Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Authors: Junhyuk So
Chiwoong Lee
Shinyoung Lee
Jungseul Ok
Eunhyeok Park
Venue: NeurIPS 2025 poster ()
Keywords: Robot Learning
Behavior Cloning
Diffusion Model
Guidance
Primary area: applications

Abstract: Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies with open-loop (OL) control, where actions are generated via a diffusion process and executed in multi-step chunks without replanning. While this approach has demonstrated strong success rates and generalization, its inherent stochasticity can result in erroneous action sampling, occasionally leading to unexpected task failures. Moreover, OL control suffers from delayed responses, which can degrade performance in noisy or dynamic environments. To address these limitations, we propose two novel techniques to enhance the consistency and reactivity of diffusion policies: (1) self-guidance, which improves action fidelity by leveraging past observations and implicitly promoting future-aware behavior; and (2) adaptive chunking, which selectively updates action sequences when the benefits of reactivity outweigh the need for temporal consistency. Extensive experiments show that our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks.

Improving Generative Behavior Cloning via Self-Guidane and Adaptive Chunking
Junhyuk So Chiwoong Lee*?, Shinyoung Lee", Jungseul Ok! Eunhyeok Park 1.2
Department of Computer Science & Engineering
Graduate School of Artificial Intelligence POSTECH, South Korea
(junhyukso, chiwoonglee, shinyyungg jungseul, eh. ww..paa.kppstteccchcoooo kr
Abstract
Generative Behavior Cloning (GBC) is simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies with open-loop (OL) control, where actions are generated via a diffusion process and executed in multi-step chunks without replanning. While this approach has demonstrated strong success rates and generalization, its inherent stochasticity can result in erroneous action sampling, occasionally leading to unexpected task failures. Moreover, OL control suffers from delayed responses, which can degrade performance in noisy or dynamic environments. To address these limitations, we propose two novel techniques to enhance the consistency and reactivity of diffusion policies: (I) self-guidance, which improves action fidelity by leveraging past observations and implicitly promoting future-aware behavior; and (2) adaptive chunking, which selectively updates action sequences when the benefits reactivity outweigh the need for temporal consistency, Extensive experiments show that approach substantially improves GBC performance across wide range of simulated and real world robotic manipulation tasks. Our code is available at htttt::///// http://gtttt..tt// com/ www./ommmnuniaee junCuukoSAuk
Introduction
With the rapid advancement of generative models [12 4 across a wide range of domains 55 6 7
g
their adoption in robot learning also accelerating 10 12.. One compelling direction Generative Behavior Cloning (GBC), which reinterprets the classic problem of Behavioral Cloning (BC) using the modern generative models. In traditional BC, expert demonstrations, pairs of observed states and corresponding actions, are collected to train a model that maps observations to actions. GBC extends this idea by leveraging the strong generalization capabilities state-of-the-ar generative models to learn this mapping more effectively. Recent studies (4 9 [0] show that GBC can handle complex sequential decision-makinn tasks across diverse environments using only supervised signals, greatly simplifies sample collection and training process without the need of intricate reinforcement learning.
Among recent trends, one particularly notable line of research is the Diffusion Policy model ( By adapting the score-based diffusion process originally developed for vision tasks, this approach enables sequential action generation through iterative refinement in a stochastic action space. This method has demonstrated significantly higher success rates compared prior works [I5 [6], representing
promising direction in BC. In particular, the integration of open-loop (OL) control, where single observation used to generate sequence of future actions, combined with the powerful generalization capability of diffusion models, leads to improved temporal consistency, higher effective
"qqual contribution
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Difftarion Step
robot move
cup mope (senckertc)
AP
1
s)
Eo (A
plais)
Pss(a)
p(nin))
Pse[e]
(a) Self-Guidance (SG)
(b) Effecc Self-Guidance
Figure Illustration of our Self Guidance(SG). By using the past state distribution as negative guidance, SG effectively sharpens the distribution or proactively reacts to environmental perturbations.
control frequencies, and ultimately smoother, more stable motions with substantially better overall performance.
However, this approach also comes with inherent limitations. Owing to the stochastic nature of diffusion-based sampling, there remains non-trivial risk of generating erroneous actions that can result in task failure. In OL control, even a single poor action can unfold over multiple consecutive time steps, leading to a significant drop in performance. Additionally, OL control lacks the ability to respond promptly to unexpected disturbances, making it particularly fragile in noisy or dynamic environments. Closed-loop (CL) control, where actions are generated at each time step based on reactive alternative. However, it introduces different challenge: the difficulty of maintaining temporal consistency. Because diffusion models sample stochastically at every step, CL. control often suffers from jittery or unstable behavior, which can severely degrade performance. These limitations raise two critical questions: How can we increase the likelihood of sampling high-quality actions? And how can we achieve both reactivity and consistency while leveraging the strengths of diffusion policies? Addressing these questions is essential for unlocking the full potential of diffusion-basee decision-making systems in real-world applications.
real-time observations, offers
In this study, we address these two fundamental challenges in diffusion-based control. First, we introduce novel form of self-guidande that incorporates negative score estimates, derived from prior observations, into the diffusion denoising process. While diffusion guidance has been extensively studied in image generation improve sample quality [17 [8 19], its application to behavioral cloning remains largely unexplored, primarily due to the difficulty of defining reward signals for imitation learning [14. By leveraging information already embedded in the model's past decision, our method guides the model toward more informed, high-fidelity action modes and enables forward- looking extrapolation, all without requiring additional fine-tuning, shown in Fig. [
In addition to this, we introduce adaptive chunking, a control mechanism that updates action chunks only when the benefits of increased reactivity outweigh the need for temporal consistency. This strikes a dynamic balance between the responsiveness of closed-loop control and the stability of open-loop planning. By combining self- guiddnee with adaptive chunking, method significantly improves the performance standard Diffusion Policy and other baselines. Extensive evaluations across simulated and real-world robotic environments demonstrate that approach outperforms Vanilla Diffusion Policy by 23.25% and the state-of-the- BID by 12.27%, while reducing computational cost by factor of 16.
2 Preliminary and Related Works
This paper explores methods to improve BC performance using diffusion policy [.. We first introduce the fundamental principles behind diffusion models and GBC, and then provide clear comparison between OL and CL control, emphasizing the strengths and limitations of each.
2.1 Diffusion Models
Diffusion Probabilistic Models (DPMs) 20 have emerged as powerful generative framework, where data generation is modeled as a gradual denoising process starting from a pure Gaussian distribution. Instead of directly learning the data distribution, DPMs aim to learn the transition

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from noise prior Pr (z) to the target data distribution (xa This generative process was first formalized by Denoising Diffusion Probabilistic Models (DDPM) 4. In DDPM, the forward process q is defined as fixed Markov chain that incrementally corrupts the data by adding Gaussian noise with variance schedule Ag E (0, 1) over time steps 1,....??
<(44 | xx-1) = N (x:: V1 B x 8.I)
By leveraging the properties of Gaussian distributions, one can sample x; at any timestep t directly from the original data Xu, without sampling all intermediate steps:
q(x,
xo) N (x+: vo, Xo: (1 (1-a,)]) T)
where = -1 3r and ā₂ = II ag denote the accumulated noise schedule. The reverse process, which learns recover clean data from noisy observations, also modeled as a Gaussian distribution. It is parameterized as:
(x x,) =
(xe, (), [x, (x,,)) ())
Here, (Xe( t) and (x,,,, t) are typically predicted by a neural network. In many implementations, the variance Eg is fixed to a predefined schedule (e.g.. I,I,, while the mean p40 is derived from noise prediction network 6g 69(X;, f), trained estimate the noise from the noised input. This formulation allows the model to gradually denoise x; over time, ultimately recovering a clean data from the noise distribution. DDPM [4] further demonstrates that predicting the injected noise is equivalent to minimizing a reweighted variational lower bound of the data log-likelihood. This leads remarkably simple training objective:
Ssimple (0) EE ([,," [1,T" eo ||e - E + v1 āze, t)]l
This formulation trains the network Eg to recover the original noise from a noisy sample, effectively teaching to reverse the diffusion process. Building upon this, Song et al. [21] showed that the reverse diffusion process can also be interpreted as solving Stochastic Differential Equation (SDE)
or an equivalent Probability Flow Ordinary Differential Equation (PF-ODE):
dx dx= [((x)) - -0:00-99100 gt)
(x) VxlgPVx
dt.
(5)
Here, the drift term involves the score function Vx lo which approximated by neural network s0(x, t). When the generative task is conditional, for example, guided by class labels, text prompts, or environment states, the score network trained to predict the conditional score, so (x c) x Vx lo
c). This allows the model to generate samples from conditional distribation p(x x e). enabling controllable generation tailored to various downstream tasks.
2.2 Diffusion Policy for Generative Behavior Cloning
With this understanding of diffusion-basse generative modeling, we now explore how these principles can be applied to the domain of control through GBC. Let us consider a demonstration dataset
D = (T where each trajectory Tj {(s1) (5) aa T3 ) consists of sequence of state-action pairs collected from human experts. In this work, we train diffusion policy model [ aiming to learn an implicit policy distribution Pa(a; st) instead of deterministic mapping from states to actions. This distributional approach enables the model capture the diversity plausible actions of expert behaviors. The training is performed by maximizing the log-likelihood of expert actions under the learned policy, using the following BC loss:
LBC = Bee po (arls:)).
Specifically, at time step we denote the action chunk as A, = auth where d, is the dimensionality of each action. The diffusion policy learns to model the distribution over such action chunks using the following training objective, which mirrors the denoising score matching loss of Eq.(4):
(D (lA.s))-Dee
co(Af, , As =
(7)
3

