VITA: Vision-to-Action Flow Matching Policy
Authors: Dechen Gao
BOQI ZHAO
Andrew Lee
Ian Chuang
Hanchu Zhou
Hang Wang
Zhe Zhao
Junshan Zhang
Iman Soltani
Venue: ICLR 2026 Poster (2026)
Keywords: Flow Matching
Robot Learning
Imitation Learning
Robotics
Robotics Policy
Manipulation
Primary area: applications to robotics, autonomy, planning
TL;DR: We present Vision-To-Action flow matching policy, a noise-free, conditioning-free framework, that evolves latent visual representations into latent actions via flow matching for efficient visuomotor control.

Abstract: Conventional flow matching and diffusion-based policies sample through iterative denoising from standard noise distributions (e.g., Gaussian), and require conditioning modules to repeatedly incorporate visual information during the generative process,  incurring substantial time and memory overhead. To reduce the complexity, we develop VITA, VIsion-To-Action policy, a noise-free and conditioning-free flow matching policy learning framework that directly flows from visual representations to latent actions. Since the source of the flow is visually grounded, VITA eliminates the need of visual conditioning during generation. As expected, bridging vision and action is challenging, because actions are lower-dimensional, less structured, and sparser than visual representations; moreover, flow matching requires the source and target to have the same dimensionality. To overcome this, we introduce an action autoencoder that maps raw actions into a structured latent space aligned with visual latents, trained jointly with flow matching. To further prevent latent action space collapse during end-to-end training, we propose flow latent decoding, which anchors the latent generation process by backpropagating the action reconstruction loss through the flow matching ODE (ordinary differential equation) solving steps.  We evaluate VITA on 9 simulation and 5 real-world tasks from ALOHA and Robomimic. VITA achieves 1.5x-2.0x faster inference compared to conventional methods with conditioning modules, while outperforming or matching state-of-the-art policies.

Published as a conference paper at ICLR 2026
VITA: VISION-TO-ACTION FLOW MATCHING POLICY
Dechen Gao', Boqi Zhao', Andrew Lee', Ian Chuang", Hanchu Zhou', Hang Wang', Zhe Zhao', Junshan Zhang', Iman Soltani'
University of California, Davis, UUniversity of California, Berkeley (dogao, boqzhao, awclee, hazhou, whang @ucdavis.ede (zao, jazh, isoltani @ucdavis. Janc@berkeley.co
ABSTRACT
Conventional flow matching and diffusion- basssd policies sample via iterative de- noising from standard noise distributions (e.g., Gaussian), and require conditioning modules to repeatedly incorporate visual information during the generative process, incurring substantial time and memory overhead. To reduce the complexity, we develop VITA Vision-To-Accionn policy). a noise-free and conditioning free flow matching policy learning framework that directly flows from visual representations to latent actions. Since the source of the flow is visually grounded, VITA eliminates the need for visual conditioning during generation. As expected, bridging vision and action is challenging, because actions are lower-dimensional, less structured, and sparser than visual representations; moreover, flow matching requires the source and target to have the same dimensionality. To overcome this, we introduce action autoencoder that maps raw actions into structured latent space aligned with visual latents, trained jointly with flow matching. To further prevent latent action space collapse during end-to-end training, we propose flow latent decod- ing, which anchors the latent generation process by backpropagating the action reconstruction loss through the flow matching ODE (ordinary differential equation) solving steps. We evaluate VITA 9 simulation and 5 real-world tasks from ALOHA and Robomimic. VITA achieves 1.5x-2x faster inference compared to conventional methods with conditioning modules, while outperforming matching state-of-the-ar art policies. Project page: VITA
INTRODUCTION
Conventional Flow Matching Policy
VITA Policy: Noise-Free Flow Matching
Latent
Latent Action
Latent Image
Inject Conditions
Image
Distribution
Distribution
Latent
Latent
Noise
Image
Actions
Gaussian
Flow Matching
Actions
Camera
Flow Matching
Actions
Distribution
Image
Figure 1: A comparison between VITA and conventional flow matching and diffusion policies. Unlike conventional methods that sample noise from standard distributions and inject input modalities conditioning, VITA poses constraints the source distribution, and flows directly from latent visual representations to latent actions, eliminating the need for conditioning modules.
Flow matching and diffusion models have demonstrated remarkable success across a wide range of cross-modal generation tasks, from text-to-image generation (Rombach et al. 2022 Peebles &
Xie 2023 Ma et al., 2024 Liu et al.
2024a
He et al. 2025 Zhang et al.) (023), text-to-
generation (Ho et al.) 2022 Li et al.
2023 Jim et al. 2024 to visuomotor vision-to-action
policies (Chi et al. 2023 Ren et al. 2024at
Gao et al.] 2025 Su et al. 2025| Zhang et al. 2025
RRoxel et al. 2024 Braun et al. 2024 Black al.). Conventional flow matching and diftusion

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methods (Lipman al. 2023 Dickstin et al. 2015) generate samples by starting with noise sampled from a basic source distribution (often Gaussian) and progressively "denoising" them into the target modality. This process requires repeatedly injecting visual information at each denoising step
through additional conditioning modules (Rombach et al. 2022 Zhang et al. 2023 Chi et al. 2023 Dasari et al., 2024), resulting in substantial time and space overheads (Liu et al.
2024a
He et al.
2025). In particular, cross-attention, AdaLN (Peebles & Xie, (223). or FiLM (Perez et al. 2018) are often used for visual conditioning. Cross-attention incurs quadratic time and space complexity, while
AdaLN and FiLM avoid quadratic complexity but require extra modulation networks to generate feature-wise parameters at every denoising step.
Minimizing complexity is essential for real-time robot control, e.g., Pi-0.5 (Intelligence et al.) operates at 50 Hz and Helix (Figure Al 2025) at up to 200 Hz, imposing stringent requirements on inference latency. The primary objective of this paper is to overcome the inefficiencies inherent to conditioning mechanisms in conventional flow matching methods. To this end, we develop VITA (VIsion-To. Action policy), noise-free flow matching policy learning framework that directly maps visual representations to latent actions. As depicted in Figure I unlike conventional methods that flow from Gaussian prior, VITA imposes visual latents, obviating the need for repeated visual conditioning during the flow. Consequently, VITA significantly reduces time and space overheads, and simplifies network architectures.
constraints the source distribution and flows directly from
Learning vision-to-action flow matching, however, presents several new challenges. Bridging two distinct modalities is inherently difficult (Liu et al.) 2024a), particularly in robotics where action data is limited, unstructured, and sparse, whereas visual representations exhibit rich structures and semanties, and far higher dimensions. Additionally, flow matching requires that the source and target have equal dimensionalities, which prevents directly aligning raw actions with visual representations.
