 0.44
3.21 + 0.15
2.86 + 0.14
2.34 + 0.56 3.50 + 0.06
Meta-World (Medium)
Mord-World (Hard)
Alg \ Task
Hammer
Basketball
Push Wall
Box Close
Sweep
Sweep Into
Assembly
Hand Insert
FalconDDIMM
3.25 0.60 2.97 0.14
3.35 0.22
4.51 1.12
3.09 0.18
3.59 0.19
2.44 + 0.23
3.29 + 0.19
Meta-Worke (Hard) Push
Meta- Woold (Very Hard)
Alg Task
Shelf Place
Disassemble
Stick Pull
Stick Push
Pick Place Wall
3D FalconDDIM
2.88 + 0.38
2.33 = 0.65
3.07 + 0.30
2.64 + 0.44
2.92 = 0.25
3.28 + 0.14
17

---

Falcon: Fast Visuomotor Policies via Partial Denoising
D. Experiment Details
In this section, we provide the detailed experimental setup for Robomimic and analyze Falcon's memory cost compared to the original samplers (DDPM, DDIM, and DPMSolver). Table 10 reports the hyperparameter settings and the peak memory usage for each experiment.
To evaluate Falcon's computational overhead, we measure the peak memory cost, denoted in the format (original sampler cost) (incremental cost due to Falcon integration). As shown in Table 10, Falcon introduces an additional 12 MB of memory overhead, which is negligible compared to the original 1876 MB cost. This demonstrates that Falcon achieves acceleration with minimal memory overhead, making it practical and efficient enhancement to diffusion-based policies.
DDPM-Falcon
Daloon
DPMSolver-Falcon
Peak Memory Cost
Peak Memory
Peak Memory
Kmin
B
8
Amin
[B]
Cost
Cost
Lift
0.04
0.1
20
50
1876+12 MB
0.05
0.2
15
50
1876+12 MB
0.08
0.2
20
50
1876+ 12 MB
Lift mh
0.04
0.1
20
50
1876+12 MB
0.03
0.25
10
50
1876+12 MB
0.005
0.2
20
50
1876+ 12 MB
ph
0.01
0.1
20
50
1876+12 MB
0.01
0.20
50
1876+12 MB
0.003
0.2
20
50
1876+12 12 MB
Can mh
0.01
0.1
20
50
1876+12 MB
0.005
0.20
50
1876+12 MB
0.003
0.2
20
50
1876+12 12 MB
Square ph
0.04
1876+12
0.01
0.20
1876+12
0.003
0.2
25
20
1876+12 MB
Square mh
0.04
0.1
25
50
1876+12 MB
0.005
0.20
50
1876+12 MB
0,005
0.2
20
20
1876+12 MB
Transport ph
0.01
1876+12 MB
0.001
0.33
1876+12 MB
0.005
0.2
20
20
1876+12 MB
Transport mh
0.01
0.2
25
50
1876+12 MB
0.01
0.33
50
1876+12 MB
0,003
0.2
60
50
1876+12 12 MB
ToolHang ph
0.01
1876+12
0.003 0.33
1876+12 MB
0.003
0.2
60
50
1876+12 MB
Table 10. Hyperparameters and Memory Cost in Robomimic.
18

---

Falcon: Fast Visuomotor Policies via Partial Denoising
E. Additional Experiments
E.1. Acceleration on Vision-Languaae-Actionn Foundation Model (RDT)
To assess Falcon compatibility with pre-trained diffusion foundation models, we integrate with RDT-IB (Liu et al., 2024), a vision language action foundation model trained in ManiSkil12. Falcon is applied directly to RDT's DPMSolver++ inference without retraining. The official model weights are obtained from the RDT repository htps://huggingface.co -hiotics-diffusion--o haw.ainorraaasommmmmaiiiiiilllao
As shown in Table 11, Falcon achieves substantial inference speedups (31x and 34x vs. DDPM) with no loss in success rate on task PickCube (see Fig. 9) and task PushCube (see Fig. 10), demonstrating its plug-and-pe and-play compatibility with VLA Foundation model.
Figure 9. RDT evaluated in PickCube with Falcon
Figure 10. RDT evaluated in PushCube with Falcon
PickCube
Method
Success Rate
NFE
Time (s)
Speedup
GPU MEMS (MB)
DPMSolver++ (5 steps)
5.00
0.08
20x
15479
DPMSolver++ (5 Falps)
0.75
3.22
0.04
31x
15483
PushCube
Method
Success Rate
NFE
Time (s)
Speedup
GPU MEMS (MB)
DDPMSolver++ (5 steps)
1.00
5.00
0.08
20x
15483
DPMSolver++ (5 steps)+Falcon
1.00
2.91
0.05
34x
15483
Table Falcon improves inference speed RDT unseen ManiSkill tasks (T,, 64, T. 32, same ere-trained checkpoint. Speedup is relative to 100-step DDPM. Falcon achieves comparable performance with significantly fewer denoising steps and lower runtime.