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ag. represents a noised version of the action chunk A, at diffusion step k where the noise schedule follows standard DDPM notation: ag During inference, the model samples a full action chunk at ~ p[a H (1),) conditioned on the current state. The first h actions of this chunk are then executed without replanning. In this setting, H is referred to as the prediction horizon, while h is the action horizon. By learning to predict joint distribution over long-horizon action sequences, the diffusion policy inherently acquires implicit long-term capabilities.
Here, A = VāgA + VT-
3g, and o = I
== 0g.
2.3 Trade-off between Open Loop and Closed Loop Controls
We define CL control as the case where action horizon h = 1, and OL control as the case where h H/2, typically H 16, h 8, following the diffusion policy convention [1 OL control is inherently vulnerable to unexpected disturbances that may occur within its h-step execution window as the entire actions fr is generated based solely the past state 8,. (E.g: It's corresponding to 0.25s in 30Hz control frequency) In contrast, CL control replans at every step (h- = 1), allowing it react immediately to sudden changes in the environment. However, this frequent regeneration often compromise long-term planning and disrupts consistency between consecutive actions. This limitations reflect an inherent trade-off between consistency and reactivity, which must be carefully balanced in control design.
Due to its importance, several studies have attempted to address this inherent trade-off. ACT Policy [22,, for instance, proposes the use of Expomential Moving Average(EMA), which ensemble current and past predictions enhance temporal consistency. Most recently, BID 233 proposes test- time search strategy that samples multiple candidate actions and selects the optimal one using two criteria: (i) backward coherence, which prefers actions that are most consistent with the previously executed ones, and (ii) forward confrast, which favors candidates that differ significantly from those generated proposed by a separate 'negative' (i.e., undesirable) model. While BID yields respectable performance gains, it comes at the cost of significant computation and inference latency, due to the need evaluate numerous candidates and maintain an auxiliary model during inference.
2.4 Limitations of Prior Score Guidance in Diffusion Control
While diffusion policies sample actions a ~ po(a st) based the current state st, the inherent stochasticity of generative models introduces the risk of producing low-fidelity samples, that is, actions with low compatibility or likelihood under the given state. Fig. 2 (a) illustrates the distribution of action chunks generated by a Vanilla Diffusion Policy [9] As shown in the Fig. a non-negligible subset of samples exhibits ambiguous or intermediate behaviors. These low-fidelity actions lead to degraded task performance, as shown in Fig. (c). This issue becomes even more critical in stochastic environments, where the agent must rapidly adapt to newly observed states 8t.
useful analogy comes from text-to-image generation, where outputs may deviate from the intended prompt. In such settings, users can simply discard unsatisfactory images and regenerate new ones. However, in sequential control tasks, this kind of post-hoc selection is often infeasible. A single erroncous action during rollout can lead to task failuree making fidelity essential for reliable control.
How, then, can we sharpen the distribution to filter out low-probability samples and enable rapid adaptation to changing states? A widely adopted approach in the image generation domain is Classifier-Fres Free Guidance (CFG) 18, which modifies the denoising score during the diffusion process to steer the model toward more desirable outputs. Specifically, CFG applies the following guidance:
CFG
&new + (1+w) co(x. 8t) w co co(200) 0)
(8)
Here, w E [0, +oo] is referred as the guidance scale and 9 denotes null (unconditional) Recall that the noise prediction Eo in diffusion models is proportional to the score of the data distribution, i.e., cx t, c) x Vx, log P: (x, (c). Under this formulation, the modified score leads to a sampling distribution of the form (o(a]s))
(p(a|st)/p(a)) distribution is effectively reweighted by a reward signal-namely, the classifier probability p(s; a).
& po(alsı) (p(s)la))", where the original
Although this guidance mechanism has proven effective in the image generation domain [88 we
observe that it does not translate well sequential decision- -akinn tasks, as demonstrated in Fig c). and similarly reported in prior work [14]

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0.753
0
04
false posilive
0,79
0.2
(a) w/o SG (Vanilla)
(b) with SG
(c)
Figure 2: (a) Visualization of the action distribution of Diffusion Policy (DP) 9 (b) The sharpened distribution after applying our Self-G.uidaneeSG). (c) Their respective performances. Standard DP often generates low-fidelity actions, which can harm sequential control performance.
which replace the unconditional output used in CFG with conditioned output from undertrained checkpoint, denoted as cø (Ir, 8t). This method builds on the insight that CFG's score modification can be interpreted as an extrapolation away from the
Another alternative AutoGuidance (AG) 19
output of negative or 'bad' model, thereby enhancing the desired 'good' distribution [19). The modified score of AG is computed as:
AG
énow f (1+ w) c0(2, st) - w cgr (2,
(9)
As shown in Fig. (c), AG significantly improves performance, highlighting the importance of filtering out false-positive actions. However, despite its effectiveness, AG has several limitations: (i) AG requires additional checkpoint, doubling storage requirements; (ii) it relies model weights (0 and 0'), which requires computing both noise predictions in multiple inferences; (iii) the selection of the 'bad' checkpoint /' introduces an additional hyperparameter.
separate
3 Methods
Motivated by the trade-off's and overhead observed in prior approaches, present two novel methods that simultaneously improve reactivity and consistency in GBC, without requiring extra training or architectural changes. These techniques designed to be lightweight and glug-and-play making them easy to integrate into existing frameworks while delivering significant performance gains.
3.1 Self Guidance: Improving Fidelity and Reactivity of Diffusion Policy
Departing from prior methods that rely on auxiliary models handcrafted guidance signals, introduce a self-guided mechanism that is simpler, more efficient, and surprisingly more effective. Rather than introducing external guidance sources in previous work, we propose novel self- referential strategy that conditions on the model's own recent outputs-eliminatie the need for extra models, tuning, compute. Specifically, our Self Guidance (SG) formulated follows:
SG Ênew (1 + w) c0(z,, st) c0 st-At).
(10)
All that is required in SG is a single batched inference pass, using concatenated conditioning input composed the current and past states, (5t,8t-rr) This simple design makes SG highly efficient in both implementation and runtime, which is especially advantageous in resource-constrained scenarios.
For more comprehensive understanding of SG, we provide deeper analysis of its sampling behavior. Similar to CFG, SG modifies the sampling distribution follows:
Push-T Performance
0.8
(_ pa (o((e))))
Phew (a) oc pe(a))st)
(a (p
(11)
This formulation encourages the model to assign higher prob- abilities to actions that deviate from those conditioned on the past state 8:-Ar, effectively guiding the model adapt more rapidly to the newly observed state st.
2.2
Figure 3: Effect of SG guidance
scale(w) on varying noise levels (P)
5

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1.0
Cosine similarity
0.7
0.6
Attempt
Transporting
Attempt to place nut rod
Task Complete
grab the
0.4
0.3
25
50
75
100
125
150
175
200
Timestep
Open-loop
Closed-loop
Figure 4: Similarity between actions from a previously planned chunk and newly replanned actions at each time step. The similarity tends to be high during simple movements (e.g., moving, transporting). Conversely, it tends to be low when high precision is required (e.g., attempting to grasp).
To give qualitative validation, analyze how guidance strength affects overall performance under varying levels of stochasticity. In Fig. 3 the x-axis denotes the guidance weight w, while the y-axis shows the average final reward over T00 episodes. As the level of stochasticity increases, the optimal guidance weight rises accordinlly-indicattinn stronger guidance is beneficial under greater uncertainty. Notably, even in the absence of injected noise, SG significantly outperforms the vanilla setting (w = 0), demonstrating its effectiveness.
that
To deepen understanding of the SG mechanism, present additional theoretical perspective based on temporal extrapolation. Under this view, Eq. 10 can rewritten as:
Enew f (1 w) ca( sx) + w (2 eæ (2 st) - E0(2, st- (1 w) c0(2, st) + w (ce(x, St-A:)))
(12)
(13)
Assuming At is small and co(x, s) is locally smooth and differentiable with respect to the state the term (z, St) co (z St st-Ar) ) can be interpreted as first-order approximation of co(x, st+ae), with higher-order terms O((A()²) being negligible. With this interpretation, the guidance mechanism effectively encourages the model sample from a modified distribution: Pnew (at) x po [8t) 1-w (o(ar(st+ar)) which represents weighted blend between the current state 8; and an extrapolated future state 8t4 This allows the model to generate actions that implicitly anticipate short-term future dynamics, thereby improving its ability adapt rapidly and respond proactively to changes or disturbances the environment.
3.2 Adaptive Chunking: : Improving Consistency while Reactive
In addition to SG focusing on improving reactivity through more adaptive sampling, we now turn our attention to another key challenge in sequential control: maintaining temporal consistency without sacrificing responsiveness.
Due to its stochastic nature, diffusion policy tends to be less compatible with CL control, often exhibiting issues such as jittering idling. On the other hand, the main limitation of OL control is its lack of reactivity, which leads to significant performance degradation in noisy environments.
Importantly, the effectiveness of each control mode depends heavily on the characteristics of the target operation. For tasks that require delicate and precise actions, such as grasping an object, the acceptable action space narrow, and motor deviations must be minimal. In such cases, the instability typically associated with CL control is negligible, while its reactivity provides a clear advantage in responding to external disturbances. Conversely, for tasks involving large-scale movements, such as transporting or lifting an object, the action space is broader, and step-by-step replanning in CL control introduce unnecessary acceleration changes, often leading to task failure. In these scenarios, OL control more stable and preferable. Therefore, both CL and OL control offer distinct advantages depending on the context, highlighting the need for action-aware adaptive control strategies.
Adaptive Chunking Based on this observation, we propose an adaptive chunking method that selectively maintains open-loop execution when consistency is high, and reverts to closed-loop

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0.9
PashTT
65
0.5
0.9
Square
10
Transport
0.5
Average
Succets Rare
0,4
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0.2
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Average
Succers Rabe
65
0.91
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0.
0.
677
0
08
Figure 5: Simulation Experiments : Stochastic(top) & Static(bottom) : Performance comparison in
the 6 simulated environment. Results are averaged over 100 episodes across three random seeds.
Method
P=0
P=1
P-2
P=3
Vanilla 9
0.496 + 0.080
0.322 + 0.043
0.231 + 0.032
0.204 0.023
EMA 22
0,456 = 0.089
0.306 # 0.012
0,229 0.030
0.204 0,018
BID [3
0.652 = 0.079
+454:00022
0.236 0.017
0.200 0.020
Ours
0.819 # 0.049
0.718 = 0.026
0.413 0.028
0.261 0.017
Table 1: Comparison under different levels of stochasticity. The performance are evaluated on Push-T task and average over 100 episodes across 3 random seeds.
control when reactive updates are needed. Specifically, the model continues to use a previously planned action chunk long as the similarity between the first action in the chunk and the newly generated action remains above certain threshold.
Let Aqueue denote the action chunk queue, de:+ H T(a 8t) the newly predicted action chunk, and T the similarity threshold. The update rule is defined as:
enqueue (0)+ if cros(Aqueec (0), a()) 2 T
Aqueue
(14)
@ H1
else,
where cos(-) denotes cosine similarity, At each timestep, the first action in the queue is dequeued and executed: a Aqueue- dequeue ().
This adaptive strategy enables the controller to operate in a closed-loop fashion during high- precision phases and switch to open-loop execution when exact actions are less critical. As result, it effectively mitigates compounding errors while avoiding the typical problems closed-loop control such as jittering and idling. Fig. Fillussraaes the similarity between actions from a previously planned chunk and newly replanned actions, along with the corresponding control mode selected by our adaptive chunking scheme. By dynamically selecting the appropriate control mode based the execution phase, our method achieves significantly higher success rates across a variety of scenarios.
Experiments
To validate the effectiveness of proposed method, conduct experiments across various tasks and environments, ranging from simulation benchmarks to real-world applications. Moreover, we perform extensive ablation studies to investigate the impact and performance contributions of the different components integrated into our approach.
4.1 Simulation Experiments
We first evaluate the performance of our method on behavioral cloning tasks within six simulation environments. These include simple tasks like PushT [], standard benchmarks from Robomimic 24 and the particularly challenging long-horizon Kitchen [25] environment. Success Rate is used for