To address these challenges. we propose two key designs for VITA. 1) Learning Latent Actions as Flow Matching Targets. We introduce a latent action space, learned via an action autoencoder, that "lifts" action representations to match the higher dimensionality of visual representations and serves as a structured target distribution for flow matching. The action encoder up-samples raw actions into target latent actions, and decoder reconstructs raw actions from these latents. 2) Enabling End-to-End Latent Action Policy Learning via Flow Latent Decoding. In conventional flow matching methods such as latent diffusion for image generation (Rombach et al. 2022), the target latent space can be pre-trained with abundant images and then frozen as reliable flow matching targets; in contrast, we show that a pre- pre-trained and frozen latent action space for flow matching yields poor performance (discussed in Appendix B.2 + since action data is too sparse and limited to learn reliable targets and frozen targets cannot be corrected. It is therefore necessary to jointly train the flow model with the action autoencoder. To prevent collapse of the latent action space (which happens by naively reducing flow matching and autoencoder losses), we introduce flow latent decoding which backpropagates the reconstruction loss of latent actions generated by solving flow matching ordinary differential equations (ODEs), anchoring latent generation using ground-truth actions. This approach bridges the vraining-inferent to match the targets given by the action encoder, and the decoder is trained to reconstruct these targets, whereas at test time, the decoder must reconstruct Oen-grreated latent actions.
gap of latent actions: during training, the flow matching model learns
We evaluate VITA on both real-world and simulated tasks using ALOHA (Chuang et al. 2024 Fu (et al. 2024b Zhao et al., 2024 and Robomimic (Mandlekar et al. 2021). VITA achieves 1.5x-2x faster inference and 18.6% 6..6%%.%% lower memory usage compared to conventional flow match- ing with similar model sizes, while outperforming or matching state-of-the-art policies in success rates. Additionally, compared to state-of-the-ans art methods that necessitate complex architectures like transformers (Ma et al. 2024), VITA naturally simplifies architecture designs. For instance, with vector-based visual representations, VITA reduces the flow-matching network to a conditioning-free vector-to-vector mapping, allowing for the use of simple MIPs; with higher-dimensional grid-based visual representations, VITA scales to more complex architectures such as transformers while elimi- nating costly conditioning modules like cross-attention. We further demonstrate that VITA achieves stable learning with fast convergence while maintaining high precision (discussed in Appendix B.8)
Our main contributions are summarized as follows:
Noise-Free Flow Matching for Visuomotor Learning. We propose VITA. a noise-free policy that directly evolves latent visual representations into latent actions via flow matching. VITA learns a
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structured latent action space aligned with visual representations bridge the modality gap. To prevent latent action collapse during end-to-end latent action policy training, we propose flow latent decoding, which refines latent actions by backpropagating through the flow matching ODE steps.
Efficient Policy Architectures. By visually grounding the source of the flow, VITA obviates costly conditioning required by flow matching policies to repeatedly inject visual inputs. VITA enables lightweight implementations. To our knowledge, VITA is the first MLP-only flow matching policy to succeed on tasks as challenging as ALOHA bimanual manipulation.
State-of-the-Art Efficiency and Performance. We validate VITA's efficiency and performance on 9 simulated and 5 real-world tasks spanning both bimanual and single-arm manipulation. VITA delivers 1.5x x-x x faster inference and 18.6%-28.7%% lower memory usage compared to conventional flow matching, while surpassing or matching state-of-the-art policies in success rates.
RELATED WORK
Imitation Learning for Visuomotor Policy. Imitation learning enables robots to learn complex behaviors by mimicking expert demonstrations. Behavior cloning is a prominent imitation learning paradigm that learns policy mapping observations to actions via supervised learning (Zhao et al.
2023 Fu et al., 2024a Gong et al. 2024 Su et al. 2025). Recent advancements in behavioral cloning increasingly leverage generative modeling (Chi al. 2023), , which models the conditional action
distribution given observations using conditional variational autoencoders (CVAEs) (Zhao et al. 2023
Lee et al. 2024), diffusion models (Dasari al., 2024 Chi et al.. (023), or flow matching (Zhang &
Gienger 2024[ZZaan et al.] 2025). These approaches commonly rely on conditioning mechanisms
(e.g.. croos-attentionn (Dasari al. 2024), AdaLN (Dasari et al.) 2024). FiLM (Perez et al. (018 to
incorporate visual observations during the generative process. In contrast, VITA removes the need for visual conditioning modules by developing a noise-free vision-to- flow.
Diffusion and Flow Matching for Generative Modeling, Unlike diffusion, which samples from Gaussian distributions, flow matching theoretically places no constraints on the choice of source distribution (Tong et al., 2024). A few works have explored this property to learn the direct transport within the same modality (Albergo Vanden-Fijnden 2022] Tong et al., (0236). Recently, [Luu et al.. (2024a) and He et al. (025) extended this to more challenging cross-modal generation between text and image. VITA learns to bridge vision and action for visuomotor control, where the action modality presents unique challenges because of limited data and its unstructured nature. Different from flow matching for image generation, which typically pre-trains and freezes the latent image space when learning the flow (Rombach et al.) 2022 Liu et al. (0244)), VITA resorts to fully end-to-end pipeline training to effectively learn the latent action space from limited and sparse action data along with flow matching. We propose flow latent decoding backpropagate action reconstruction losses through the latent action generation process (ODE solving steps) during training.
3 PRELIMINARIES
Flow matching models learn to transport samples from a source distribution Po to target distribution p1 by learning a velocity vector field vg. The generative process defined by an ODE da, vy (Z
dt (Liu et al. 2022c Wang & Qureshi 2023), where t E [0. 1] is continuous time, and denotes sample time t. The goal is for the learned flow to transport Zu ~ Po to Z1 P1.
Training, For linear interpolation between two samples, the interpolation path is z; (l-
The ground-truth velocity along this path is dz: this supervised vector field:
Z1 Zg. The flow matching loss trains Uy to match
dt
CFM Ee, zo. [100 [le: (ze: t) -Zz zo)]l3]
(I)
Inference. Given source sample Zo, target sample is obtained by solving the ODE from to t 1: 2 = Zo + So oo t) dt. In practice, we apply an Euler solver with K discretization steps. yielding updates of the form Z4.t1 Zz, + At vo (Ze,, 1e), where At 1/K.
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4 VITA: VISION-TO-ACTION FLOW MATCHING
The key challenge in VITA is the large dimensionality gap between vision and action, compounded by the sparsity and unstructured nature of action data. In this section, present the core designs of VITA developed to address these issues. We first introduce the mathematical formulation of VITA (Section 4.1) and its overall architecture (Section 4.2).. We then show why constructing latent action space is essential for resolving dimensionality mismatch (Section 4.3), and propose flow latent decoding address latent action collapse. Finally, we describe the objectives that enable effective end-to-end VITA learning from scratch (Section 4.4
4.1 FLOWING FROM VISION TO ACTION
VITA learns policy (AAO) that directly maps observations to a corresponding sequence of future actions A. The observations O encompass raw visual inputs E R# and, optionally, robot's proprioceptive states Actions are represented as temporal sequences over a prediction horizon, formally defined as A E mm Danie, where pred is the prediction horizon and Daction is the dimensionality of the action space. We employ action chunking with Tpred > 1 to enhance temporal consistency (Zhao et al. 2023).