2, 0.02, [B] 2) using the
19

---

Falcon: Fast Visuomotor Policies via Partial Denoising
E.2. Additional Ablation Threshold
Trunsport ph
Tool hang ph
4.8
4.8
0.5
0.75
Score
0.4-
Score
0.60
Speed
3.2
Speed
3.2
Soore
Speed
Score
Speed
0.45
2.4
2,4
0.2
0.30
0.1
0.000
0.015
0.030
0.045
0.000
0.025
0.050
0.075
0.100
Epsilon
Epsilon
Figure 11. Additional Ablation Analysis threshold We conduct extended experiments with DDPM+Falcon Transport ph and Tool hang ph, varying the threshold e. Results show a trade-off between success rate (Score) and speedup.
E.3. Falcon achieves accelerating diffusion policy in long-horizon tasks
To evaluate Falcon's effectiveness long-horizon tasks, we apply it to the Franka Kitchen environment. As shown in Table 12, Falcon maintains the same high success rates as the original DDPM model, confirming that the acceleration introduced by Falcon does not compromise task performance, even for mang-horioon tasks. The key benefit of Falcon lies in its ability to reduce NFE and accelerate the denoising process, which is evident in the speedup shown in Table 13. Falcon achieves 5.25x speedup in Kitchen, which shows that Falcon can accelerate DDPM in long-horizon tasks and can be adopted into transformer architecture.
Block Push
Kitchen
BlockPush
Kitchen
pl
p2
pl
p2
p3
p4
DDPM
0.99
0.94
1.00
0.99
0.99
0.96
DDPM+Falcon
0.99
0.97
1.00
0.99
0.99
0.96
Table Success Rate BlockPush Kitchen. For BlockPush, par refers to frequency pushing æ blocks into the targets. Kitchen, pa refers to the frequency of interacting with more objects.
BlockPush
Kitchen
NFE
Speedup
NFE
Speedup
DDPM
100
1.0x
100
1.0x
DDPM+Falcon
32.7
2.8x
19.03
5.25x
Table 13. Speedup in PlockPush and Kitchen. Falcon can accelerate diffusion policy in long-horizon tasks.
20

---

Falcon: Fast Visuomotor Policies via Partial Denoising
E.4. More results of Multimodality
In this section, we provide a visualization of the multimodal action distributions generated by Falcon in the BlockPush environment. As shown in Fig. 12, the frequency distribution of different action modalities in the BlockPush task, where each modality corresponds to a distinct combination of blocks being pushed into two squares. The chart illustrates the uniformity of the action modality frequencies, with the four modalities (1-2, 1-1, 2-1, and 2-2) being equally represented. This visualization confirms that Falcon is capable of expressing multimodal actions, effectively handling different action combinations without bias.
0.5
Action Modality
1-2
0.4
1-1
2-1
2-2
Frequency
0.3
0.1
0.0
Figure Action Modalities Distribution BlockPush Task. The bar chart shows the frequency of different policy modalities for pushing two blocks into two squares in any order. The modalities are represented as 1-2, 1-1, 2-1, and 2-2, where the numbers indicate the block number and square number respectively. The chart illustrates the frequency distribution the action modalitics generated by Falcon in the BlockPush task.
21

---

Falcon: Fast Visuomotor Policies via Partial Denoising
F. Visualization of Starting Points
This section visualizes where Falcon starts the denoising process at each time step, specifically from which past time step and at what noise level the partial denoised actions originate. We analyze two tasks: Lift ph and Transport ph, representing high and low acceleration scenarios, respectively.
As shown in Fig. 13 (left), in the Lift task, Falcon consistently starts denoising from the partial denoised action of the previous time step, with low noise level. This indicates significant reduction in sampling steps, leading to substantial acceleration. In contrast, Fig. 13 (right) shows that the Transport task starts from actions with higher noise levels, limiting the acceleration effect. This suggests that Falcon's s speedup is more pronounced in tasks with smoother, more predictable action transitions.