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Top
(a)
(b) Initialize
(c) Pick
(d) Perturbation
(e) Place
Figure Real world experiment. (a) Experimental setup (b)-(e) Pick-and-place example.
main metric for most tasks, except for Push-T, which used target area coverage. For fair comparison, we endeavor to follow the evaluation setups of [23], with the primary modification being the use of a DDIM-30 [26] solver instead of the DDPM-100 [4] solver employed in the original work. Detailed setup configurations and results obtained using other solvers are included in the supplementary.
Baselines To demonstrate the effectiveness of our method, we conducted experiments comparing it not only against the Vanilla Diffusion Policy but also against two other inference methods:
Exponential Moving Average (EMA): Introduced in [22), which also called temporal ensembling.
During inference, actions mixed with the previous action using ratio A: A. X A + (1
A) A₂ to enhance action smootheness. We set À = 0.5.
state-of-the-art inference method for behavioral cloning that employs heavy test-time-search to select the optimal action sequence for given state. We follow the default settings proposed in the original BID for fair comparison. Please refer to 22]
Bidirectional Decoding (BID) 23]:
Problem Setup We consider two distinct problem setups as follows:
(i) Stochastic: Following [23] we introduce temporally correlated action noise during the manipula- tion task execution to simulate actuator noise or external disturbances. In this setting, closed-loop control is employed for fair comparison.
(ii) Static: We assume an ideal, clean environment without any external disturbances or noise. In this setting, open-loop control is utilized for all methods.
Results Fig. iillsstrate the performance of our method compared to baselines in both stochastic(top) and static(bottom) cases. As shown, while EMA often improves performance, it results in performance degradation on some tasks. contrast, both BID and our method consistently enhance performance across the evaluated simulation environments. However, BID not only worse than ours in performance but also it requires significant computational overhead 16x more FLOOPs and 2x slower latency. Our method, conversely, achieves superior performance than Vanilla DP by 23.25% and BID by incurring additional computational cost.
4.2 Real World Experiments
Figure Param. Sensitivity of EMA and AC.
We further validate the practical applicability of our method through real-world experiments. Specifically, We utilized Lerobot(Huggingface)) (27] implementation of Diffusion Policy(DP), and deployed it on SO-100 low cost robot arm (28).
EMA
AC
Perf, Goin
8. am
We employ 3-camera setup, top (bird's-cee s-eye), front,
and wrist views visualized in Fig. a[).. All exper- iments are conducted on one A6000 GPU server with DDIM-10 Solver with 30Hz standard visuomotor control frequencies.
Problem Setup We design simple pick-and-ples task using pen holder and cup. The task involved picking up a pen-holder grip and placing it into a cup, as shown in Fig. 7 Similar to Sec. 4.1 we evaluated performance under two conditions: (i) Stochastic The target cup is moved during task execution to introduce environmental disturbance; (ii) Static : The target cup remained stationary.
Results In Fig. report the Success Rate of Vanilla D and Ours accross 20 trials. As shown in Fig. our method demonstrated stronger performance than the vanilla, especially in dynamic environments. This confirms the effectiveness and robustness of our approach beyond simulation and

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[5c] P-0
[56]
[56] P-2
[Se] P
[S6] P-4
[AG] Pw0
[AG]
[AA] P=2
[AG] P-3
[AG] Pwd
Push-T Perforaance
0.8
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1.1
0.24
0.0
0.0
0.5
1.0
1.5
2.0
2.5
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W
Figure 8: Effect of SG & AG guidance scale(w) on varying noise levels (P)
its applicability real-world scenarios with noisy hardware and potential disturbances. Moreover, while BID 23 shows halting behavior
4.3
Ablation Studies
the performance results under varying environ- mental noise scale (P) PushT task. The detailed experimental setup is in appendix. As shown, the performance of baselines degrades rapidly as noise scale increases. In contrast, ours maintains performance and outperforms other methods, showcasing effectiveness of Self Guidance's 's reactivity enhancement and robustness of Adaptive Chunking.
Different Levels of Stochasticity
Comparison with AutoGuidance [1]. conduct a detailed comparative study between our Self-Guidance (SG) and AutoGuidance (AG) [19]. In Fig. 15 plot the performance of both methods across different guidance scales, ranging from w (no guidance) to w 3, in various environment noise levels (P). As shown, while both methods improve performance with guidance, our SG consistently achieves a higher peak performance than AG across all evaluated noise levels. Moreover, AG's performance degrades rapidly as the noise scale increases, whereas our SG maintains its robustness introduces a significant computational burden, including storage costs for the weak model's ' weights and increased effective latency due to inability due to inability to perform. In contrast, SG incurs no computational overhead while delivering superior performance.
To highlight the superior performance of our method, we
in noisy environments. Finally, AG
Sensitivity Analysis While EMA 22 can achieve good performance with an optimal decay rate, it is often overly sensitive. In Fig. 6, we present a parameter sensitivity analysis comparing EMA's decay rate Ả with the threshold r of our Adaptive Chunking (AC), As shown, EMA exhibits significantly different optimal decay rates across tasks. In contrast, our AC demonstrate consistent performance trends, highlighting their notable hyperparameter robustness and real-world applicabilityy
Individual effect of SG and AC In Fig. 10 we present an ablation study evaluating the impact of applying only Self Guidance (SG), only Adaptive Chunking (AC), and both components (Ours). As shown, the results indicate that while using either SG or AC alone improves performance over the baseline, the combination of both yields the best results.
Discussion Extension to VLAS
While mainly demonstrated our method with diffusion policy, method can be extended to any behavior cloning framework that utilizes action chunking and probabilistic modeling of action space. To further validate the effectiveness and generality of approach, conducted experiments with two modern, state-of-the-an
Aasion-Language-Attin models (VLAs): Fo and OPeNVLA-OFT.
is a recently proposed, state-of-the-e VLA pretrained on web-scale data, which demonstrates potential of leveraging embedded world knowledge of foundation models for ceneral-purposs. robotic planning and control. Specifically, it employs an early-fusion approach to process multimodal inputs and directly generates chunked actions through a denoising process. We integrated our SG method directly into this denoising stage also with AC.
(pi-zeer) 12

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Vanilla
Ours
Success Rate
1.0
Success Rate
0.9
0.6
0.7
0.6
0.4
0.5
Stochastic
Static
0.2
Stochastic
Static
Figure 9: Real World Experiments We compare Success Rate(%) be- tween Vanilla [9] and Ours under stochastic and static scenarios.
Figure 10: Ablation study for our methods We depict individual performance of ours with average success rate across simulation benchmarks.
OpenVLA-OFT 229 is another web-scale, fine-tuned Vision-Language-Action (VLA) model de- signed for robotic tasks, which also utilizes action chunking(OL) for control. However, since OpenVLA-OFT not diffusion fiffusion-bbsed,, our original SG method cannot be directly applied. To address this, we introduce variant of our SG, inspired by recent LLM guidance techniques, activation steering [30].
Specifically, During the forward computation of i-th Trans- former blocks TV, we inject negative guidance using past activation A follows:
Method
P=1
P=5
To 12
82.0%
12.2%
closed-loop
73.4%
14.4%
A&+1 + (A)) + (T' -T())))(;;
(15)
Ours
83.8%
19.9%
Open
29
82.0%
12.2%
This formulation in Eq. [5] is analogous SG (Eq. TO where we now applies guidance in feature-space instead of denoising output space in diffusion.
closed-loop
73.4%
14.4%
Ours
83.8% %
19.9%
Experimental Results We use the LIBERO-Spatial bench- mark [31 to evaluate performance. Similar Sec, 4.2 we LIBERO with different noise scale P adopt stochastic environment where target objects are in motion. Detailed experimental settings are provided the Appendix. Table 2] we compare the performance of original Tig 12] and OpenVLA-OFF [29), which executed on open-loop control, and its closed-loop variant, finally with ours. As shown, the performance of vanilla VLAs with open-loop control decreases significantly in stochastic environment (large P). While the closed-loop version shows some improvement high-stochasticity regions, this improvement is marginal. contrast, To and Open VLA-OFT combined with our method achieves the best performance across all tasks, highlighting its broad applicability and potential for future extensions VLA-style models.
Table Performance of OpenVLA on
6 Conclusion and Limitations
In this work, we demonstrate that Generative Behavior Cloning, particularly Diffusson Policy, can suffer from low-fidelity issues and reactivity
trade-off. To address these, we propose two novel techniques: Self-Guidance, which injects past score predictions as negative guidance, thereby enhancing fidelity and reactivity; and Adaptive Chunking, which dynamically balances reactivity and consistency. Our experimental results show that our approach consistently improves robotic control quality across diverse scenarios, including both simulation and real-workh applications.
Limitations One limitation of adaptive chunking is its computational cost, which is comparable that CL control due to step-wise similarity evaluations. Nevertheless, we view this as a valuable opportunity for future work, and believe that designing more computationally efficient similarity measures could further enhance the practicality of adaptive chunking.
Acknowledgment
This work was supported by ITTP and NRF grant funded by the Korea goVernmen((MSTT) (No. RS-2019- 19919-919999666 RS-2003-002113611, RS-2024-00457888),
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A
Experimental Details
Hyperparameter Settings are summarized in Table.
The hyperparameters used in simulation experiments in main paper
Hyperparameter
Value
Observation history
2
Prediction horizon (H)
16
Diffusion Scheduler
DDIM-30
of Adaptive Chunking
0.97 (CL), 0.99 (OL)
At of SG's past state st-At
w for SG [Stochastic]
2.23 (Push-T)
0.86 (Square)
1.5 (Lift)
1.3 (Can)
1.17 (Transport)
0.62 (Kitchen)
w for SG [Static]
4.42 (Push-T)
1,25 (Square)
1,29 (Lift)
0.8 (Can)
1.02 (Transport)
0.64 (Kitchen)
Table 3: Additional hyperparameters for simulation experiments.
Implementation of Perturbation P In P noisy environment setting, we implement the distur- bance by moving the T-block in fixed direction at a velocity P. which is the same implementation used in BID [23). The goal of this stochastic scenario is to approximate environmental disturbances, such as slipperiness or wind.
B Experimental Results with DDPM-100 Solver
Fig. 3(main) reports the success rates for six tasks evaluated under two environments (Stochastic & Statie) using the DDIM-30 solver (26) To ensure fair comparison with prior works 9 23 we also visualize the results using the DDPM-100 solver [4], keeping all other hyper-paramedn
unchanged. As shown in Fig. [| our method outperforms Vanilla Diffusion Policy [9] by 19.63 % and BID 23 by 7.58 % on average across the six tasks in the stochastic setting. In the static environment, our method still achieves higher performance, surpassing Vanilla Diffusion Policy by 1.90 and BID by 1.23%.
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1.
PashT
10
1.0
0.9
Square
1.0
Transport
0.9
Average
Succets Rare
0.-
05-
0.8
0.7
04
0.4
0.2
0.6
Transport
Average
Success Rate
0.9
54
0.0
0.6
0.6
08
0.-
04
Figure 11: Simulation Results with DDPM-100 solver Stochastic(top) Static(bottom) Performance comparison in the 6 simulated environment. Results are averaged over 100 episodes across three random seeds.
C Comparison with different EMA Rate X
BID reports that Exponential Moving Average(EMA) 22 can perform well on several tasks,
but also that the result is highly sensitive to the decay rate X, with the optimal value differing by task. Motivated by this, we evaluated EMA over E {0.0, 0.1. ..03 for every task. Fig. 12 shows results for the Stochastic setting using representative values À {0.1, 0.3, 0.5, 0.7, 0.9}, which
include the empirically optimal value for each task. As shown Fig. T2 our method surpasses EMA on most benchmarks, highlighting both the challenge of choosing appropriate À for EMA and the robustness of ours.
Vari
1
EMA
25A0.5
EMACO
S10
Oura
PasheT
Kitrher
Lift
Square
Cen
Thansport
0.0
Average
SUcces Bare
0.5
0.
0.8
05
0,7
0.5
Figure 12: Simulation Results on the Effect of İ in EMA: Performance comparison in the 6 simulated environment. Results are averaged over 100 episodes across three random seeds. The hatched bars indicate the optimal À value for EMA in each task.
D Comparison with different similarity Metric in Adaptive Chunking
To investigate effect of other similarity metrics in Adaptive Chunking(AC)) we replaced cos(.) with the L1 and Le distances in AC. A threshold of = 0.1 performed best for norm-bat basnd metrics, while 0.97 is used for the cosine-based method. As shown in Table 4 cosine similarity achieves the best performance across all tasks.
E Similarity visualization in other tasks
To verify the generality our observation in Sec.3.2 of main, we also visualized the similarity of actions with Can task in Fig.
Similar to 4 of main], the similarity decreases noticeably during precise actions in the Can task, such grasping or placing the object into the target bin.
21