Conventional vs. VITA Flow Matching. Conventional flow matching policies generate actions by evolving samples from noise prior, typically Zu ~ I). To incorporate visual information, these models learn a conditional velocity field vo (z O), requiring conditioning modules (e.g.. cross-attention) to inject observations O every denoising step. In contrast, VITA directly treats the visual latent as the source the flow Zn- Because the flow is visually grounded at the source, VITA Iearns a conditioning- free velocity field for flow matching, 0y (Ze t). climinating the need for repetitive conditioning and yielding a noise-free freee-free framework with enhanced efficiency.
Critically, flow matching requires 2o and 2 share identical dimensionality, necessitating the construction of latent action space that matches the dimensionality of visual representations (Sec- tion 4.3) During inference, the current observation Ocurr first encoded into its latent visual representation 2o (OCurr) which is subsequently evolved into predicted latent action represen- tation, 21, by numerically solving the ODE from 0 to using the learned velocity field eg- In other words, 21 is approximation to the target latent 21. The resulting latent action 2 is then decoded through the action decoder to yield the final action sequence = Da
4.2 VITA ARCHITECTURE DESIGN
ODE Solving
Ground-truth
Vision
Action Encoder
Encoder
Zo
Z1
L AE
Camera Image
Flow Matching
Action Decoder
Flow Latent
L P
Zo: latent images
2 generated latent actions
Decoding
Action
Z target latent actions
Decoder
Figure 2: An overview of the VITA architecture: The vision encoder maps observations into source latent representation 2g for the flow; the action encoder provides a target latent representation ø for flow matching training. The action decoder learns to decode &1 (latent actions generated by solving ODEs) to actions via flow latent decoding losses, and decode z to actions (latent actions from action encoder) via autoencoder losses. The flow matching network learns the velocity field over continuous flow matching path from 2 to Z1.
As depicted in Figure 2 VITA is composed of three primary components: 1) The Vision Encoder (E,) maps observations O to the latent representation 2, (O), where E IR Phese serves the source of the flow. 2) The Action Autoencoder (AE) consists of the Action Encoder and the Action Decoder, and learns latent representations for action chunks. The Action Encoder (E.) maps the
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ground -round-truth action chunk A to latent actions 21 (A), where #1 E Pamm serves the target for flow matching: the Action Decoder (D₂) reconstructs action chunk À Da from latent actions 21. 3) The Flow Matching Network (va) is learned to predict the velocity field at arbitrary t.
4.3 BRIDGING THE MODALITY GAP BETWEEN VISION AND ACTION
key constraint of flow matching is that the source and target distributions must share the same dimensionality. This poses critical challenge for vision-to-action policies, since action spaces are typically much lower-dimensional than visual representations. For example, action dimensionalitics range from 2 PushT to 21 ThreadNeedle, whereas visual representations be 512- dimensional (Zhao et al. 2024) or even higher when using grid-based features.
To bridge the gap, one naive option is to down-sampp latent visual representations to the action chunk dimensionality, which, however, causes severe information loss and degrades performance. Alternatively, one can up-sample actions with zero- vero-padding. yielding sparse and unstructured targets that hinder flow matching training (see Appendix [B.].. A third alternative is a pre-trained, frozen action AE, akin common practice in latent diffusion for image generation (Rombach et al. 2022). but this proves ineffective for learning flow matching over latent actions: with sparse and limited action data, the induced latent space is unreliable as a flow target and cannot be corrected once frozen (see Appendix B.2). As another alternative, jointly training the action AE with flow matching may still fail; our empirical studies identify the root cause as latent action space collapse induced by the training-inferent gap in action decoder inputs, as detailed below.
Training-Infern Inference Gap between Encoder-Basee and ODE-Generated Latent Actions. During training, the decoder reconstructs actions from encoder- based latent actions 21, whereas inference it decodes 21 generated by solving the flow matching ODE. Since ξ1 is an approximation and does not always align with z1, the decoder can fail to map them into meaningful actions. To address this gap, we propose flow latent decoding, which enforces the model to decode from ODE-generan latent actions & during training, anchoring the latent generation process with ground ttunhhhhthhhha actions.
4.4 VITA LEARNING OBJECTIVES
Building upon our analysis of the training-inferenee gap, we now formulate a comprehensive learning framework for VITA that prevents latent collapse and ensures effective end-to-end optimization. Our framework includes three essential objectives: flow latent decoding (FLD), flow matching (FM), and action autoencoder (AE) losses, each addressing distinct aspects of the learning challenge.
Flow Latent Decoding (FLD). FLD addresses the training-inference gap by anchoring ODE- generated actions using ground-truth truth actions during training. Formally, FLD is defined as the reconstruction loss using ODE-generated latent actions, LFLD ( - All. where ê is obtained by solving the flow ODE with an Fuler solver during training. FLDD propagates gradients through the decoder and the ODE solver into both vo and &.,. By decoding 21 into actions and measuring reconstruction error directly in action space, FLD effectively minimizes the discrepancies between encoder-based and ODE-generated latents.
Flow Latent Consistency (FL.C). To gain deeper insight into the mechanics of FLD,, we introduce flow latent consistency (FLC), a minimalist surrogate that directly aligns DDDE-geneaate and encoder- based latents without decoding. Formally, FLC is defined as LFLC ق 21 Under mild local regularity assumptions on D, (stated below), FLC and FLD provide locally equivalent training signals for the same 21. Empirically, FLC also prevents collapse without explicit action reconstruction, though convergence is slightly slower than with FLD (Section 5.3). This theoretical connection not implementation. A sketch of
only validates our approach but also offers computational flexi the analysis is given below, with full proofs and corollaries deferred to Appendix A
Assumption 1 (Decoder locally well-behaved). DD, is C² in a neighborhood of 21, with Jacobian singular values bounded as m S miin S <maax S on that neighborhood. Let EAE == [( All denote the local AE reconstruction error.
Theorem (Local equivalence of FLD and FLC). Under Assumption 7 for any 21 in the neighbor- hood, we have m 1 21ll
EAE v (()) All < T. 1 21 CAE-
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If FAE 0, the minimizers of LELD and LELC coincide and equal {z1): 0, any mininizer of CFLD lies within radius E m of %1.
This theoretical result confirms that FLD and FLC target the same underlying optimization objective.