Visualization of starting point in Lift ph
Visualization starting point Transport ph
100
to
100
Historical Timesteps
110
80
Historical Timesteps
t20
80
Noise Level
Noise Level
t20
60
60
t40
t30
-00
44
t00
t00
20
20
tg0
to
t00
t2o
to
E40
to
t2
t40
60
t0
Current Timesteps
Current Timesteps
Figure 13. Visualization of denoising starting point. This figure shows heatmaps R ph task (left) and Transport ph task (right). A]j = & means that at time step i, Falcon starts denoising at the partial denoised actions a d3
22


Reviews:
[{"review_id": "rq9Ztu2yPb", "reviewer": "Reviewer_i58m", "summary": "The authors propose a method to speed up inference with diffusion policies using a scheme where the denoising chain for action sequences is initialized to a partially noised sequence predicted from a previous timestep. The proposed method is supposed to preserve the multimodality of the diffusion policy while achieving anywhere from 2 to 7x speedup. The authors evaluate their method on a variety of simulated robotics tasks.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the thoughtful and detailed comments. We appreciate the constructive feedback and address the key concern below.\n\n**Clarifying the Motivation and Positioning of Our Method**\n\nOur contribution is not to replace distillation or ODE solvers, but to offer a practical, training-free alternative that leverages temporal structure for faster inference in diffusion policies.\n\nWhile we discuss limitations of prior methods, Falcon is not motivated by them. As stated in our abstract, *“The core insight is that visuomotor tasks exhibit sequential dependencies between actions at consecutive time steps.”* Falcon leverages this via partial denoising to reduce sampling steps.\n\nWe fully recognize that **distillation methods are powerful**, particularly in settings with large data and compute [2,4,5,6]. In such settings, they deliver substantial speedups while maintaining performance. We respect this line of work and do not position Falcon as a replacement.\n\nInstead, Falcon instead targets low-resource regimes. In these cases, distillation may be infeasible, and ODE solvers can suffer from large discretization errors under very low NFE [1]. Falcon addresses this gap by offering a plug-and-play solution that also integrates well with ODE solvers in low-step regimes **(Section 4.3)**.\n\nWe acknowledge that our initial wording may have been overly strong regarding distillation. While methods like CP [2, **Sec. 5**] may face challenges in preserving multimodality, this is task-dependent. We will revise accordingly, and thank the reviewer for pointing this out.\n\n**Lack of Theoretical Foundation**\n\nAlthough Falcon lacks a full theoretical derivation, its **core components are grounded in well-established principles**.\n\nFalcon is built on the insight that in sequential decision-making, when past actions are strongly correlated with the current one, initializing from them can reduce sampling steps. Falcon exploits this via reusing past denoised actions.\n\nCrucially, the choice of which past action to reuse is not heuristic. It is determined by Falcon’s **thresholding mechanism**, based on **Tweedie’s formula**, a foundational result in empirical Bayes theory. As noted in **Remark 1 of [7]**, Tweedie’s formula yields the Bayes-optimal posterior mean under Gaussian noise, guiding our selection of the cleanest available prior. This forms the theoretical core of our partial denoising module.\n\nThus, while Falcon lacks a full pipeline of theory, its **main mechanism—Tweedie-based partial action reuse—is mathematically justified** and validated through strong empirical performance.\n\n**On Expressing Multimodal Distributions**\n\n1. The BlockPushing task has inherently multimodal expert trajectories (**BeT [3], Table 2**).\n2. We added a **Gaussian baseline** using the same network. It fails on both goals **(p1: 0.02, p2: 0.01)**, while Falcon succeeds **(p1: 0.99, p2: 0.97), (see in Table 3 in the [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede))** showing the need for multimodal modeling.\n3. We evaluated **Consistency Policy** on PushT and found it biased toward one mode, indicating that some distillation methods may struggle to retain multimodality see in **Figure 4 in the [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**.\n\n**Comparisons with Reduced-Step DPMSolver**\n\nTo directly assess whether lower-step DPMSolver leads to performance degradation, we matched its NFE to that of DPMSolver+Falcon. Under this constraint, DPM-Solver* exhibited significant performance drops—**10.6%** in Square_ph, **35.6%** in Square_mh, and **6.8%** in Transport_ph—while Falcon maintained high success rates (**see Table 2 in [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**). This confirms that aggressive NFE reduction in ODE solvers can hurt performance, and Falcon helps mitigate this degradation.\n\n**Missing or Incorrect References**\n\nWe will add the missing citation for Progressive Distillation [6], and correct the mistaken reference on Line 432.\n\n**Closing Remarks**\n\nWe are grateful for the reviewer’s thoughtful comments, which helped us improve both the clarity and positioning of our work. If our responses have addressed your concerns, we would greatly appreciate a reconsideration of the score. Please let us know if further clarification is needed.