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Vanilla
L1
L2
Cosine (Ours)
Push-T
0.496 0.08
0.621 = 0.012
0.639 = 0.03
0.72 0.037
Kitchen
0.298 = 0.007
0.309 = 0.008
0.323 0.014
0.398 = 0.032
Lift
0.157 = 0.006
0.227 = 0.015
0.07
0.587 0.006
Square
0.633 + 0.04
0.583 0.015
+533
0.753 0.021
Can
0.787 = 0.015
0.687 = 0.068
0.7
0.91 0.00
Transport
0.387 + 0.07
0.25 0.03
0.25 + 0.053
0.553 0.021
Table 4: Performance comparison of different vector metrics across tasks
Cosine similarity
0.9
0.7
0.6
Attemot to grab the can
Transporting
Attempt place the target bin
Tosk Complete
0.4
25
50
75
100
125
150
175
200
Timestep
Figure 13: Similarity between actions from a previously planned chunk and newly replanned actions each time step in the Can task.
F Performance of Additional Guidance Methods
In addition to our Self-Guiiance(SG)) we evaluate two additional self guidance approaches for ablation.
Noised Observation (NO) Instead of utilizing past condition as negative guidance, we also try to utilized directly perturbed condition as bad output. Specifically,
Enew + (1 + w) (z 8t)
co(x, 8t t S 8), where 8 N(O, I)
(16)
where denotes scailing factor. We set empirically.
Time-Step Guidance (TSG) 32
Recent work 18 introduce following Time-Step guidance.
In this method, the bad output computed by perturbed denoising timestep with same condition distribution. Specifically.
Enew f (1+ w) E0(ar, St, t) c0(x, sc,
(17)
where i denotes perturbed timestep embedding i i= t + s t and s, are hyperparameters of TSG.
We set s = 2, a = 1, following default configuration TSG.
Results As shown in Fig. 14 'NO' also shows worse performance than Vanilla. While TSG outper- forms Vanilla slightly, its improvements is still marginal. Our SG shows remarkable performance improvement compared to other guidance methods.
22

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(Ours)
1.0
0.873
0.8
Success Rate
0.496
0,516
0.6
0.375
0.4
0.2
0.070
0.0
Figure 14: Compared to other guidance methods, SG achieves superior performance.
G
AutoGuidance vs. Self Guidance
To present detailed comparison between Autoguidance (AG) and Self- Gelf-Guidance (SG), depict the performance of both methods at different guidance scales in Fig. 15| As shown, our SG clearly achieves higher optimal performance than AG across various noise scales. Moreover, while AG's opti- mal performance decreases rapidly as the noise scale increases, our SG maintains robust performance in noisy environments.
[SG] P-0
[SG] P-1
[5G] P-2
[SG] P-3
[SG] P-4
[AG] PwO
[AG] P-1
[AG] P=2
[AG] P-3
[AG] Pw4
1.0
Push-T Performance
0.8
0.6
0.4
1.1
0.2
0.1
0.0
0.5
1.0
1.5
2.0
2.5
3.0
(Vanilla)
W
Figure 15: Effect SG & AG guidance scale(w) on varying noise levels (P)
H Real World Experiments Details
This section describes the experimental details of Sec.4. (.mainn... We have detailed the SO-100 robot arm 28 and camera setup, training details, evaluation details for the both inference method Vanilla DP and Ours.
Experimental Setup We perform real world experiments with S0-100 robot arm 28 with three cameras. As shown in Fig. 16 each camera records bird-eye, front, wrist view of robot arms. The input shapes of images are 1920 1080 px videos with 30 fps, but down-sample the image shapes to 224 x 224 px for training and inference. To ensure consistency with the training environment, we set the robot's operation 30 FPS during inference.
Problem Setup As simplified version of Robomimic 'Can" task 24, we consider task that robot grasps pen-holder grip and placing it into cup. Fig. (m)aii)) illustrates the total sequence
23

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(a) view
(b) Bird view
(c) Wrist view
Figure 16: Real world robot arm camera setup. We use three 1920 x 1080 px, 30fps webcam. For training and inference, input videos are down-sampled 224 x 224 px without cropping.
Hyperparameter
Value
Observation history
Prediction horizon (H)
16
LR scheduler
cosine scheduler with linear warmup 500 steps
Learning rate
le-4
Batch size
32
Train steps
240K (Early stopped with max 600K steps)
Input image size
224 x 224 px
Vision backbone
ResNet-50
UNet dimension
(256,512,1024,
Table 5: Diffusion Policy hyperparameter for real world experiments
of placing tasks. For stochastic scenario, we move the cup introduce disturbance, while static scenario maintain the position of both pen-holder grip and cup.
Training Details We make 300 demonstration episodes with lerobot open source 28 For each demonstration episodes, initial place of pen-grip holder and cup are randomly chosen while robot arm starts with same rest position. We follow the Diffusion Policy [9] training recipe with few modifications fit in real world. We use ResNet-50 33 as vision backbone with IMAGENET 34 pretrained weight, and cosine LR scheduler starts with linear warmup 500 steps. We use early stopped 240K steps checkpoint, which requires 27H with one NVIDIA RTX 6000 Ada Generation GPU and AMD Ryzen Threadripper PRO 7985WX CPU. Additional hyperparameter details are listed Table. 5
Evaluation In Sec.4.2 Sec.4.2(main), we compare with two models, Vanilla DP [9, and Ours, using the guidance weight w 0.1 for SG, and the similarity threshold as 0.99 for AC. In the static scenario, we set four types of starting points, and measure the success rate from five experimental runs at each point, total 20 episodes. In the stochastic scenario, we introduce disturbance by moving the cup by hand after the robot arm grasped the pen-grip holder. We consider each evaluation episode as failure it exceeds 30 seconds time limit drops pen-grip holder before placing cup.
I Additional Real World Experiments
We conduct additional experiments in real-world scenarios to compare the performance of our proposed method (Ours) with that of Vanilla DP 99, EMA 22 and BID 2 in stochastic scenario.
Problem Setup Similar to the previous real world experiment with the Vanilla DP 9 we perform a task that grasp pen-holder grip and placing in cup. To introduce highly stochastic scenario, the cup periodically moves in a circular path. Fig. visualizes the successful and failed samples of the task.
Baselines We collected 300 demonstration episodes of placing the pen-holder grip while the pen- holder grip and cup are in static scenario. The detailed hyperparameters are same to those presented Table. which the Sec. 4.2mann) experiment's baseline, excluding the batch size and the number of training steps. We trained a new baseline for 320K steps with batch size of 16, employing cosine warmup scheduler. For a fair comparison, we configured the baseline methods, EMA and BID, similarly to the Sect.4. (ect.4.1(mainn) experiments. For EMA, we set its decay rate À to 0.5. For BID,

---

Vart 2MA BID Dura
Success Rate
(a) Sample success from Ours
0.6
Grasp fall
Figure 18: Additional Real World Experiments: We compare Vanilla DP 2. EMA (22), BID [23], and Ours under 20 stochastic episodes. Ours achieve 70% success rates, which higher than other inference methods.
(b) Sample failure from BID
Figure 17: Additional Real World Stochastic Task: The goal is grasp a pen-grip holder and placing it to cup which periodically move along circular path. (a) Visualization of success sample from Ours (b) Failed sample from BID [23]. We observe that a few evaluation fail due to idling actions.
adopted its original settings, and choose the strong policy as 320K steps and the weak policy as 240K steps.
Evaluation We evaluated each method based on 20 task executions, each initiated from the same position. Fig. 18 shows the success rates of tasks in real-world experiments. EMA shows lower success rate than the Vanilla Diffusion Policy. Both BID and Ours achieved higher success rates compared to the Vanilla DP, but Ours shows slightly higher success rate as it performed the pen- holder gripping action more precisely. The success rate of each method was consistent with the analysis from the Sec.4. I (main) simulation experiments. As shown in Fig. 17 BID failures frequently resulted from idling actions following grasp failure, which can also be observed in Vanilla DP and EMA. However, our method experienced fewer grasp failures and exhibited no idling actions during evaluation. We found that BID inference ran at an average of 16 Hz for each action generation, so the robot operates unsmooth and halting manner. But, Ours generate actions average of 29 Hz, and move smoothly.
25