We further discuss this in Section 5.3 showing that including the flow latent decoding loss with non-zero ÄELD critical for avoiding latent space collapse and training successful VITA policies. We will also ablate the effects of ÄLLD, AFLC. and AAE in Figure 6
Flow Matching and Autoencoder Losses. The flow matching loss supervises the flow network Ug by minimizing the MSE between the predicted velocity and the ground-truth velocity as shown in Equation IT. The action autoencoder loss trains (Ea, Da) to reconstruct action chunks using an LI loss, CAB = || (E, [1, where
&a (A) serves as a structured target latent with small reconstruction bias and good local conditioning. This structured latent space strengthens theoretical link between FL.D and FLC, and further stabilizes training.
The training objective a weighted sum of all three losses: LVTTA AFM AELDCFLD AAELAE-
5 EXPERIMENTS
We evaluate VITA on 9 simulation and 5 real-world tasks spanning both bimanual and single-arm manipulation. The bimanual tasks include 5 simulation (Figure 3) and real-world tasks (Figure 4 on AV-ALOHA (Chuang al. (0024,, which augments AL.OHA (Zhao al. (024) with active- vision camera mounted on an additional 7-DoF arm. This challenging suite features high-precision requirements, non-stationary observations enabled by active vision, and 21-DoF high-dimensional actions. The single-arm tasks include 3 real-world tasks using one ALOHA arm (Figure 4) featuring high randomization in object positions or colors, and simulated tasks, including 2 Robomimic tasks (7D actions), PushT (2D actions), and CloseBox (9D actions) from RLBench (James et al. 2020).
For simulated tasks, each environment provides 100-200 demonstrations. AV-ALOHA demonstrations were collected via expert teleoperation in VR, using the left-eye image input; single-arm ALOHA demonstrations were collected using a leader arm. For the remaining tasks, we use publicly available datasets. In real-world AV-ALOHA experiments, each task is trained from 50 demonstrations using the left stereo image, and the single-arm ALOHA tasks are trained from 50-100 demonstrations using the wrist camera and overhead camera. See Appendix Dorr details.
Cube Transfer
Slot Insertion
Hook Package
Pour Test Tube
Thread Noedle
Square
Can
Autonomous rollouts of VITA across AV-ALOHA tasks (CubeTransfer, Insertion. HookPackage, PourTest Tube, Threa dNeedle), and Robomimic tasks (Square, Can). Notably, the AV-ALOHA tasks demand high-precision control, such as accurately pouring small ball into a narrow tube opening, or threading a needle through a tiny hole.
Figure 3:
5.1 EXPERIMENT SETTINGS
Flow Matcher: We adopt OT-CFM (Tong al. 2023a), which is based on optimal transport, and solve the ODE with an Euler solver using 6 linearly interpolated time steps for both VITA and FM.
Vision Encoding, We use ResNet-18 (He et al. 2016) the vision encoder. A common practice in visuomotor policy learning to use the vector- visual features after global average pooling (Su Chi al. 2023 in which case VITA operates entirely vector representations for both
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Hidden Pickup
Transfer From Box
Pick Ball
Tooth Brush
Store Drawer
Figure 4: Autonomous rollouts of VITA on five challenging real-world tasks, including two bimanual AV-ALOHA tasks, HiddenPick ck, and TransferF romBox using active vision, and three single- arm ALOHA tasks, PickBall, ToothBrush, and StoreDrawer
vision and action. We additionally evaluate variant of VITA using the 9 x 512 ResNet grid-based features to preserve more spatial information and assess VITA's scalability.
Baselines. Our baselines include state-of-the policies, including flow matching (FM) pol- icy (Zhang & Gienger 2024), diffusion policy (DP) (Chi et (203), and action chunking transformer (ACT) (Zhao et al. (023). We evaluate both efficiency and performance in Section 5.2
Training. We train VITA and baselines predict action chunks of length 16, of which the first actions executed. We train VITA and FM each task for 25K-50K steps. We follow the ACT and DP implementations LeRobot (Cadene et al. 2024). Since VITA and FM converge much faster than DP or ACT (a known advantage of FM methods (Lipman al. 2023)
extend DP training to 100K steps, and ACT to 100K-2008 steps. The training batch size is 128. See Appendix for more detailed training settings.
Evaluation. We use ODE steps for VITA and FM, and 10 DDPM (Denoising Diffusion Probabilistic Model) steps for DP (Ho et al. 2020). In simulation, using 50 episodes per eva eva luation and report the best success rate (SR) averaged over 3 seeds (Table 2h. For real-world tasks, we evaluate over 20 episodes per checkpoint on three single-arm tasks (Table 3 and two bimanual manipulation tasks (Appendix D.2.1] Efficiency gains of VITA are analyzed in Section 5.2.1 with all latency and memory measurements obtained on single NVIDIA RTX 4090.
evaluate the policy every 200 training steps
5.2 PERFORMANCE
The main objective of this paper is to improve the efficiency of visuomotor policies. This section demonstrates that VITA is a fast and precise visuomotor control policy, delivering efficiency gains (Section 5.2.1) I while matching or surpassing state-of-the-art methods in success rates (Section 5.222). Appendix BB. 8.2 further analyzes VITA's advantages in fast convergence and high precision. Ap- pendix B.8 demonstrates VITA's 's robustness to online perturbations, and Appendix E.2 evaluates its generalization to unseen objects.
5.2. EFFICIENCY
FM and DP are typically parameterized using U-Nets (Ronneberger al., 2015 or diffusion trans- formers (DiTs) (Peebles & Xie 2023), which predict velocity fields or noise at each denoising step. U-Nets and DiT's are often large and computationally costly, which limits their real-time deployment that requires highly efficient inference. These architectures rely on explicit visual conditioning modules, e.g., cross-attention, FiLM, or AdaLN, which must be exccuted at every denoising step and inevitably increase both inference time and memory footprint.
key determinant of efficiency both conventional FM and VITA is the choice of visual latent representation. In conventional methods. vector-based and grid-based latents require different conditioning modules and thus incur different computational costs. In VITA, the visual and action latents must share the same dimensionality, the latent choice directly determines the FM network
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architecture. We analyze the implementations and efficiency of VITA and conventional FM in these two settings and explain why VITA yields efficiency advantages in both.
Vector-Based Visual Latents. With vector-based visual features, conventional methods typically employ FiLM (Perez et al., 2018) or AdaLN (Peebles & Xie 2023) to condition the FM network. These methods compute modulation parameters via a separate conditioning network at every denoising step. FiLM modulates network outputs at each feature channel; AdaLN modulates normalization statistics each network layer. The FM network often implemented using transformers or U-Nets to effectively process noisy action chunks of shape T X Duction. and fuse in visual latents. In contrast, since VITA uses rector-based latents for both the vision source and action targets, the FM network reduces to vector-to- pecttorto-vector mapping, with no need to fuse visual information. This enables highly lightweight MI.P-nll architecture choice. We show that MIP--based VITA achieves better efficiency (both latency and memory) than the most efficient FM baseline (MLP-basee) while matching the task performance of the strongest FM baseline (transformer-basee based).