\n\n**References**\n\n[1] PFDiff: Training-Free Acceleration of Diffusion Models Combining Past and Future Scores\n\n[2] Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation\n\n[3] Behavior Transformers: Cloning k Modes with One Stone\n\n[4] Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning\n\n[5] Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion\n\n[6] Progressive Distillation for Fast Sampling of Diffusion Models\n\n[7] Diffusion Posterior Sampling for General Noisy Inverse Problems", "claims_and_evidence": "The claims regarding significant speedup are clear. However, ultimately I do not find the proposed method convincing. The authors motivate their method by claiming that other more principled approaches to speed up diffusion inference such as ODE solvers suffer from numerical discretisation errors, and distillation techniques cannot represent multimodal policy distribution. These claims are never properly justified in the paper. The proposed method itself is does not have much theoretical motivation either.", "methods_and_evaluation_criteria": "The datasets and tasks which are evaluated are a good starting point, but there are some issues. Firstly, there is no evidence provided that the behavior policy for these datasets is actually multimodal, and requires diffusion policies. Section 4.5 does not truly evaluate multimodality of policy distributions, since trajectory level multimodality can even be achieved through per-action unimodal policies like Gaussians. It is important to have a Gaussian policy baseline to compare against. One of the important claims of the paper is that the proposed method preserves multimodality whereas other distillation methods do not, and this claim is never backed up.", "theoretical_claims": "There are no theoretical claims made by the paper. The proposed method is justified primarily through author's intuition and empirical evaluation.", "experimental_designs_or_analyses": "As mentioned earlier, the tasks themselves are probably fine, as long as a Gaussian baseline is included to show that they actually have multimodal behavior policies. Table 2 compares number of function evaluations, and for DPMSolver and DDPM more evaluations are shown to be used then Falcon. However, we can see that NFE for DPM solver is already quite close to the Falcon. No comparison is shown where the ODE in DPMSolver is integrated with fewer steps. Does it actually lead to loss in performance in these tasks? There is also no distillation method that is compared against (consistency distillation, progressive distillation etc.) except a very limited evaluation in Fig 4, which in theory preserve the same marginals as the original diffusion model so should see minimal performance drop.", "relation_to_broader_scientific_literature": "The paper is related to fast sampling of diffusion models. Some important works in this class are ODE based sampling [1], ODE distillation [2, 3], and progressive distillation [4] among many others.\n\n[1] DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps, https://arxiv.org/abs/2206.00927\n\n[2] Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning, https://arxiv.org/abs/2309.16984\n\n[3] Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion, https://arxiv.org/abs/2310.02279\n\n[4] Progressive Distillation for Fast Sampling of Diffusion Models, https://arxiv.org/abs/2202.00512", "essential_references_not_discussed": "The literature in fast diffusion sampling is vast, and for flow models there are even more. The authors should additionally cite progressive distillation at least, since it is a very popular and important paper in the field.", "other_strengths_and_weaknesses": "### Strengths\n1. The authors evaluate on many different tasks, which is appreciated\n\n### Weaknesses\n1. The proposed method is explained poorly in my opinion. I think this has more to do with the method not having sufficient theoretical justification.\n2. Some confusing statements which are not really justified, such as \"Moreover, distillation-based approaches are inherently training-intensive and task-specific, meaning they cannot generalize effectively to accelerate unseen tasks or adapt to diverse visuomotor applications.\"  in the introduction.", "other_comments_or_suggestions": "Minor error: in line 432 the consistency policy reference instead points to consistency trajectory models, which is a different paper from the consistency policy paper (which is referenced correctly in section 4.5).", "questions_for_authors": "1. Why do you say distillation techniques are task specific and not generally applicable? Most diffusion distillation techniques are applicable to any diffusion model.\n2. How do you know your proposed method requires less function evaluations than DPMSolver? You can manually adjust the number of steps for the ODE solvers using different integration schemes.", "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": "i0XemY0jI6", "reviewer": "Reviewer_4p76", "summary": "This paper introduces Falcon, a method that accelerates the diffusion process by denoising partially noisy actions at each step using a one-step adaptive mechanism. Extensive experiments validate the speed improvement of the Falcon method on robot datasets.\n\n## Update after rebuttal\nI'm satisfied with the authors' rebuttal which addresses most of my main concerns, including the comparisons with related baselines and the real-world experiments.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the thoughtful and constructive feedback. We appreciate your recognition of Falcon’s motivation and your suggestions for strengthening the comparison and evaluation. Below, we address each concern in turn.\n\n**On the Effectiveness of the Threshold Mechanism (DDIM+Falcon)**\n\nThe performance drop in **Table 1** for DDIM+Falcon stems from the threshold $\\epsilon $ being set relatively high in our original configuration to maximize speedup. This allowed partial denoised actions with weaker temporal dependency to be reused, leading to performance degradation. This does not indicate a flaw in the mechanism itself, but rather reflects a trade-off between efficiency and accuracy.\n\nTo address this, we conducted additional experiments with smaller $\\epsilon$ values. As shown in **Table 9 in the [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**, reducing $\\epsilon$  (e.g., **0.005 → 0.001, 0.01 → 0.003**) significantly improved success rates in Transport **(0.74 → 0.81)** and Tool Hang **(0.51 → 0.54)**, while retaining acceleration.