Reviews:
[{"review_id": "IXUqCCAVIP", "reviewer": "Reviewer_qBvg", "summary": "The authors investigate means of improving generative behaviour cloning policies, particularly the diffusion policy approach. They propose that one issue with these models is that they do not function as “closed-loop” policies, in which decisions are made at each state to complete a task. To address this limitation, they propose a novel self-guidance technique using subsequent states to modify the noising process during action generation. They then propose an adaptive mechanism to decide when the current action sequence differs too much from the model's output and to either continue executing the current plan or use the new action sequence. Experimentally, their approach improves control performance over baseline methods that propose alternative means of adapting action sequence generations with diffusion policies, showing strong performance improvements.", "questions": "- Line 119: Why is “h = H / 2”? \n- How fast do we need to be able to make a decision? Even in robotics, where sampling rates could make some difference \n- What is “P” in Figure 3 precisely? It says it’s noise levels, but is that referring to the standard deviation of the noise process or something else?", "limitations": "We felt the author's work could benefit from a more extensive limitations discussion. Computation time for their approach seems a much more important issue than they allocate space for.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n-  Claimed contributions are clearly stated in the introduction, making it easier to understand the objectives of the paper\n- The proposed techniques are accessible and appear to require minimal changes to utilize Diffusion policies more effectively during deployment. \n- Experiments demonstrate both real-world results and simulation outcomes. The authors conduct ablation experiments to put the mechanisms proposed in the paper into context and assess their effectiveness and utility. Inclusion of sensitivity results \n\nWeaknesses:\n\nIn terms of the method techniques proposed and experiments conducted, the only two major concerns we had were about the following:\n- No experiments were done to help motivate determine the theoretical conclusions shown in Equations 12 - 13. This intuition of “predicting into the future” seems testable where instead of using the subsequent steps, the authors could use varying distal states to calculate the $\\epislon_{new}$, e.g. instead of $s_{t - \\delta t}$ use something like $s_{t - k \\delta t}$ where $k$ is varied over. \n- Experiments considering the tradeoffs in computation by using a “closed-loop” approach. The authors briefly mention these limitations, noting that their approach may be slower if queried more frequently. If the authors performed experiments to demonstrate this tradeoff in computation time to previous approaches, it would put the consequences of their techniques' tradeoffs into perspective between performance and computation, particularly for real-world robotic use cases. \n\nOther concerns we had which are more about how to position the work:\n- We did not find the motivations for closed-loop vs. open-loop compelling justifications for the work. The horizons the author mentions (H = 16 steps) seem short, particularly if there’s a high sampling rate on a robot. If your system works at 1000 Hz, what is the difference in precision you’re losing at such a short window of prediction? As the authors point out in their limitations (line 334) regarding computation costs, the bigger issue, in our opinion, is not being able to query the model fast enough for real-time control in a closed loop system. To our understanding, diffusion is a fundamentally iterative process, so generating a single action chunk could take more time than possibly hope to query in between decisions.  We suggest that the authors focus their paper more on discussing the improvement of diffusion policy inference than on making claims about the benefits of being  a closed vs. open-loop control system, becuase we do not agree that their results support this argument well. \n\n- The work should acknowledge fields addressing similar problems more beyond the diffusion policy research literature. In our opinion, the “adaptive action chunking” mechanism is similar in motivation to the stopping function defined in the options framework [1]. Likewise, fields like control theory have also studied the value of replanning action sequences despite being able to roll out multiple steps (e.g., model predictive control predicts multiple time-steps but typically only sends one command). It’s surprising, for instance, that lines 38-45 provide  no supporting research on the tradeoffs between “open loop” and “closed loop\" approaches. We would appreciate the authors acknowledging that these motivations of their work are also present in other domains. In our opinion, these ideas (OL vs CL, stopping functions) are not novel by themselves, but instead addressing these issue specifically in the context of diffusion policies is the real focus. \n\n[1] Sutton, Richard S., Doina Precup, and Satinder Singh. \"Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.\" Artificial intelligence 112.1-2 (1999): 181-211.", "was_revised": true, "final_justification": "I maintain my original score, which was to accept the paper with some revisions as we discussed during the rebuttal which the authors addressed.", "rebuttal": "Thank you very much for your positive review and acknowledging the contributions of our work. We have carefully considered each of your points and present our detailed responses below.\n\n## Weakness 1\n> No experiments were done to help motivate determine the theoretical conclusions shown in Equations 12 - 13 ... The authors could use varying distal states to calculate the $\\epsilon_{new}$ , e.g. instead of $s_{t-\\delta t}$ use something like $s_{t-k\\cdot t}$ where $k$ is varied over.\n\nThank you for your insightful and constructive feedback. In response to your suggestion, we revised our algorithm to incorporate varying internal states and evaluated its predictive quality by computing the mean $L_2$ distance between the future actions generated by SG and the ground truth in the Push-T environment. Specifically, we experimented with the formulation :\n\n$$\n\\epsilon_{\\text{SG}} \\leftarrow (1 + w)\\epsilon_{\\theta}(x \\mid s_t) - w\\epsilon_{\\theta}(x \\mid s_{t - k})\n$$\n\nacross different values of $k$, to assess how past states influence prediction accuracy.\n\n| | k=1 | k=2 | k=3 | k=4 | k=5 | baseline ($a_t - a_{t-1}$) |\n|---|---|---|---|---|---|---|\n| $L_2$ | **8.164** | 9.616 | 10.757 | 11.389 | 12.338 | 10.667 |\n\nWe denote the baseline $\\|a_t - a_{t-1}\\|$ as the natural variance between consecutive ground-truth actions. As shown in the results, our SG method with $k = 1$ achieves the lowest $L_2$ error (8.164), outperforming the baseline (10.667) in predicting future actions. Moreover, the performance gap widens as the temporal offset $k$ increases, indicating that the accuracy of SG degrades more slowly over time compared to the baseline.\n\nThis behavior suggests that SG effectively acts as a discretized approximation of continuous-time action trajectories. As the temporal gap decreases, the prediction becomes more accurate, highlighting the model's strength under locally linear dynamics. These results provide strong empirical evidence for the effectiveness of our extrapolation mechanism, and we will include this experiment in the final version of the paper.\n\n\n## Weakness 2\n> Experiments considering the tradeoffs in computation by using a “closed-loop” approach.\n> If the authors performed experiments to demonstrate this tradeoff in computation time to previous approaches, it would put the consequences of their techniques' tradeoffs into perspective between performance and computation, particularly for real-world robotic use cases.\n\nThank you for your valuable feedback. As you rightly noted, the trade-off between performance and computational cost is a critical concern in real-world robotics. In this regard, we would like to emphasize that **our proposed method offers a more favorable performance-computation trade-off than prior approaches \\[1]**, as also demonstrated in our real-world experiments:\n\n* **Closed-Loop Latency:**\n  Similar to prior work \\[1], our method assumes that modern GPUs can support closed-loop control without compromising standard visuomotor control frequencies (\\~30Hz). In our real-world experiments using a 10-step diffusion policy on an A6000 GPU, the closed-loop controller comfortably maintains the \\~30Hz requirement. We clarify that **the main contribution of our work lies in maximizing control accuracy under this assumption**, and we validate this through extensive empirical results.\n\n* **Performance-Computation Trade-off:**\n  Importantly, we highlight that **our method is significantly more efficient than prior methods such as AG \\[6] and BID \\[1]**. These approaches require multiple inferences to stabilize diffusion control, resulting in a high computational load that often **exceeds the practical limits of the assumed hardware**, leading to reduced control frequencies. As detailed in Appendix. I, our main baseline BID \\[1] incurs \\~16× higher computation, reducing control frequency to \\~15Hz and causing unstable behavior and lower task success rates. In contrast, our method sustains stable 30Hz control without such issues.\n\nYour comment prompted us to reflect more deeply on how to better highlight one of the core contributions of our work. We sincerely appreciate the insightful suggestion and will incorporate this point into the final version of the paper.\n\n## Weakness 3\n> We suggest that the authors focus their paper more on discussing the improvement of diffusion policy inference than on making claims about the benefits of being a closed vs. open-loop control system.\n\nThank you for your insightful questions and valuable suggestions regarding the paper's position. As you pointed out, a more impactful contribution of our work is effectively improving diffusion policy inference, with our novel self guidance technique. We will be sure to emphasize this contribution more explicitly in the final version of the paper.\n\n> We did not find the motivations for closed-loop vs. open-loop compelling justifications for the work..\n\nHowever, we wish to reiterate that the contribution regarding the OL vs. CL framework is also critical contribution, especially towards improving a robot's real-world performance. Below are reasons and our responses to your specific questions.\n\n>If your system works at 1000 Hz, what is the difference in precision you’re losing at such a short window of prediction?\n\nInstead of low level control at ~1000Hz, our work mainly focuses on visuomotor policies,  which take entire camera images as input to decide on the next action (e.g., target end-effector coordinates). These policies conventionally operate at ~30Hz, which is a standard setting in this research domain. In this context, a prediction horizon of H=16 corresponds to approximately 0.5 seconds (16/30s) and is not a short-term window.\n\nIn manipulation tasks, 0.5 seconds is a significant duration where environmental changes can cause task failure. While open-loop control is the de facto standards for modern generative model-based control, research into its risks has been limited explored [1].\n\n>...the bigger issue, in our opinion, is not being able to query the model fast enough for real-time control in a closed loop system.\n\nAs you rightly pointed out, the inference speed of diffusion models is a major concern. However, as answered in Weakness 2, we operate on the assumption that modern systems are powerful enough to maintain a stable ~30Hz control loop. We believe this trend will continue and extend to edge computing, thanks to advancements in hardware and model optimization techniques.\n\n**Remark :** The challenge for closed-loop control we want to solve in our paper is no longer speed, but rather it's low performance despite their theoretical superiority by its lack of consistency and long-term planning abilities. Solving this problem is especially critical in real-world environments, where can be noisy or on low-cost robot systems. Our work directly tackles this issue to increase the performance (accuracy) of diffusion-based control systems. \n\n## Weakness 4\n> The work should acknowledge fields addressing similar problems more beyond the diffusion policy research literature.\n\n> We would appreciate the authors acknowledging that these motivations of their work are also present in other domains. \n\nFirst, thank you for your valuable insights and feedback. We agree that the concepts of open-loop and closed-loop control are well-explored topics in fields such as Model Predictive Control,  Receding Horizon [2] and Options framework [3].