Title: FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
Venue: NeurIPS 2025
Accepted: yes
Keywords: Visuomotor policy; Robotic manipulation; Efficient AI; Deep learning acceleration;
Abstract: Generative modeling-based visuomotor policies have been widely adopted in robotic manipulation, attributed to their ability to model multimodal action distributions. However, the high inference cost of multi-step sampling limits its applicability in real-time robotic systems.
Existing approaches accelerate sampling in generative modeling-based visuomotor policies by adapting techniques originally developed to speed up image generation. However, a major distinction exists: image generation typically produces independent samples without temporal dependencies, while robotic manipulation requires generating action trajectories with continuity and temporal coherence.
To this end, we propose FreqPolicy, a novel approach that first imposes frequency consistency constraints on flow-based visuomotor policies.
Our work enables the action model to capture temporal structure effectively while supporting efficient, high-quality one-step action generation.
Concretely, we introduce a frequency consistency constraint objective that enforces alignment of frequency-domain action features across different timesteps along the flow, thereby promoting convergence of one-step action generation toward the target distribution.
In addition, we design an adaptive consistency loss to capture structural temporal variations inherent in robotic manipulation tasks.
We assess FreqPolicy on $53$ tasks across $3$ simulation benchmarks, proving its superiority over existing one-step action generators.
We further integrate FreqPolicy into the vision-language-action (VLA) model and achieve acceleration without performance degradation on $40$ tasks of Libero.
Besides, we show efficiency and effectiveness in real-world robotic scenarios with an inference frequency of $93.5$ Hz.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a one-step visuomotor policy that incorporates temporal knowledge for robotic manipulation. The authors introduce two key innovations: (1) a Frequency Consistency Objective, which enforces consistency between the velocities of action segments across different timesteps in the frequency domain; and (2) an Adaptive Frequency Component Loss, which dynamically emphasizes frequency components with greater discrepancies through adaptive weighting. The writing is clear, and the effectiveness of the approach is validated on MetaWorld, demonstrating superiority over existing one-step visuomotor policies and achieving significant improvements in inference speed for VLA models.\n\nSTRENGTHS AND WEAKNESSES:\nQuality\n\nThe paper presents a well-motivated idea, supported by comprehensive experimental results. The writing is detailed, and the visualizations are clear. The experiments are thorough and effectively demonstrate the validity of the proposed innovations.\n\nClarity\n\nThe paper is clearly written and easy to follow. The figures are well-presented, and the experimental results and ablation studies are clearly designed to validate the proposed innovations.\n\n\nSignificance\n\nThe experimental results demonstrate the effectiveness of the proposed approach. The flow-matching-based policy model is of interest to both academia and industry, and the proposed objective function is broadly applicable to sensorimotor policy networks and VLA models.\n\nIt is strong due to its comprehensive experiments including real-world experiments and reasonable innovation design.\n\nOriginality\n\nThe paper demonstrates strong originality, and the core idea is precise and impactful.\n\nQUESTIONS:\nMethodology\n\n- Equation 11 employs the type-II DCT. Have the authors conducted ablation studies using alternative similarity functions?\n- Equation 1 enforces the velocity field as a straight trajectory. How sensitive is the proposed objective function to the hyperparameters, like the ratio with respect to L_fm ?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents FreqPolicy, a consistency flow matching method for efficient generation of visuomotor control actions. FreqPolicy considers the setup where we train a rectified flow matching policy on some demonstration data, and would like to run efficient one-step inference with good task performance. Some background works such as Consistency Flow Matching considers velocity consistency loss given the same endpoint, and FAST considers action representation in the frequency domain with DCT. For efficient inference, FreqPolicy proposes training a flow matching policy, with consistency loss in the frequency domain with focal loss-like adaptive weighting. Experiments in various sim and real environments show that this approach gives us efficient one-step flow matching policy inference with good performance. Ablation studies validate the necessity of each design component in FreqPolicy.\n\nSTRENGTHS AND WEAKNESSES:\n- The idea of frequency domain consistency matching for efficient flow matching inference, as well as adaptive weighting, is well-motivated.\n- Various experiments in real and simulated environments show that FreqPolicy achieves good results with efficient one-step inference.\n- The paper compares with a comprehensive set of baselines such as iterative generation with DDPM, DDIM, flow matching policies, as well as consistency models and IMLE.\n- Ablation studies show that the core ideas of frequency domain consistency loss, as well as adaptive weighting, improves performance over vanilla velocity consistency or no weighting.\n- The paper combines two existing ideas (DCT for action representation, and consistency flow matching).\n\nQUESTIONS:\n- Would this be equivalent to taking a DCT on the action chunks, then taking a regular consistency loss during training?