Grid-Based Visual Latents. With grid-based features (e.g.. 9 512), cross-attention is often used in transformers to fuse visual tokens and action chunks for conditioning (Dasari et al. (024), which introduces quadratic time and space complexity. In contrast, VITA eliminates cross-attention in transformer-based implementations. As shown in Appendix B.6.2 VITA attains strong performance while being more efficient (Table goeeeggribbbbaa grid-based settings).
To provide a comprchensive efficiency comparison, we implement two FM parametcrizations (DiT's (Peebles & Xie 2023) and U-Nets (Konneberger al., 2015)) and three conditioning mecha- nisms: AdaLN (Peebles & Xie 2023) and FiLM (Perez et al. (0188) for vector- based features, and cross-attention (Gong al. (024) for grid-based features.
We compare the inference latency and inference memory usage of VITA and other FM methods in Table [ both vector-based and grid-hased representations. VITA achieves inference wall-clock time of 0.22 ms (vector-based) and 0.25 ms (grid-based) per action chunk, which is 1.5x and 2x faster than the best-performing FM baseline (transformer-basee FM with similar model sizes) that incurs higher latency (~0.33 ms and ~0.51 ms). VITA effectively reduces memory usage: the peak memory is 18.6% less than FM in the vector-based setting, and 28.7% less in the grid-based setting. Additionally, we discuss the training time and space efficiency in Appendix B.7 We further compare against FM accelerated with simple MLP architectures similar model size to isolate the effect of architecture design in the vector-base setting. VITA remains 1.3x faster at inference and uses 19.3% less memory, while FM with MLPs fails to achieve competitive success rates (Appendix
(.rrendixB.6.1.) B.6.1..
Table Comparison of the time and space efficiency of VITA and flo--mathhing baselines, grouped by the type of visual latents used ("Vector" or "Grid" based). Metrics include: model size, inference latency (ms/chunk, batch size 1), and inference memory (MiB), (see Appendix B.7.2 for inference memory measurement details).
Visual
Model
Architecture
Conditioning
Params
Latency
Memory
VITA
MLP
N/A
31.09M
0.2215
333.86
FM
Transformer
AdaLN
31.16M
0.3307
410.38
Vector
FM
U-Net
FiLM
84.05M
0.3650
818.79
FM
MLP
AdaLN
32.20M
0.2831
413.95
DDPM
U-Net
FiLM
81.82M
2,5985
801.47
VITA
Transformer
N/A
31.80M
0.2502
377.55
Grid
FM
Transformer
Cttss-Attn
29,06M
0.5102
529.16
5.2.2 SUCCESS RATES
We evaluate VITA against state-of-the-ar policies on 9 simulated tasks and 5 real-world tasks. We report SRs for FM using the transformer-based FM with AdaLN, as it achieves the strongest performance among the FM variants evaluated in Section 5.2. other variants, such as MLP-dased FM. perform poorly (Appendix I. while FM using attss-attnntion or U-Net reaches similar SRs but is substantially slower train. Across the 9 simulated tasks (Table 2) and the 3 real-world single- arm ALOHA tasks (Table B. VITA consistently outperforms or matches state-of of-te-of-thheart methods. We further evaluate VITA 2 challenging real-world bimanual AV-ALOHA tasks (Appendix D.2.1
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featuring 21-DoF high- dihhesninniional actions and active vision. We discuss the under- under-performance of DP Appendix B.8.2, which is largely due to the tigh-precision requirements and stringent success criteria of multi- stage ALOHA tasks.
Table 2: SRs simulated tasks AV-ALOHA, Rohomimic, PushT, and C1 oseBox. We report the mean and the standard deviation of the best SRs during validation across 3 random seeds.
Task
VITA
DP
ACT
ThreadNeedle
1..3
90+2
19.33+
44.67=14.47
SlotInsertion
78:12
82::
50.67::5.03
47.33-12.11
PourTestTube
78.67 12.31
86::2.11
46:0
42:7.21
HookPackage
86:t2
82+2
37.33+6.11
32:2
CubeTransfer
100=00
100+0
94.67+3..06
99.33+1116
PushT
88:+2
1.13
74.67+6.11
28=5.29
Square
14.16
87.33.1.3.66
84
72.12
Can
100.10
100.10
15.
88.67.1231
CloseRox
1.13
85.33
85.3311.16
72.25.29
Table Comparison of SRs on three real-world single-arm ALOHA manipulation tasks. Each task is decomposed into subtasks, and SRs are reported per subtask.
PickBall
St StoreDrawer
ToothBrush
Pick
Place
Pick
Place
Close
Pick
Insert
VITA
0.75
0.70
1.00
0,95
0.95
0.80
0.50
FM
0.75
0.65
0.90
0.90
0.90
0.90
0.50
DP
0.60
0.60
1.00
0.95
0.90
0.60
0.30
ACT
0.50
0.45
0.65
0.65
0.50
0.70
0.30
5.3 ABLATION FLOW LATENT DECODING
We investigate the importance of FLD for effectively training VITA policies. As demonstrated by Figure Sia,, the latent actions generated at inference may not decode into meaningful actions because of the training-infere inference gap between encoder-based latent actions z1 and ODE- -DE-ggeeratee latent actions ě discussed Section 4.3 We propose two objectives that backpropagate through ODEs to minimize the discrepancy: 1) FLD, which enforces accurate reconstruction in the raw action space by comparing Da (²) against the ground-truth action; and FL.C, which enforces alignment in the latent action space between ODE-generate, latents &n and encoder-bar based latents ø1.
80
Greusd-rrek Artions
Ground-trvk Actkas
Success Rate
60
40
FLD
FLD
FLC
FLD+FLC
0-
2
a) VITA
b) VITA w/o FLD
Step
10"4)
Figure 5: Comparison of reconstructed actions between (a) VITA, and (b) VITA without FLD.. Reconstruction fails without FLD because of latent space collapse.
Figure 6: Success rates using differ- ent objectives.
Figure 6 shows that the model completely fails to learn without FLD (i.e., \FLD 0) due to latent collapse, while applying FLD succeeds in learning the policy. FLC provides a weaker signal compared FLD (because FL.D directly anchors generation using the ground-truth actions), resulting in slightly

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slower convergence. However, found that using a combination of both objectives in practice yields the best performance due to richer learning signals both raw and latent action spaces.