\n\nWe also note that **Figure 3 (left)** in our main paper presents an ablation study showing this trade-off trend. For more task-specific results, **Figure 5 in the [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)** further supports this behavior. We will incorporate the updated results in the revised version.\n\n**Comparison with SDP[1]**\n\nWe would like to clarify that **SDP was already cited and briefly discussed in Section 5 of the original submission**. We appreciate the opportunity to provide a more detailed comparison.\n\nFalcon offers **two key advantages** over SDP:\n\n- **Training-free**\n\n    Falcon requires no retraining or noise-corruption design. In contrast, **SDP must be retrained per task** with a handcrafted noise corruption scheme (SDP Sec. 3.3), limiting flexibility.\n\n- **Less Memory Cost**\n\n    **Falcon’s thresholding mechanism allows it to maintain a small buffer (e.g. 50, see Appendix D)**, while SDP stores a full horizon × noise-level buffer (e.g., 16×100). As shown in **Table 7 on our [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**, Falcon adds only +12MB of memory compared with DDPM, but SDP adds + 26M.\n\nFinally, we implemented SDP under our setting and provide comparisons in **Tables 4–8 on our [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**. Falcon achieves comparable task performance while offering higher speedups and lower memory overhead.\n\n**On Sensitivity to $\\epsilon$ and Deployment Concerns**\n\nWe agree that $\\epsilon$ is a key hyperparameter, and we have already provided a detailed analysis of its impact in **Figure 3 (left)** in our submission and **Figure 5 in [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**. These results show that:\n\n- Falcon’s performance remains stable within a **broad $\\epsilon$ range (e.g., 0.001–0.01)** across tasks;\n- The accuracy-speed trade-off is smooth and interpretable, making ε easy to configure in practice;\n- The same $\\epsilon$ values generalize well across different environments, reducing per-task tuning burden.\n\nAlthough Falcon does rely on $\\epsilon$ , it is **training-free**, and introduces only a few parameter to adjust—a significantly lighter requirement compared to training based acceleration methods.\n\nWe believe this controllable trade-off offers a practical balance between deployment flexibility and performance.\n\n**On Real-World Deployment Evaluation**\n\nTo assess real-world applicability, we conducted a **dexterous grasping experiment** with a physical robotic platform (**Figure 1 in [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**), composed of a RealMan 7-DoF arm, a PsiBot 6-DoF hand, and dual RealSense cameras.\n\nDDPM, SDP(DDPM), and Falcon+DDPM were deployed using the same Unet architecture, with $T_p=16,T_o=1,T_a=8$. As shown in **Table 1 on our [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**, all methods achieved 100% success rate, but **Falcon achieved 3.07× speedup** and outperformed SDP in both runtime **(0.14s vs. 0.23s)** and memory consumption **(3730MB vs. 3808MB CPU)**.\n\nThese results confirm that Falcon not only accelerates diffusion policies in simulation but also transfers efficiently to real-world robotic execution.\n\n**Others**\n\nWe consider Paper [2] as concurrent works.\n\n**Closing Remarks**\n\nWe once again thank the reviewer for the detailed comments. If our responses have addressed your concerns, we would be sincerely grateful for your consideration in revising the score. Please let us know if further clarification is needed.\n\n**References:**\n\n[1] Fast Policy Synthesis with Variable Noise Diffusion Models\n\n[2] Responsive Noise-Relaying Diffusion Policy", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "The provided propositions are clear and have been proven.", "experimental_designs_or_analyses": "The main experiments in the paper are conducted on the RoboMimic dataset, with comparatively fewer analyses on other datasets. I would have liked to see Falcon's performance on more complex datasets, such as Dexterous Grasping or Long-Horizon tasks.", "relation_to_broader_scientific_literature": "Acceleration of diffusion policies is crucial in the field of robotics, as lower latency benefits practical deployment in real-world robotic systems.", "essential_references_not_discussed": "Streaming Diffusion Policy (ICRA 2025, https://arxiv.org/pdf/2503.04051), which also leveraged the insight that generating a partially denoised action trajectory is substantially faster than a full output action trajectory, and was released last year.", "other_strengths_and_weaknesses": "**Strengths:**\n\n- The paper is well-written and easy to read.\n- The simulation experiments are sufficient and thorough.\n- The motivation is clear and reasonable, aiming to accelerate the diffusion denoising process using partially noisy actions. The corresponding mathematical tools support the proposed method.\n\n**Weaknesses:**\n\n- One of the major issues with this paper is that despite having a strict selection mechanism (Line 12 in Algorithm 1), the accuracy drop caused by the partial noise selected through this mechanism is significant. Specifically, in Table 1 (DDIM + Falcon), this not only raises questions about the effectiveness of the threshold mechanism but also calls for a more detailed explanation from the authors about why this limitation still results in performance degradation with carefully selected partial noise candidates.\n\n- This paper needs a more detailed comparison with existing work on streaming diffusion policies (SDP [1]), which uses step-wise partial noise without filtering as a buffer to achieve acceleration. A basic partial noise arrangement, such as the one in SDP where the noise level decreases as $t$ increases, should serve as a baseline to validate the effectiveness of the threshold mechanism.