\nMoreover, within the context of diffusion model-based control, prior studies have also investigated these concepts. For instance, BID [1] analyzes the performance trade-offs between open-loop (OL) and closed-loop (CL), while [4] provides a theoretical analysis of the effect of the horizon. We will incorporate these acknowledgments more clearly and clarify our main contributions.\n\n## Questions\n>Line 119: Why is “h = H / 2”?\n\n- This follows standard of diffusion policy model [5] for fair comparison. We will add more description. \n\n> How fast do we need to be able to make a decision?\n\n- In the visuomotor policy tasks we focus on, the standard control frequency is typically around 30Hz. This corresponds to an action inference time of approximately 260ms in the open-loop setting (h=8) and 33ms in the closed-loop setting. As noted in the paper, such latency requirements are well within the capabilities of modern GPU-based systems.\n\n> What is “P” in Figure 3 precisely?\n\n- We denotes $P$ as a environmental noise, which is the speed of movement of T block and is the same noise setting in previous works, BID [1]. Please see Appendix.A also. We will clarify in final version.\n\n\n--- references ---  \n[1] Liu, Yuejiang, et al. \"Bidirectional decoding: Improving action chunking via closed-loop resampling.\", ICLR 2025  \n[2] Mattingley, Jacob, Yang Wang, and Stephen Boyd. \"Receding horizon control.\" IEEE Control Systems Magazine 31.3 (2011): 52-65.   \n[3] Option discovery using deep skill chaining. 2019  \n[4] Foster, Dylan J., Adam Block, and Dipendra Misra. \"Is behavior cloning all you need? understanding horizon in imitation learning.\" Advances in Neural Information Processing Systems 37 (2024): 120602-120666.  \n[5] Chi, Cheng, et al. \"Diffusion policy: Visuomotor policy learning via action diffusion.\" The International Journal of Robotics Research (2023): 02783649241273668.  \n[6] Karras, Tero, et al. \"Guiding a diffusion model with a bad version of itself.\" Advances in Neural Information Processing Systems 37 (2024): 52996-53021."}, {"review_id": "fFXEMIgGJn", "reviewer": "Reviewer_fbxt", "summary": "This paper proposes an improved generative behavior cloning (GBC) framework, specifically targeting the application of diffusion strategies in robot learning. The authors point out two main problems of current diffusion strategies in open - loop (OL) control: (1) Randomness in the diffusion process may lead to incorrect action sampling; (2) OL control lacks the ability to quickly respond to dynamic environments. To address these issues, the paper proposes two innovative methods: Self - Guidance (SG) and Adaptive Chunking (AC).", "questions": "1. The self-guidance (SG) formula (Eq. 10) uses past states $ s_{t-\\Delta t} $ as negative guidance.  \n  - How was the time window $ \\Delta t $ optimized? Was it task-specific or theoretically derived?  \n  - Were ablation studies conducted to compare SG with explicit future-state prediction?  \n2. Why not use probabilistic metrics (e.g., KL divergence) for uncertainty-aware transitions?  \n3. Does fixed-threshold chunking fail in edge cases (e.g., sudden disturbances)?\n4. Would SG’s reactivity degrade with deformable objects or non-stationary tasks?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Proposes two innovative methods: Self - Guidance (SG) and Adaptive Chunking (AC).\n2. Adapts SG to OpenVLA , showing applicability beyond diffusion models.\n3.  Empirical validation with real-world experiments.\nWeaknesses:\n1. AC’s step-wise similarity evaluations incur latency comparable to closed-loop control.\n2. Experimental design exhibits ​limited breadth in baseline comparisons. Boader comparisons would better contextualize its advancements.\n3. SG’s extrapolation (Eq. 13) assumes local smoothness of ϵ, which may not hold for highly non-linear dynamics.", "was_revised": true, "final_justification": "Your comprehensive replies fully address our technical concerns. I will raise my score to 5.", "rebuttal": "Thank you very much for your positive feedback and constructive suggestions. We have carefully considered each of your points and present our detailed responses below.\n\n## Weakness 1\n> AC’s step-wise similarity evaluations incur latency comparable to closed-loop control.\n\nThank you for your valuable feedback. Like prior work \\[1], our approach assumes that **modern GPUs are capable of supporting the computational demands of closed-loop control without compromising the standard visuomotor control frequency (\\~30Hz).** In practice, our real-world experiments with a 10-step diffusion policy on an A6000 GPU comfortably maintains the ~30Hz control rates requirement.\n\n**We would like to clarify that the primary contribution of this work lies in maximizing control accuracy under this assumption**, which we have validated through extensive experimental results.\n\n\n\n## Weakness 2\n> Experimental design exhibits ​limited breadth in baseline comparisons. Boarder comparisons would better contextualize its advancements.\n\nThank you for your feedback. To provide a broader comparison with additional baselines, we conducted further experiments on two state-of-the-art diffusion-based VLAs that use open-loop control: **\\$\\pi\\_0\\$** \\[2] and **GR00T-N1** \\[3]. Specifically, we evaluated **\\$\\pi\\_0\\$** in both real-world settings and the LIBERO simulation benchmark, and **GR00T-N1** in the real-world setting. For consistency, we followed the same experimental setup as described in Section 4.2 of the main paper for real-world evaluation, and Section 5 for the LIBERO benchmark.\n\n### $\\pi_0$\n\n| (Real) | **Ours** | $\\pi_0$ (open loop; vanilla) | $\\pi_0$ (closed loop) |\n| :--- | :---: | :---: | :---: |\n| Static | **85%** | 75% | 70% |\n| Stochastic | **65%** | 45% | 50% |\n\n| LIBERO (simul) | P=1 | P=3 | P=5 |\n| :--- | :---: | :---: | :---: |\n| $\\pi_0$ (open loop; vanilla) | 0.786 | 0.424 | 0.094 |\n| $\\pi_0$ (closed loop) | 0.786 | 0.486 | 0.208 |\n|  **Ours** | **0.828** | **0.526** | **0.222** |\n\n### GR00T-N1\n\n| (Real) | Ours | GR00T-N1 (open loop; vanilla) | GR00T-N1 (closed loop) |\n| :--- | :---: | :---: | :---: |\n| Static | **85%** | 75% | 25% |\n| Stochastic | **45%** | 20% | 20% |\n\nAs shown, **our method achieves the best performance across all settings when applied to both \\$\\pi\\_0\\$ and GR00T-N1**. While their vanilla (open-loop) versions suffer from significant performance degradation due to stochastic noise, and their closed-loop variants offer only marginal gains, our approach consistently delivers substantial improvements in every scenario. This highlights both the broad applicability of our method and its potential to enhance future VLA-style models.\n\nThank you again for the valuable suggestion; these additional results further strengthen our claims. We will include them in the final version of the paper.\n\n\n## Weakness 3 ,  Question 4.\n> SG’s extrapolation (Eq. 13) assumes local smoothness of ϵ, which may not hold for highly non-linear dynamics.\n \n> Would SG’s reactivity degrade with deformable objects or non-stationary tasks?\n\nThank you for your valuable feedback and question. Our control system typically operates at around **30Hz**, meaning the time interval, $\\Delta t$, between the current and past state is only about **0.033 seconds**. We posit that this short duration is sufficient for the **system's dynamics to be reasonably approximated as linear**, also numerically validated at answer of ***Question 1***.\n\nHowever, we acknowledge the reviewer's point that this assumption of local linearity for our Self-Guidance (SG) may not hold in environments with extremely non-linear dynamics, such as highly non-stationary dynamics or deformable objects you mentioned,  even with such a short time interval. \n\nThe primary objective of our research, though, is to enhance performance on established benchmarks that closely resemble real-world scenarios where this assumption generally holds, such as the well-established Robomimic or LIBERO benchmarks. As demonstrated in our work, our proposed SG significantly improves control performance in such benchmarks, providing strong empirical evidence that our assumption is effective and practical for its intended applications.\n\n## Question 1 \n> How was the time window  optimized? Was it task-specific or theoretically derived?\n\n* **Time Window:** In all experiments presented in the paper, we use the immediate past state, **\\$s\\_{t-1}\\$**, as input. This choice is supported both empirically, yielding the highest control performance, and theoretically, based on the intuition that the most recent state offers the most accurate and relevant information for predicting future actions. As discussed in the paper, SG can be viewed as an extrapolation of a continuous action signal using discretized differences. From this perspective, a narrow time window is essential for accurately estimating future trajectories.\n\n> Were ablation studies conducted to compare SG with explicit future-state prediction?\n\n* **Explicit Comparison:** In response to your question, we evaluate the precision of the predicted actions by computing the $L_2$ distance between the future actions generated by SG and the ground-truth actions in the Push-T environment. Specifically, we experiment with the formulation\n\n$$\n\\epsilon_{\\text{SG}} \\leftarrow (1 + w)\\epsilon_{\\theta}(x \\mid s_t) - w\\epsilon_{\\theta}(x \\mid s_{t - k})\n$$\n\nfor various values of $k$, to examine how incorporating past states influences prediction accuracy.\n\n\n  | | k=1 | k=2 | k=3 | k=4 | k=5 | baseline ($a_t - a_{t-1}$) |\n  |---|---|---|---|---|---|---|\n  | $L_2$ | **8.164** | 9.616 | 10.757 | 11.389 | 12.338 | 10.667 |\n\n  we denote baseline ||$a_t - a_{t-1}$|| to represent the natural variance between consecutive ground-truth actions.\n\n  As the results show, our SG with $k=1$ achieves the lowest $L_2$ error (8.164), predicting the future action more accurately than the baseline (10.667). This provides strong empirical evidence that our extrapolation mechanism operates effectively under the locally linear dynamics. We will add this experiment to the final version of the paper.\n\n## Question 2\n> Why not use probabilistic metrics (e.g., KL divergence) for uncertainty-aware transitions?\n\nComputing exact probabilities of generated samples for divergence-based metrics incurs significant computational overhead. For instance, Kernel Density Estimation (KDE) \\[4], a widely used technique, requires $N$ forward passes, typically 20 to 30, to approximate the underlying distribution. This leads to a computational cost roughly 30 times higher than our method, rendering it impractical for most systems.\n\nOur primary goal is to improve the control quality of diffusion policies **in real-world environments**, where maintaining a sufficient control rate, typically 30Hz as stated earlier, is critical. From this perspective, while probabilistic metrics may offer more precise evaluation, they are often infeasible in practice due to their heavy computational demands.\n\n\n\n\n## Question 3 \n> Does fixed-threshold chunking fail in edge cases (e.g., sudden disturbances)?\n\nThank you for the insightful question. As shown in Figure 6, our method with a fixed threshold actually maintains strong and consistent performance across a wide range of tasks, highlighting its practical robustness to disturbance.\n\nHowever, we agree that a fixed threshold may not be theoretically optimal for all scenarios, especially where noise levels differ significantly between phases of a single rollout. Therefore, we believe that exploring a dynamic thresholding mechanism, which could adapt based on task context or uncertainty, is an interesting and valuable direction for future work.\n\n--- references ---  \n[1] Liu, Yuejiang, et al. \"Bidirectional decoding: Improving action chunking via closed-loop resampling.\", ICLR 2025   \n[2] Black, Kevin, et al. \" $\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.\"  \n[3] Bjorck, Johan, et al. \"Gr00t n1: An open foundation model for generalist humanoid robots.\" arXiv preprint arXiv:2503.14734 (2025).  \n[4] Węglarczyk, Stanisław. \"Kernel density estimation and its application.\" ITM web of conferences. Vol. 23. EDP Sciences, 2018."