5.4 VITA DENOISING: FROM VISUAL LATENTS TO PRECISE ACTIONS
Figure 7 compares the denoising processes of VITA and conventional flow matching. In conventional methods, the ODE solver transports samples from an uninformative isotropic Gaussian prior to highly structured action space, and therefore requires repeated visual conditioning to guide denoising. In contrast, VITA initializes the flow directly from visual representations. Owing to the end-to-end joint optimization enabled by FLDD, the visual and action latent spaces are co-evolved, leading to closely aligned latent manifolds vision and action in practice. Empirically, we observe that the latent visual representations already encode coarse action semanties and can be directly decoded into preliminary action trajectories even before any ODE integration, demonstrating that VITA learns an action-centric visual representation. The close alignment between vision and action manifolds also explains why a lightweight MLP suffices for VITA, whereas MLP-only FM struggles due to the need transport from an unstructured Gaussian prior to a structured action space (detailed in Appendix B.
(a) Conventional Flow Matching Denoising
Source (Gaussian Noise)
Step
2
3
4
5
6
(b) VITA Denoising
Source aaten Image)
Step
2
3
4
5
6
Action Decoder
Figure 7: (a) Conventional flow matching transports Gaussian noise into action chunks, requiring repeated injection of visual information to shape action trajectories. (b) VITA initializes the flow from visual representations. Remarkably, the end-to-end optimization induces action-centric visual representations aligned with the latent action space. As result, the initial visual latents be directly decoded into preliminary action trajectories, which the ODE solver then smoothly refines into precise actions.
CONCLUSION
We developed VITA, an efficient and high-perforning visuomotor policy that generates actions in a noise-free manner by directly evolving visual latents into action latents. VITA removes the need for explicit conditioning modules (e.g., cross-ates -ooss-attention), simplifying architectures and improving efficiency. When employing vector representations for both visual and action latents, VITA reduces the flow matching network conditioning- frre vector-to-vece only architecture for complex visuomotor tasks. We show that introducing latent action space the flow matching target critical for successfully learning the vision-to-action flow. To enable effective end-to-end training, we propose flow latent decoding (FLD), which bridges the training-inference gap between encoder- based latents and ODE -oDeeeeredtedd latents. FLD may serve as principled approach that can be applied to other generative tasks and latent action policies. Extensive experiments demonstrate that VITA achieves ofate-of-the--e art efficiency and performance on both simulated and real-world tasks, while converging rapidly and stably with high-precision control.
mapping, enabling simple MLP.
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7 ETHICS STATEMENT
All experiments were conducted in simulation environments or on standard robotic platforms without involving human subjects, sensitive data, any form of personal information. Thus, there are no privacy, security, or human participant concerns. The datasets we use are publicly available benchmark datasets, and no proprietary or restricted data were employed. No conflicts of interest or external sponsorships influence the reported findings.
8 REPRODUCIBILITY STATEMENT
We take multiple steps to ensure reproducibility of our results. A detailed description of our model ar- chitecture, training objectives, and algorithmic choices is provided in the main text. Hyperparameters, training configurations, and ablations are reported the Appendix. For theoretical derivations (e.g.. flow matching formulation), complete proofs and assumptions materials. To facilitate replication, we include open-source code with training scripts, evaluation pipelines, and configuration files. All datasets used are publicly available (Robomimic, ALOHA).
included in the supplementary
ACKNOWLEDGEMENTS
This work is supported in part by NSF Grants ARO Grant 1911NR-2410066.
CNS-2203239, ECCS- ECCS-2413529, and
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A PROOF OF FLD AND FLC EQUIVALENCE
A.0.0.1 Preliminaries. All norms below are vector norms with induced operator norms. We use the ball (z), {æ : || - z1 r}. Assumption I in the main text holds throughout.
Lemma (Local bi Di-Lipschitzness from Jacobian bounds). For any 21 E (=1,TT).
m | z1 v [[ - Du(z))ll - L 2 - z1ll-
Proof of Lemma
Let ((8) = 21 + s 21) for [0, 1]. The mean-value integral formula gives
Da(2)) - (a))))) = f." Jo. (7)(s) - -21) 21)ds.
Taking norms and using, for any matrix J and vector + 0, min (J)l & || S max ( [ell together with m S (mi, (7())) and Tmax (n(s))) S for all s, yields the bounds.
Proof of Theorem Add and subtract (=1) and apply tranngeeieeeens--rinnggee inequalities:
[[Da - All S [(),( (z I|| (P.. -Al,,
[| A| 2 - (2) Al|-
Invoke Lemma and set A (1)a A obtain the two-sided inequality stated in Theorem The minimizer claims follow immediately: SAE 0, both losses are minimized at & = 21: otherwise any minimizer of FLD must satisfy || 21 EAE/m.
A.0.0.2 Corollary A.1 (squared-loss version).
Assume AE = 0. Then
m² || - 11 < [ - 2 < L 11 =11
With SaF 0, Lemma gives m||=; - 21 < - S L||=1 =1 |l. Since both sides
are nonnegative, squaring preserves the inequalities. Thus the squared FLD objective is sandwiched between m² and - times the squared FLC objective. Consequently, the map ε + ( A is locally I.²-smooth and mm-strongly convex along latent directions (intuitively, its curvature is controlled by JDD_" JD. whose eigenvalues lie in [m? I.?)..
A.0.0.3 Corollary A.2 (gradient scaling for squared losses). Let == JD. (21) and assume EAE = O. For the squared losses,
V ((2) = 2J 2 - A),
(22) V = 2 - 21).
V
Then
m² /CRLoll < | - L V
Use [|J b/l and Lemma ! to get m |!vl - || yll L
[mlly]]. L|yy|| (by singular-value bounds) with y = D2 (2(2)) A -
|| and m | - =1| S |Mvl < L - - Multiplying
the bounds yields m m =1 S |J L 21|1. Since |VC =2| z1 1l this
gives the stated inequality (up to the common factor 2). It follows that step-size sensitivity is governed by the squared condition number (I./m)².
B DISCUSSIONS
B.1 DIMENSIONALITY MATCHING FOR VISION-TO-ACTION FLOw
A constraint of flow matching is that the source and target must have identical dimensionality. In visuomotor contexts, the visual latent representations (æn) are typically much higher-dimensiona. than raw action chunks (A). A naive solution would be down-sample the visual representation to match the action dimensionality. which, nevertheless, leads to significant information loss and poor performance, particularly when the dimensional gap is large.
Therefore, we adopt the opposite strategy: we map the raw action chunks into a digees-dimeesional latent space that matches the dimensionality of the visual latent representations.
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A naive approach is to fixed linear transformations, When the action dimension is smaller than the latent dimension, we construct a lossless mapping by embedding the actions into the higher- dimensional latent space through zero-padding. The inverse mapping simply discards the padding. Our experiments show that action representations produced by such transformations are insufficient for learning reasonable flow matching policies. We found that learning well-structured action latent spaces via autoencoders as the target distributions for flow matching is crucial for the success of vision-to-action flow. We develop an action encoder (E,), which does simply remap dimensions, but also learns structured latent action spaces, making the complex flow from vision to action more tractable.