\n\n- There is a lack of more comparative results on the hyperparameter epsilon. The existing results indicate that the method is highly sensitive to this hyperparameter, which poses challenges for deployment on different tasks in the future.\n\n- As a robot learning paper, it is difficult to be highly convinced of the method's real-world effectiveness without physical experiments.\n\n- Please consider comparing with previous partial denoising-based methods [1][2]. If some of the works are considered concurrent works, feel free to disregard them.\n\n--- \n*References:*\n\n[1] Fast Policy Synthesis with Variable Noise Diffusion Models. https://arxiv.org/pdf/2406.04806\n\n[2] Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control. https://arxiv.org/pdf/2502.12724", "other_comments_or_suggestions": "Overall, this paper presents an interesting idea of using partial noise to accelerate diffusion policies. However, the emergence of existing work (SDP) and the performance drop caused by the filtering mechanism reduce the novelty of this work. Nevertheless, I still look forward to the authors providing a more fundamental explanation of why Falcon causes uncontrollable performance degradation and potential solutions for hyperparameter sensitivity. Additionally, I would like to see more real-world experiments. I will adjust my future rating based on the improvements made.\n\nIf I have misunderstood any points, I am open to discussion.", "questions_for_authors": "Please carefully read the weaknesses part.", "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": "O5DyOc3ccB", "reviewer": "Reviewer_7PAa", "summary": "This paper presents Falcon, Fast visuomotor policies via partial denoising. This approach improves diffusion policies by accelerating action generation while preserving the multimodal generation capability. Accelerations are mainly provided by using partial denoised actions to reduce denoising steps. Falcon is a training-free algorithm and can be plugged-in to further improve efficiency on top of existing techniques. The proposed algorithm has been evaluated on three different simulated robotics datasets that expose different challenges and help evaluate the contributions of this paper.\n\n\n## update after rebuttal\nI confirm my score. Authors addressed comments and added clarity and results to the original submission.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the thoughtful suggestion and for the generous score. We deeply appreciate your recognition of our work, and your feedback has helped us identify opportunities to clarify and strengthen our contributions.\n\n**On the Relationship Between $\\epsilon$ and $\\delta$**\n\nIn Falcon, $\\epsilon$ and $\\delta$ play complementary roles in controlling the reuse of partial denoised actions.\n\n- **$\\epsilon$ serves as a selection threshold** that determines whether a previously denoised action is temporally aligned enough to be reused. Smaller ε enforces stricter matching (favoring accuracy), while larger $\\epsilon$ allows more aggressive reuse (favoring speed).\n\n- **$\\delta$ is the exploration rate**, specifying the probability of discarding the reused action and instead sampling from standard Gaussian noise. This mechanism injects diversity into the action sequence and prevents over-reliance on suboptimal reuse.\n\nWhile our ablations (**Figure 3, left and middle in our submission**) analyze these parameters independently, we agree that their joint effect is important. In our experiments, we typically fix **$\\delta = 0.1$ or $\\delta = 0.2$** , and find that **$\\epsilon \\in [0.001, 0.01]$** works robustly across tasks. This setting consistently balances acceleration and success rate. We will explore their interaction more formally in future work.\n\n**On the Definition of $\\alpha$**\n\nThank you also for pointing out the missing definition of $\\alpha$. It refers to the variance schedule in the forward diffusion process, as commonly defined in DDPM[1] or DDIM[2].   We will include the formal definition and cite the appropriate reference in the revised version.\n\nWe once again thank the reviewer for their thoughtful and constructive feedback. Your comments have been instrumental in helping us refine the clarity and usability of our method.\n\n\n\n**References**\n\n[1] Denoising Diffusion Probabilistic Models\n\n[2] Denoising Diffusion Implicit Models", "claims_and_evidence": "Claims are supported by clear and convincing evidence, through thorough analysis and grounding the proposed approach in relevant literature.", "methods_and_evaluation_criteria": "The proposed method is supported by clear and convincing evaluations, through experiments on three robotics dataset, ablations of parameters, analysis of results presented in a clear way. \nNote: the two parameters (epsilon and delta) play a key role in obtaining speed and high success score. Is there an analysis of their relationship or an hypothesis regarding how to set guarantees ranges for these two in order to achieve high scores and speed?", "theoretical_claims": "I reviewed the theoretical claims and equations, although I did not verify all mathematical derivations in detail. While it is possible that I may have overlooked some aspects, the theoretical claims appear to be correct to the best of my understanding.\nNote: alpha (page 2) is not defined - please add a definition or relevant reference.", "experimental_designs_or_analyses": "Experiments are meaningful and informative. Results are analyzed in a compelling way and ablations are useful and to the point.", "relation_to_broader_scientific_literature": "This work is relevant for the robotics community where diffusion policies can play an important role.