}, {"review_id": "BBp20naYFN", "reviewer": "Reviewer_tuvk", "summary": "This paper presents two training-free inference modules, Self-Guidance and Adaptive Chunking, to tackle low-fidelity action sampling and balance reactivity with temporal consistency in diffusion-based behavior cloning. Self-Guidance sharpens the action distribution by using the model’s past state predictions as negative guidance during denoising, thereby biasing sampling toward higher-fidelity and forward-looking actions without extra training. Adaptive Chunking dynamically alternates between open-loop and closed-loop control based on cosine similarity of successive actions, preserving consistency in bulk motions while enabling rapid replanning during precision phases. This work yields substantial performance gains over vanilla Diffusion Policy and state-of-the-art BID method in both simulation and real-robot tests.", "questions": "1. Could you clarify and empirically demonstrate your contributions different from AutoGuidance and BID? For example, are there task settings where those baselines fail but your methods succeed?\n2. Please extend OpenVLA-OFT evaluations to include direct comparisons with diffusion-head VLA architectures such as $\\pi_0$, UniVLA, or similar methods on standard benchmarks (e.g., LIBERO). This will show whether your inference modules work for state-of-the-art policies instend of pure diffusion policies only.\n3. I would expect more real-world experiements on extension to other VLAs.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\nThe proposed methods are plug-and-play and computationally efficient, requiring no retraining or architectural changes, yet consistently improve action fidelity and responsiveness across diverse manipulation tasks.\n\n**Weakness**\n\n1. The contribution seems limited: While the methods improve existing diffusion policies, they mainly adapt known ideas from classifier-free and auto-guidance. The novelty over AutoGuidance [1] and prior chunking strategies (i.e., BID) is incremental and could be more sharply distinguished.\n\n2. The extension to VLA-based models (OpenVLA-OFT) is still at an early stage and is not benchmarked against recent diffusion-head VLA architectures such as $\\pi _0$ or other $\\pi _0$-style methods (like UniVLA), leaving open the question of these techniques' performance on state-of-the-art policies. I would expect more real-world experiements on extension to other VLAs.\n\n3. The real-world robot is a little toy, but it's understandable.\n\n**References**  \n[1] Karras, Tero, et al. \"Guiding a diffusion model with a bad version of itself.\" Advances in Neural Information Processing Systems 37 (2024): 52996-53021.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns except the contribution about introducing two plug-and-play inference techniques is a little fair. But they show superior performance and the quality of this paper is above the borderline. So, I suggest borderline accept.", "rebuttal": "Thank you for your insightful feedback and for highlighting our method's simplicity and computational efficiency, which can be directly applied to many generative model-based control systems without incurring cost. Below, we have carefully considered each of your points and present our detailed responses.\n\n\n## Weakness 1, Question 1\n> The contribution seems limited: While the methods improve existing diffusion policies, they mainly adapt known ideas from classifier-free and auto-guidance. The novelty over AutoGuidance [1] and prior chunking strategies (i.e., BID) is incremental and could be more sharply distinguished.\n\n> Could you clarify and empirically demonstrate your contributions different from AutoGuidance and BID?\n\nWe'd like to remark again on our contribution and novelty against AG and BID as follows:\n\n-   **Guidance**: As far as we know, this is the **first work that enhances the performance of diffusion-based behavior cloning by using a guidance technique**. While guidance techniques, especially CFG, are a de facto standard for improving performance in image and video generation, they have remained largely unexplored in the domain of **action generation**. A key reason for this is that CFG performs poorly for action generation (See Sec. 3.3 of [1] and the table below), or it requires training a separate goal-state classifier [4], which lacks generalizability. In contrast, we are the first to demonstrate that guidance techniques can be applied to diffusion-based behavior cloning by using extrapolation-based techniques, such as AG or our SG. Furthermore, we reveal that the mechanism for this improvement is the elimination of low-fidelity actions. **We believe this finding is a significant contribution that can be immediately applied to the rapidly advancing field of VLA community.** \n\n---\n\n-   **Superior Performance**: As shown in Fig. 3 and Fig. 5 of the main paper and Fig. 1,  Fig. 4, Fig. 5 of the Appendix, our approach (Ours/SG+AC) consistently outperforms both AG and BID across a wide range of settings, including various datasets, hyperparameters, noise levels, and both simulation and real-world environments. **These results suggest that our method is not limited to improving performance in specific scenarios but is rather a robust and statistically significant improvement that generally enhances performance**, also highlighted as a key strength by Reviewer eCfX.\n    -   Moreover, to further validate our claim, we conducted an ablation study with results for BID, CFG, AG, and each guidance method augmented with our Adaptive Chunking (AC). As the table clearly indicates, our SG+AC method achieves the highest average performance, reinforcing the robustness and superiority of our approach.\n\n        |             | Vanilla | Ours (SG+AC) | BID (ICLR'25) | CFG   | CFG+AC | AG    | AG+AC |\n        | :---------- | :------ | :---------- | :----------- | :---- | :----- | :---- | :---- |\n        | **Push-T** | 0.496   | 0.819       | 0.652        | 0.071 | 0.473  | 0.722 | 0.826 |\n        | **Square**| 0.633   | 0.753       | 0.653        | 0.000 | 0.000  | 0.497 | 0.607 |\n        | **Lift** | 0.157   | 0.650       | 0.447        | 0.020 | 0.017  | 0.340 | 0.843 |\n        | **Can** | 0.787   | 0.927       | 0.813        | 0.000 | 0.000  | 0.667 | 0.867 |\n        | **Kitchen** | 0.298   | 0.412       | 0.410        | 0.137 | 0.138  | 0.294 | 0.457 |\n        | **Transport**| 0.387   | 0.587       | 0.437        | 0.000 | 0.000  | 0.310 | 0.350 |\n        | **Average** | 0.460   | **0.691** | 0.569        | 0.038 | 0.105  | 0.471 | 0.658 |\n\n---\n\n- **Computational cost :** While Autoguidance and BID incur significant computational costs, our method effectively resolves these issues. Specifically, Autoguidance and BID have the following problems in terms of computational costs:\n\n  -   **v.s. Autoguidance (AG):**\n      -   ***Storage Overhead***: AG introduces new hyperparameters for selecting a \"weak\" model, which necessitates saving model weights at every epoch during training. This leads to substantial storage overhead.\n      -   ***Memory Cost***: AG **doubles the memory cost** (especially VRAM) by requiring weights for both strong and weak models. This increased memory overhead is particularly problematic for deployment on resource-constrained edge devices.\n      -   ***Latency***: AG doubles the computational cost for inference. Unlike our SG, which can leverage batched computation efficiently, AG's use of different weights on the same input prevents effective batching and effectively **doubles the practical latency**, which is a critical issue for real-time applications.\n      -   In contrast, our Self-Guidance (SG) resolves all these computational issues, **having 2x faster latency while achieving higher performance than AG**.\n\n  -   **v.s. BID:**   The BID \"forward contrast\" process requires generating `N` (usually 16) samples from both the strong and weak models. Hence, it suffers from all the aforementioned drawbacks of Autoguidance  and also multiplies the computational cost by a factor of `N`, which is significantly large computational overhead, especially for edge devices. Our method resolves all these issues, **having 16x smaller computational cost and 2x faster latency while simultaneously delivering superior performance to BID.**\n\nWe will add clarification of our novelty claims and comparison of AG and BID in the final version of paper.\n## Weakness 2,3, Question 2,3\n\n> The extension to VLA-based models is not benchmarked against recent diffusion-head VLA architectures such as $\\pi_0$ ... I would expect more real-world experiments on extension to other VLAs.\n\n> Please extend OpenVLA-OFT evaluations to include direct comparisons with diffusion-head VLA architectures such as $\\pi_0$ on standard benchmarks (e.g., LIBERO).\n\n> The real-world robot is a little toy, but it's understandable. I would expect more real-world experiments on extension to other VLAs.\n\nThanks for the suggestion. We conducted additional experiments on other diffusion based state-of-the-art VLAs that use open loop control, **$\\pi_0$** [2] and **GR00T-N1** [3].  Specifically, we experiment the $\\pi_0$ model in both a real-world setup and LIBERO simulation benchmark, and **GR00T-N1** in real world setup.  We employ the same setting of Sec. 4.2 (of our main paper) for real world experiment and Sec.5 (of our main paper) for LIBERO simulation evaluation. \n\n\n### $\\pi_0$\n\n| (Real) | **Ours** | $\\pi_0$ (open loop; vanilla) | $\\pi_0$ (closed loop) |\n| :--- | :---: | :---: | :---: |\n| Static | **85%** | 75% | 70% |\n| Stochastic | **65%** | 45% | 50% |\n\n| LIBERO (simul) | P=1 | P=3 | P=5 |\n| :--- | :---: | :---: | :---: |\n| $\\pi_0$ (open loop; vanilla) | 0.786 | 0.424 | 0.094 |\n| $\\pi_0$ (closed loop) | 0.786 | 0.486 | 0.208 |\n|  **Ours** | **0.828** | **0.526** | **0.222** |\n\n### GR00T-N1\n\n| (Real) | Ours | GR00T-N1 (open loop; vanilla) | GR00T-N1 (closed loop) |\n| :--- | :---: | :---: | :---: |\n| Static | **85%** | 75% | 25% |\n| Stochastic | **45%** | 20% | 20% |\n\nAs shown, when applied to $\\pi_0$ and GR00T-N1,  **our method achieves the best results in all settings**. While their vanilla (open-loop) suffers a drastic performance decrease due to stochastic noise and the closed-loop variant shows only marginal improvement, our method demonstrates a consistent and significant improvement in every setting. This underscores our method's wide applicability and its potential for enhancing future VLA-style models. We will include these additional VLA experimental results in the final version.\n\n\n--- references ---\n\n[1] Pearce, Tim, et al. \"Imitating human behaviour with diffusion models.\" , ICLR 2023.  \n[2] Black, Kevin, et al. \" $\\pi_0$ : A Vision-Language-Action Flow Model for General Robot Control.\"  \n[3] Bjorck, Johan, et al. \"Gr00t n1: An open foundation model for generalist humanoid robots.\" arXiv preprint arXiv:2503.14734 (2025).  \n[4] Janner, Michael, et al. \"Planning with diffusion for flexible behavior synthesis.\" arXiv preprint arXiv:2205.09991 (2022)."}, {"review_id": "afkZFrxD80", "reviewer": "Reviewer_eCfX", "summary": "The paper presents two novel methods for improving diffusion policies: Self-Guidance (SG), which guides the diffusion process by negating the action probabilities of previous timesteps, and Adaptive Chunking (AC), which dynamically selects the length of the action chunk to balance reactivity and consistency. The experiments conducted demonstrate significant performance improvements across a variety of simulation benchmarks and real-world robotic manipulation tasks.", "questions": "-  In Figure 3, it is unclear from the main text what exactly \"different levels of noise\" refer to (diffusion policy sampling process, the environment observations, or dynamics?). Also, including a baseline result for SG at w=0 (vanilla) would significantly improve interpretability, clearly illustrating the gains specifically attributable to SG.\n\n- The authors use cosine similarity to measure action similarity. However, this metric is primarily suitable for position control tasks. It should be clarified that for tasks involving joint angles or velocity control, metrics like L1 or L2 norms might be more appropriate. \n\n- In the experimental section, it is stated that closed-loop control was used for stochastic environments. This choice seems contradictory since the key advantage of adaptive chunking is precisely to automatically decide on the execution length. Either this choice needs clarification, or the experiment should be restructured to genuinely test AC.\n\n- Regarding the VLA experiment in the discussion, can something like that be done for ACT? I'm aware that such an experiment is out of the scope of a rebuttal, just interested in your thoughts on the matter.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n- The proposed methods (SG and AC) are innovative, simple, and practically appealing, requiring minimal changes to existing diffusion policy frameworks.\n\n- The experiments are comprehensive, covering both simulation benchmarks and real-world scenarios, with clear and statistically significant performance improvements.