Table 4: Task SR (%) on ThreadNeedle with different action up-sampling strategies.
Up-sampling Strategy
SR (%)
Zero-Padding
0
Action AE (w/o FLD)
0
Action AE (w/ FLD)
92
B.2 ABLATION OF FROZEN TARGET FOR FLOW MATCHING
Learning a flow when the target distribution lies in a learned latent space is inherently challenging. Jointly optimizing the flow and the latent encoder creates moving target, as the latent space continually shifts during training; it also introduces a training-inference gap: flow matching does not guarantec that ODE-generated latents are decodable during inference, since the decoder is trained to reconstruct encoder- based latents.
10
80
(a) Success Rate
(b) (b) Action MSE
60
Ma Monchmm IN
10-2
40
VITA
VITA
20
VITA (frozen action AE)
VITA (frozen action AE)
10-3
0
0
5
15 Step (* 10"4)
10
20
25
5
10
15
20
25
Step (* 10"4)
Figure 8: Success rates (left) and log-scaled action MSEs (right) comparing end-to-end VITA training with VITA using frozen action AE ThreadNeedle, EMA 0.9.
natural solution is to follow the strategy in latent diffusion models (Rombach et al. 2022) : pre-train the latent space large-scale data and freeze it when training the flow. We evaluate this approach by pre-training the action autoencoder for 100k steps using a reconstruction loss, CAE. then freezing while training the VITA policy for 25k steps (same as VITA trained from scratch). As shown in Figure 8 this frozen-latent setup yields poor online success rates and high offline MSEs that plateau early. contrast, end-to-end VITA training enabled by FLD performs substantially better. The underlying issue is that, unlike image generation, robotics action data is sparse and limited; pure latent pre-training produces a weak representation that cannot be improved once the latent space frozen.
B.3 CONTRASTIVE LATENT ALIGNMENT
Section 4.4 introduced two key objectives, FLD and its surrogate FLC, which are both designed to prevent latent space collapse and enable effective VITA learning. Empirically, find that FLD
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alone outperforms FLC alone, provides more direct training signal by anchoring the generation process to the ground actions. However, the most robust performance is achieved by combining them, which enforces consistency in both latent action space and raw action space.
Additionally, motivated by the ability contrastive learning to improve representations and pre- vent latent collapse (Liu et al. 2024a), introduced contrastive loss between vision and action latents (Radford et al.) 2021). We show that this objective can further boost performance be- yond FLD and FLC some tasks by encouraging the model learn representations where the similarity between corresponding vision-action pairs is maximized, while the similarity between non-corresponding pairs minimized.
We employ InfoNCE Conss-Cvntr Estimation) for contrastive learning between vision and action. For given batch of size N, the vision latent z0, and action latent 11,4 from the same data sample are treated positive pair. All other non-coreepponding combinations (zo, ø1.j) where # are considered negative pairs. The loss aims maximize the similarity of positive pairs while minimizing the similarity of negative pairs. The symmetric InfoNCE loss is defined as:
exp(sim(zo.z) 21,1)/T)
log exp(sim(= Z TT.i)/T)
Coontrastive 2NV
Σ
log
Σド=1 exp(sim(Zo.t /1.) Ej=1 exp(sim 21,1, zo.j
where sim(:.)) denotes the cosine similarity between L2-normalized feature vectors and temperature hyperparameter.
As shown in Figure the contrastive objective, when used alone, is insufficient for effective VITA learning and underperforms VITA with only the FLD loss (see Figure ( However, it provides additional performance gains when combined with the FLD and FL.C losses on some tasks, as it helps create more robust and well-structured latent space.
80
Success Rate
60
40
Contrastive
20
FLD+FLC
LDD+FCC+Contrasttn
1
2
3
4
5
6
Step (* 10-4)
Figure 9: Success rates using different VITA learning objectives with or without contrastive losses.
B.4 ABLATION OF THE VARIATIONAL ACTION AUTOENCODER
In our experiments, deterministic autoencoder (AE) learns the target latent space for actions. To investigate the effect of imposing prior on this latent space, conducted an ablation study replacing the AE with a variational autoencoder (VAE) (Kingma et al.) 2013). This change introduces a KL divergence regularization term, weighted by AKI., which encourages the encoder's posterior output,
q(z) A), to match a standard normal prior:
LKL DKL(q(z1 4) p((z))),
p(z1) = (O( T).
As shown by the training curves in Figure a0 and Figure 2T with various weights (AKI.) can degrade model performance.
incorporating this variational objective
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(a) Action KL Regularization
(b) Observation KL Regularization
Ar Wr ww MaP w
Awn mm Mywmh
Success Rate
60
www.maaaa MMm
60
action_M M -
observation,
action kl 1e.4
observation, ki 16-4
0.5
1
1.5
2
255
0.5
3.5
255
step 4)
step 10"4)
Figure 10: Ablation of VAE for action (a) and vision (b) on ThreadNeedle.
B.5 ABLATION OF THE VARIATIONAL VISION ENCODER
As depicted in our main architecture (Figure 2). the source latent variable 2n = ( E TR Puss is produced by a deterministic vision encoder. We performed similar ablation to assess the impact of introducing stochasticity to visual encoding. Specifically, we replaced the deterministic encoder with a variational one that models the posterior ( O) and introduces corresponding KL divergence loss (weighted by X joh
KL
Cobs KL
DRI(U(zO 10) [[)(()), p(=0) = ().
As shown in Figure 10 b), employing VAE for the vision encoder drastically degrades performance.
Using a VAE to model the source distribution introduces stochasticity, which is often desirable for generative tasks that emphasize diversity (Liu et al. (024a)). Similarly, stochasticity in DP and FM helps policies model action multi-modalle (Chi et al. (023). However, we found that making the visual latent encoding stochastic degrades VITA performance. In robotics tasks that demand extremely high precision, such ThreadNeedle where millimeter-level errors can variational objectives tend to blur latent representations, discarding critical visual details. As result, deterministic vision encodings yield substantially better precision and performance. We discuss the precision requirements of robotics tasks in greater detail in Appendix B.8
complete failure,
B.6 ABLATION OF NETWORK ARCHITECTURE
VITA, when using vector representations for both vision and actions, reduces the flow matching network to conditioning- free vector-to to-veetor mapping. We find that even MLP-only architecture can successfully learn challenging, high-precision visuomotor tasks, including bimanual manipulation with active vision on AV-ALOHA. To the best of knowledge, VITA is the first visuomotor policy to master such complex tasks using MLPs.