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "The paper is well presented and significant. Claims, theoretical contributions and experiments are presented in a clear way. It can have impact on robotics applications.", "other_comments_or_suggestions": "See note on definition of alpha.", "questions_for_authors": "Please refer to the other comments.", "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": "Ozy3XeJMf2", "reviewer": "Reviewer_8uiC", "summary": "The paper presents Falcon, an innovative approach that accelerates diffusion-based visuomotor policies without sacrificing their performance. Conventional diffusion policies rely on multiple denoising steps, which can hinder real-time decision-making. Falcon addresses this issue by exploiting the sequential dependencies among actions, enabling the denoising process to start from partially denoised actions rather than from a standard normal distribution. This approach reduces the number of required sampling steps, improving inference speed without the need for extra training.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the detailed and thoughtful feedback. We appreciate your recognition of Falcon’s novelty and practical motivation, and we are grateful for your constructive suggestions on deployment, which helped us strengthen the final version of our work.\n\n**Real-World Validation**\n\nTo address the concern about practical utility, we conducted a **real-world dexterous grasping** using a physical robotic setup (see in **Fig. 1 of the [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**) . Falcon, DDPM, and SDP were evaluated using the same Unet backbone with planning horizon $T_p=16$. As shown in **Table 1 on our [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**, all methods achieved 100% success, while **Falcon reduced runtime by 3.07x compared to DDPM**, and **outperformed SDP in both runtime (0.14s vs. 0.23s) and memory usage (3730MB vs. 3808MB)**, demonstrating strong real-world performance.\n\n**Memory and Computational Overhead**\n\nFalcon maintains a **small buffer of 20–50 actions** (**see Appendix D in our submission**), while SDP requires a much larger horizon × noise-level buffer (e.g., 16×100). As shown in **Table 7 of the [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**, Falcon’s memory overhead is **+12MB**, significantly lower than SDP’s **+26MB**, making Falcon more suitable for real-time and resource-constrained deployment.\n\n**Hyperparameter Sensitivity and Usability**\n\nFalcon introduces two scalar hyperparameters: $\\epsilon$ (reuse threshold) and $\\delta$ (exploration rate), which control the balance between inference speed and action accuracy.. While both influence the trade-off between speed and accuracy, our ablations (**Figure 3, left and middle in submission**) show that **Falcon performs robustly when ε ∈ [0.001, 0.01] and δ ∈ [0.1, 0.2]**, across all tested environments. These parameters are intuitive, require **no retraining or fine-tuning**, and generalize well across tasks. We will include these practical guidelines in the revision.\n\n**Comparison with Streaming Diffusion Policy (SDP)**\n\nWe thank the reviewer for highlighting SDP [1], which is indeed an important related work. We would like to clarify that SDP was already **cited and briefly discussed in Section 5 of our submission**. We appreciate the opportunity to strengthen this comparison further.\n\nWhile both Falcon and SDP leverage partial denoising for policy acceleration, Falcon offers **2 core advantages:**\n\n- **Training-free and plug-and-play**:\n    Falcon does **not require task-specific training** or modifications to the diffusion model. In contrast, **SDP must be retrained** on each task with a specially designed noise corruption scheme (see SDP Section 3.3) to enable recursive denoising. This limits SDP’s adaptability and ease of deployment.\n- **Lower memory overhead**:\n    Falcon uses a **threshold-based selection mechanism** to maintain a compact buffer (20–50 entries). SDP, by design, stores a full horizon × noise-level buffer (e.g., 16×100 entries). As shown in **Table 7 on our [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**, Falcon adds only **+12MB** of memory compared to DDPM, while SDP adds **+26MB**—a significant difference for resource-constrained settings.\n\nIn both **simulated tasks** (**Tables 4–8 in our [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**) and **real-world experiments** (**Table 1 in our [link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede)**), Falcon achieves **comparable or higher success rates than SDP**, while also achieving **higher speedups and lower runtime** (e.g., 0.14s vs. 0.23s in real-world grasping, . These results demonstrate that Falcon not only accelerates diffusion policies but also transfers effectively to physical robot systems.\n\n**Figure Improvement (Regarding Figure 1)**\n\nThank you for the helpful suggestion. We have revised **Figure 1** to improve clarity and visual structure, now included in our updated submission and in the **[link](https://anonymous.4open.science/api/repo/506Falcon/file/material.pdf?v=69217ede) (Figure 3)**.\n\nThe new diagram adopts a cleaner modular layout and color-coded elements to distinguish the two-stage mechanism: (1) reference action estimation and (2) threshold-based candidate selection. We believe this revision significantly improves readability.\n\n**Closing Remarks**\n\nWe once again thank the reviewer for their valuable feedback. If our responses have addressed your concerns, we would be sincerely grateful for your consideration in revising the score. Please let us know if further clarification would be helpful—we would be happy to provide it.\n\n**References**\n\n[1] Fast Policy Synthesis with Variable Noise Diffusion Models", "claims_and_evidence": "Overall, the claims made in the Falcon paper are clearly articulated and largely supported by experimental evidence provided by the authors. The paper's main claims revolve around its ability to achieve accelerated inference speed through partial denoising, while maintaining performance and multimodal expressiveness.  \n\nThe author suggest Falcon is promising for real-world robotics. However, all validations are conducted exclusively in simulation environments. Real-world robot experiments are absent.", "methods_and_evaluation_criteria": "Yes, the proposed methods and evaluation criteria presented in the Falcon paper generally make sense for the stated problem of accelerating diffusion-based visuomotor policies for robotic tasks. \n\nThe authors evaluate Falcon on diverse and representative benchmark datasets, each suitable for validating different aspects of visuomotor policy performance.\n\nWhile the current evaluation criteria are robust and appropriate, the authors could further strengthen the evaluation: Include at least limited real-robot experiments to demonstrate practical utility, robustness, and generalization beyond simulated environments.\n\nMaintaining a buffer of previously denoised actions could significantly increase memory consumption. This may limit practical deployment in resource-constrained scenarios.", "theoretical_claims": "The derivations and mathematical transformations in the paper appear to be correct, and consistent with existing literature on diffusion-based methods.", "experimental_designs_or_analyses": "Overall, the experimental designs and analyses are sound and valid, and align well with standard practice in evaluating diffusion-based visuomotor policies. The chosen benchmark environments and evaluation criteria are appropriate. Additionally, the baseline methods (DDPM, DDIM, DPMSolver) are well-selected and relevant, ensuring fair comparisons.\n\nThere are some limitations:\n\nExperiments were conducted exclusively in simulated environments, leaving uncertainty regarding real-world applicability and robustness.\n\nComputational efficiency is primarily measured by NFE, with no explicit runtime or memory consumption data provided.\n\nThe proposed method relies on hyperparameters, requiring task-specific tuning for optimal results, potentially complicating practical adoption.", "relation_to_broader_scientific_literature": "The contributions of the Falcon paper are closely related to several areas of existing research in diffusion models and robotic policy learning.\n\nFalcon builds explicitly upon the foundational formulations of Denoising Diffusion Probabilistic Models (DDPM, Ho et al., 2020) and Denoising Diffusion Implicit Models (DDIM, Song et al., 2020). Existing methods such as DDIM (Song et al., 2020a) and DPMSolver (Lu et al., 2022) accelerate diffusion sampling by reducing the number of iterative denoising steps. Falcon explicitly integrates with and further enhances these existing solvers.\n\nDiffusion policies (Chi et al., 2023) inherently model multimodal action distributions. Falcon retains this strength through careful initialization and partial denoising, contrasting with distillation methods that often reduce multimodality.", "essential_references_not_discussed": "Yes, there is an important related work that the Falcon paper did not mention, namely the \"Streaming Diffusion Policy (SDP)\" proposed in the paper titled \"Fast Policy Synthesis with Variable Noise Diffusion Models\".\n\nSDP also proposes to accelerate diffusion-based policy generation through partial denoising, sharing conceptual similarities with Falcon. Specifically, SDP leverages the insight that partially denoised action trajectories can be generated significantly faster, enabling efficient inference without a large reduction in policy quality. Importantly, SDP is validated in both simulated and real-world robotic tasks, clearly demonstrating its effectiveness in realistic conditions.", "other_strengths_and_weaknesses": "Falcon creatively combines existing ideas from diffusion models, Tweedie's formula, and sequential dependency exploitation. Although each component individually is known, the particular combination is novel and cleverly addresses a key practical challenge—slow inference speed.\n\nHowever, the paper does not sufficiently address the complexity, memory overhead, and scalability concerns introduced by buffer management. Furthermore, the approach lacks validation through real-world robotic tasks, as evaluations remain limited to simulated environments.", "other_comments_or_suggestions": "Figure 1 is somewhat complex and could benefit from simplification or clearer annotation.", "questions_for_authors": "Falcon currently requires manual tuning of hyperparameters. Could you clarify how sensitive Falcon's performance is to these hyperparameters across diverse tasks, and discuss whether you've explored any automated or adaptive methods for tuning them?\n\nCould you clarify the computational and memory overhead of maintaining the latent buffer in practical deployment scenarios?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes a method to speed up inference with diffusion policies while preserving multimodality.
Several gaps in the empirical evaluation emerged during the review phase, but the authors have thoroughly addressed them. I'm positive that adding the additional results to the final version will make it much stronger.

There remains an issue with the motivation of the proposed approach: claims on the limitations of existing acceleration techniques are not fully and convincingly supported by empirical evidence. For the final version, some further work is needed to adjust the narrative of the paper and clarify the intent and scope of the contribution, as the authors wrote "not to replace distillation or ODE solvers, but to offer a practical, training-free alternative that leverages temporal structure for faster inference in diffusion policies". 
Given the effort already put by the authors in the rebuttal phase, I trust them on fixing this presentation issue for the camera-ready. In doing so, please also consider that some observed shortcomings of alternative methods (e.g. mode collapse of consistency policies) may be due to the implementation. I suggest to acknowledge that further experimental evaluations will be needed to establish the practical advantages of your method in interesting applications.
Decision: Accept (poster)