\n\n- The paper is written in a clear and easy-to-follow way.\n\n**Weaknesses:**\n\n- The paper could provide a more thorough intuitive discussion about cases where SG might not be beneficial. For example, in slow-changing scenarios (e.g., a robot continuing to move toward the same target for some time), the SG method seems likely to reduce the probability of repeatedly sampling the optimal action. \n\n- Equations 12 and 13 contain an error. The approximation for first-order differences is not as mentioned, but $f(x+\\delta x) = 2f(x)-f(x-\\delta x)$. This also invalidates the discussion following the equation.\n\n- While the authors argue that adaptive chunking can mitigate issues due to unexpected changes in the environment, it is less clear how it prevents jittering arising from sampling multimodal trajectories. For instance, consider a scenario where a robot must pass an obstacle, which can be approached either from the left or from the right. If the current action chunk in the queue directs the robot to move left, but the next sampled action chunk directs it to move right, adaptive chunking would replace the current action. In subsequent steps, the sampled action could revert again to the left, causing continuous toggling between these trajectories rather than smooth behavior. Clarifying or experimentally addressing this concern would strengthen the paper. Clarifying or experimentally addressing this concern would strengthen the paper.", "was_revised": true, "final_justification": "The authors addressed all the points I raised in my rebuttal. One of the weaknesses was an error in their derivation, which they fixed, and for the other two, they provided sufficient explanation. I don't see any clear weakness in their results and think that this paper should be accepted.", "rebuttal": "We are grateful for your insightful feedback and for acknowledging the contributions of our work, especially highlighting our method's innovation, simplicity, and statistically significant performance improvements. We have carefully considered each of your points and present our detailed responses below.\n\n## Weakness 1\nThank you for the insightful suggestion. However, **our SG is not negatively impacted by slowly changing scenarios**. In these situations, where the change in state $|s_t - s_{t-\\delta t}|$ is small, the ratio $\\frac{p_\\theta(a_t|s_t)}{p_\\theta(a_t|s_{t-\\delta t})}$ approaches 1. Consequently, Eq. (11) simplifies to $p(a_t|s_t)$. Thus, our SG is not adversely affected by slowly changing scenarios unless an extremely large guidance weight, $w$, is used.\n\nConversely, the scenario where SG is less beneficial is when the robot move very quickly. This rapid movement creates a large observation gap, $|s_t - s_{t-1}|$, which in turn causes SG to produce a faster (larger) action in the subsequent step due to large guidance signal. This can create a positive feedback loop, leading to progressively larger observation gaps. However, this issue can be mitigated in practice by filtering out excessively fast actions using a thresholding technique, mechanisms that are commonly built into robotic systems due to inherent motor speed limitations. We will add this discussion in final version.\n\n\n## Weakness 2\nThank you for pointing this out. This was a **typographical error**, we mistakenly used ‘+’ instead of ‘–’. However, the subsequent discussion remains valid despite this mistake. The correct version of Eq. (12) is:\n\n$$\n(1 - w)\\cdot \\epsilon(x, s_t) + w\\cdot (2\\cdot \\epsilon(x, s_t) - \\epsilon(x, s_{t - \\Delta t}))\n$$\n\nWe will correct this in the final version of the paper.\n\n## Weakness 3\nThank you for your valuable feedback. The toggling scenario you described rarely occurs in practice and is, in fact, **prevented by our proposed methodology** for the following reasons:\n\n1.  At a specific point during the rollout, the policy model $\\pi(a|s_t)$ outputs a multi-modal distribution over actions. The robot samples an action from one mode according to its probability and executes the corresponding control.\n\n2.  Once the robot has committed to one mode, the model predicts the subsequent action, $\\pi(a|s_{t+1})$. However, because the robot has already entered a specific mode, the probability of the model sampling an action corresponding to the opposite mode is significantly lower than it was at the previous timestep. Consequently, toggling between modes is unlikely, even with Adaptive Chunking alone.\n\n3.  Furthermore, applying our **Self-Guidance** (as shown in Eq. (11)) resolves this problem as well. Since the probability of the alternative mode has already decreased, Self-Guidance further **attenuates the probability of the opposite mode** while reinforcing the probability of the currently selected one.\n\n4.  For instance, consider a simplified two-mode scenario (e.g., left/right). Assume the robot has just enter one mode, and the probabilities evolve as follows: the previous probability was $p(a|s_{t-1}) = [0.5, 0.5]$, and the (unguided) current probability is $p(a|s_{t}) = [0.7, 0.3]$. With a guidance weight of $w=2$, our method updates the probability based on Eq. (11). The new guided probability, $p_{SG}(a|s_{t})$, is proportional to:\n    $$p_{SG}(a|s_{t}) \\propto p(a|s_{t}) \\left( \\frac{p(a|s_{t})}{p(a|s_{t-1})} \\right)^w$$\n    Calculating this element-wise:\n    $$p_{SG} \\propto [0.7, 0.3] \\odot \\left( \\frac{[0.7, 0.3]}{[0.5, 0.5]} \\right)^2$$\n    $$p_{SG} \\propto [0.7, 0.3] \\odot [1.96, 0.36] = [1.372, 0.108]$$\n    After normalization, the final probability becomes $[0.927, 0.073]$. As shown, the probability of selecting the other mode is significantly attenuated ($0.3 \\rightarrow 0.073 $ ).\n\nWhile this is a simplified example, we observe in our experiments that the model prevents toggling for the reasons described above. This analysis underscore the importance of using both of our methods, SG and AC. We will add a more detailed explanation and relevant visualizations of this situation to the final version of the paper.\n\nAdditionally, the \"jittering problem,\" as we define it in our paper, refers to the 'jerky' or 'trembling' movements that occur while the robot follows a trajectory from a specific mode, due to stochastic nature of the generative model. This problem is well discussed in recent work [1,2] and is known to negatively impact overall rollout performance, particularly in real-world scenarios. The primary objective of our Adaptive Chunking is to mitigate this issue.\n\n## Question 1\n- **Figure 3 P :** Please see Appendix A. **we denotes $P$ as an environmental noise**, which is the speed of movement of T block and is the same noise setting in previous works, BID \\[1].  We will clarify in final version.\n- **SG at w=0 :** **Please see Figure 5 in Appendix**. As shown in figure, our SG largely surpass the $w=0$ (vanilla) with large margin and even surpass the more computationally heavy technique, AG (AutoGuidance) \\[6] in all settings. We will append this result in our Figure 3 (main). Thanks for your constructive feedback.\n- **Remark** : Moreover, we'd like to remark again that this is the **first work that enhances the performance of diffusion-based behavior cloning by using a guidance technique**, as far as we know. While guidance techniques, especially CFG, are a de facto standard for improving performance in image and video generation, they have remained largely unexplored in the domain of **action generation** because of their poor performance in BC (See Sec. 3.3 of [5]). In contrast, we are the first to demonstrate that guidance techniques can be applied to diffusion-based behavior cloning by using extrapolation-based techniques, such as AG or our SG. Furthermore, we reveal that the mechanism for this improvement is the elimination of low-fidelity actions. **We believe this finding is a significant contribution that can be immediately applied to the rapidly advancing field of VLA community.** \n\n## Question 2\nThank you for your insightful feedback. As shown in Table 2 in the Appendix, we found that the cosine-similarity-based method outperforms other metrics in all benchmarks we tested, which represent a wide range of standard manipulation tasks. However, we also agree that for some action types (e.g., velocity-based), other metrics can be more useful. We will clarify this point in the final version.\n\n## Question 3\nThanks for the feedback. The term 'closed-loop' for Adaptive Chunking means that its update rule (Eq. (14)) is applied at every timestep. Conversely, the 'open-loop' version, which we used in the static environment for a fair comparison with the baselines, applies the update rule only every 8 timesteps. You can also check the `codes/improvingGBC/diffusion_policy/sampler/single.py:25` for more specific implementation. The current Eq. (14) denotes this closed-loop implementation. We agree that this can be misleading and will clarify it in the final version. \n\n## Question 4\n**Yes**. While our paper mainly focused on Diffusion Policy , we believe our method can be applied to any behavioral cloning framework as long as it use  action chunking and use probabilistic framework to model action space. \n\nFor instance, in the case of ACT [2], which uses a VAE decoder to model action probability, we can simply apply Self-Guidance by extrapolating the input to the VAE decoder (or the output of the transformer decoder) using the value from the previous timestep. Adaptive Chunking could also be seamlessly integrated into the ACT framework, similar to their temporal ensemble technique.\n\nMoreover, to validate our method's wide applicability claim, we present results from applying our method to other state-of-the-art diffusion-based VLAs that use open-loop control: $\\pi_0$ [3] and GR00T-N1 [4].   Specifically, we experiment the $\\pi_0$ model in both a real-world setup and LIBERO simulation benchmark, and GR00T-N1 in real world setup.  We employ same setting of Sec. 4.2 (of our main paper) for real world experiment and Sec.5 (of our main paper) for LIBERO simulation evaluation. \n\n\n### $\\pi_0$\n\n| (Real) | **Ours** | $\\pi_0$ (open loop; vanilla) | $\\pi_0$ (closed loop) |\n| :--- | :---: | :---: | :---: |\n| Static | **85%** | 75% | 70% |\n| Stochastic | **65%** | 45% | 50% |\n\n| LIBERO (simul) | P=1 | P=3 | P=5 |\n| :--- | :---: | :---: | :---: |\n| $\\pi_0$ (open loop; vanilla) | 0.786 | 0.424 | 0.094 |\n| $\\pi_0$ (closed loop) | 0.786 | 0.486 | 0.208 |\n|  **Ours** | **0.828** | **0.526** | **0.222** |\n\n### GR00T-N1\n\n| (Real) | Ours | GR00T-N1 (open loop; vanilla) | GR00T-N1 (closed loop) |\n| :--- | :---: | :---: | :---: |\n| Static | **85%** | 75% | 25% |\n| Stochastic | **45%** | 20% | 20% |\n\n\nAs shown, when applied to $\\pi_0$ and GR00T-N1,  **our method achieves the best results in all settings**. While their vanilla (open-loop) suffers a drastic performance decrease due to stochastic noise and the closed-loop variant shows only marginal improvement, our method demonstrates a consistent and significant improvement in every setting. This underscores our method's wide applicability and its potential to other VLAs, such as ACT.\n \n\n-- references --  \n[1] Liu, Yuejiang, et al. \"Bidirectional decoding: Improving action chunking via closed-loop resampling.\", ICLR 2025  \n[2] Zhao, Tony Z., et al. \"Learning fine-grained bimanual manipulation with low-cost hardware.\"  \n[3] Black, Kevin, et al. \"$\\pi_0 $: A Vision-Language-Action Flow Model for General Robot Control.\"  \n[4] Bjorck, Johan, et al. \"Gr00t n1: An open foundation model for generalist humanoid robots.\" arXiv preprint arXiv:2503.14734 (2025).  \n[5] Pearce, Tim, et al. \"Imitating human behaviour with diffusion models.\" , ICLR 2023.  \n[6] Karras, Tero, et al. \"Guiding a diffusion model with a bad version of itself.\" NeurIPS 2024"}]

Author rebuttal:

Meta-review: The paper received ratings of 4, 5, 5, 5. After rebuttal, reviewers acknowledged that the authors had provided extensive new experiments—covering both simulation (LIBERO) and real-world robotics, as well as additional VLA models (e.g., GR00T-N1)—and clarified theoretical and computational tradeoffs. Reviewer eCfX raised the rating to Accept (5). Reviewer tuvk raised the rating to Borderline Accept (4) after recognizing the broad applicability and robust empirical validation of the proposed methods. Reviewer qBvg maintained an Accept (5), noting the paper’s contributions to diffusion policy inference.

Overall, the proposed Self-Guidance and Adaptive Chunking modules are simple, plug-and-play, and training-free techniques that yield consistent improvements across a wide range of robotic manipulation tasks. While some questions about conceptual novelty remain, the rebuttal and follow-up results sufficiently addressed the major technical concerns. I recommend Accept.
Decision: Accept (poster)