\n\nLIMITATIONS:\n- Yes, the paper has adequately addressed its limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFreqPolicy accelerates flow-based visuomotor policies by adding a frequency-domain consistency loss. Velocities at different flow timesteps are projected with a DCT and forced to share similar spectra, while an adaptive weighting scheme emphasizes the most mismatched bands. Combined with the standard flow-matching loss, this yields a one-step policy that matches or exceeds multi-step diffusion/flow baselines on various simulated and real-robot benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths. The work is the first to exploit frequency consistency for robot policies, offering a fresh angle on the speed-versus-quality trade-off. Mathematics and implementation details are clear, and experiments are thorough. Simulation tasks plus real-world trials show the one-step model outperforming Consistency Policy, SDM, OneDP, and FlowPolicy while being 5–20× faster than diffusion. The method removes the need for a pretrained teacher and integrates cleanly into a large vision-language-action model, demonstrating practical relevance.\n\nWeaknesses. The contribution, though clever, is incremental on top of existing consistency/flow frameworks; its novelty lies mainly in switching to the frequency domain. Applicability is demonstrated only for flow-matching; it is unclear whether diffusion or discrete-action policies would benefit. Ablations do not fully isolate how much improvement comes from the spectral projection itself, and hyper-parameter sensitivity (e.g., DCT length, loss weighting) is not discussed.\n\nQUESTIONS:\n1. How much gain comes specifically from frequency projection versus plain time-domain consistency?\n2. Can the loss be applied to diffusion policies in general?\n3. Does the adaptive weighting ever over-focus noisy high-frequency band? how is stability ensured?\n4. In the OpenVLA experiment, did you fine-tune or retrain the policy head from scratch?\n\nLIMITATIONS:\nThe focus is on flow models; generality to other generators and combination with compression techniques are future work. Societal impact is minimal. Limitations are adequately acknowledged.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors investigate methods for improving generative policies in robotic control. The problem they identify is the use of iterative generative models when generating actions, which reduce action sampling rates. They suggest that a solution is to introduce temporal consistency loss functions as a means of enhancing single-step prediction policies. The authors apply this loss function to a flow-matching policy and demonstrate the efficacy of this approach through extensive simulation and real-world experiments, showing improvements in robot control while increasing the action frequency rate.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The authors' proposed method seems to address issues of iterative sampling from prior generative policies, showing similar or better success rates while requiring fewer model evaluations to generate action sequences\n- The evaluation is extensive, utilizing 53 tasks across three simulation benchmarks to assess the efficacy of this platform.\n- Real robot experiments are performed, which is always an important demonstration for robotic-focused applications.\n\nWeaknesses\n\nOur biggest concern is distinguishing the author’s contribution from prior works. The authors can address this issue by refining their writing, which we found to be verbose in certain sections. We discuss suggestions on this in the “Paper Formatting” section.\n\n Some of the reported results could benefit from additional analysis, such as performing appropriate hypothesis testing to verify that any improvements shown on average are also statistically significant. It is surprising that standard deviations are excluded in some sections for evaluation, suggesting that the authors did not perform multiple runs of the experiments during model training. If the author’s experiments report results from a single trained model for each testing scenario rather than several models trained with different seeds, this is a weakness of the paper. In this case, the authors should clearly state this as a limitation of their results. We suspect it’s unlikely, as this is behavior cloning, but perhaps different policies would lead to measurably different performance results. Additionally, as we stated, this makes it hard to perform a statistical assessment of their model.\n\nQUESTIONS:\n- Related Work line 95: How does the author’s work differ from the various previous works on generative models in visuomotor policies? \n- Line 176 - Isn’t this technically preliminary work if you’re using the same loss as other papers?\n- Equation 8 - Should the target be a_1 - a_t? As written, my understanding is that v_\\theta should always predict the whole duration from a_0 to a_1 regardless of the time or a_t value given. \n- Equation 10 line 204:  If r <s < u, wouldn’t it be better to explicitly show this in the notation when sampling? I.e. r \\sim U(0, 1), s \\sim U(r, 1), u \\sim U(s, 1)? Or something similar? \n- Equation 11, 13, what is “H” here?\n- What research questions are the experiments addressing? \n- Table 1: are the reported improvements statistically significant by an appropriate hypothesis test?\n - How many runs were used in the ablation experiments shown in Table 4? It seems strange there aren’t standard deviations reported.\n\nLIMITATIONS:\nNo\nThe limitations section (lines 335 - 340) doesn’t list any real limitations: “Our work focuses on accelerating flow matching through frequency domain modelling.” which is followed by a vague comment that the authors might or might not try to enforce other things on temporal motions. The authors should consider the potential problems or pitfalls of using their proposed loss function more deeply. An obvious limitation in our assessment is that the performance gains of the full loss function may depend on task complexity (e.g., Table 4 - Can or square results show minimal gains, whereas Transport and Toolhang show an 8% and 12% improvement in success rate, respectively). As we mention above, if only a single model is trained and evaluated on, this is definitely an important limitation.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}