B.6.1 FM USING MIP
We evaluate FM using the same 4-layer MLP architecture as VITA (with a similar parameter count of 330M). Concretely, we remove self-attention from the DiT-based FM network and retain only AdaLN (which is also MLP-based) for visual conditioning. However, the MLP-only FM fails to learn effective policies: on PushT, the reward remains around 0.4 and success rate remains 0% even after 100K training steps, as the task requires high-precision control (successful only when reward exceeds 0.95). In contrast, VITA and transformer-based FM achieve 88% and 83% success, respectively. This failure arises because MLP is insufficient for processing noisy action chunks and integrating visual conditioning at each denoising step. leading to poor control precision, as shown in Figure [Tb), where action MSEs of MLP-based FM plateau while VITA yields significantly lower MSEs. Since success rates are sparse on PushT, we also report online reward curves (see Figure I| Iaa to more clearly compare MI.P-based FM and VITA.
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0.8
(b) Action MSE
10*
(a) Reward
10a
0.4
VITA (MLP)
VITA (MLP)
FM (MLP)
10a
FM (MLP)
9
Step (* 10-4)
Step (* 10 4)
Figure 11: Reward curves and the action MSEs (log-scaled) of MLP-only VITA and MLP-only FM on PushT. FM learning is ineffective because lack of precision, and performs poorly online, necessitating effective architectures process action chunks and fuse in conditions.
The fundamental reason is that conventional FM learns transport from an unstructured isotropic Gaussian prior to a highly structured action distribution, which places a heavy burden on the flow network to simultaneously denoise, integrate visual conditioning, and recover precise control signals at each step. This requires strong architectural inductive biases (e.g., transformers or U-Nets) to effectively model high-dimensional noisy action chunks. In contrast, VITA operates between two structured and semantically aligned latent spaces, where visual latents already encode coarse action-relevant structure. As result, the flow only needs perform incremental refinement rather than reconstruct actions from noise, allowing a lightweight MLP architecture to suffice without sacrificing control precision.
B.6.2 VITA USING TRANSFORMERS
Additionally, we show that VITA is not limited to vector-based features MLP. We evaluate VITA using grid-based features (in particular, 9 x 512 spatial features obtained via ResNet- 18), and use transformer for the flow matching network. The flow matching network does not require any conditioning for spatial tokens such costly cross-attention compared to FM using transformers.
We evaluate VITA on multiple challenging tasks, demonstrating that VITA yield high success rates and Appendix B.7).
(see Figure 12 while retaining the efficiency gains (see Table and
CubeTransfer
Can
ThreadNeedle
Success Rate
Success Rate
Success Rate
40
40
40
20
(MLP)
20
VITA (MLP)
20-
(MLP) VITA (Transforer)
VITA (Transtormer)
VITA (Transtormer)
0
0
0
20
40
50
20
30
40
50
20
30
40
50
Step
Step 10"4)
Step 10"4)
Figure 12: Comparing success rates of VITA using MLP and vector-based features and VITA using transformers and grid- features.
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B.7 EXTENDED EFFICIENCY METRICS
B.7.1 TRAINING TIME& & SPACE OVERHEADS OF FLOW LATENT DECODING
Recall that we introduce FLD during training to enable effective end-to-end learning, which requires solving 6-step ODEs. Although this inevitably adds some training-time overhead, we trade modest increase in training cost for significantly more efficient inference. which is critical for real-time robotic deployment. For MLP-based VITA, the training time increases from 0.677 ms/chunk (without FLD) to 0. 7.0 ms/chunk (with FLD), corresponding to only 9.3% overhead. For transformer- based VITA, the cost rises from 0.766 ms/chunk to 0,951 ms/chunk, a 24.1% % overhead. A similar trend appears for GPU memory usage: for MLP-based VITA. peak training memory increases from 2716.62 MiB (without FLD) to 2835.86 MiB (with FLD). a relatively small 4.4% increase. Despite these modest training-time and memory overheads, FLD enables stable end-to-end optimization and yields markedly improved inference-time efficiency, a critical requirement for real-time robotic deployment. Additionally, VITA maintains the lowest training-time memory usage compared to all baselines even with FLD enabled, while achieving comparable training latency.
B.7.2 TRAINING MEMORY USAGE
We have reported the absolute peak memory usage of each policy architecture in Table measured via torch. cuda max max__mmo
located for fair comparison. Importantly, we exclude the shared visual encoder, which transforms raw RGB images into visual latents and is used identically across all methods. This observer accounts for approximately 1732.5 MiB of peak GPU memory across all methods, and does not help differentiating memory usage across policies. For instance, after removing observer cost, VITA (MLP) peaks at 333.86 MiB, FM (MLP, AdaLN) peaks at 413.95 MiB, and FM (Transformer, Cross-Attn) reaches 529. 16 MiB. These numbers reflect the true architectural and conditioning differences in the policy modules themselves. Consequently, VITA (MLP) reduces peak memory usage by 19.4% relative to FM (MLP, AdaLN), 18.6% relative to FM (Transformer, AdaLN), and 36.9% relative FM (Transformer, Cross-Attn), highlighting its inference-tinee time efficiency gains from conditioning- formulation.
In Table 5 we report the absolute peak memory usage during training for each policy. As with inference, we ablate the memory consumed by the shared visual encoder, and record peak memory after vision encoding. For VITA, we also include the peak usage observed during the flow laaaaaeettt. ltw.eatteeeeeddinnneeee phase, which accounts for the highest memory load. This choice ensures that the reported number reflects the true upper bound of VITA training footprint with FLD.
Table 5: Comparison of the conditioning parameter overhead, training- time cost, and training-ting time memory usage of VITA and baselines, grouped by the type of visual latents used ("Vector" or "Grid" based). Metrics include (i) parameters introduced solely by conditioning modules, (ii) training time per chunk (ms), and (iii) peak GPU memory during training (MiB).
Visual
Model
Architecture
Conditioning Cond. Params (M)
Time
Memory
VITA
MLP
N/A
0.00
0.740
2835.86
FM
MLP
AdaLN
11.82
0.664
2926.60
Vector
FM
Transformer
AdaLN
6.58
0.697
3071.88
FM
U-Net
FiLM
11.33
0.782
3676.38
DDPM
U-Net
FiLM
9.49
0.779
3643.04
VITA
Transformer Transformer
N/A
0.00
0.951
2977.10
Grid
FM
Cross-Attn
4.47
0.812
3585.06
B.8 CONTROL PRECISION VS. SAMPLING STOCHASTICITY
B.8.1 VITA WITH SAMPLING STOCHASTICITY
Since stochastic visual encodings degraded performance (as discussed in Section B.5 and we hypothesized this due to precision loss from blurred latent representations, further investigated the trade-off between stochasticity and control performance. We examined VITA variants with different sources of sampling stochasticity. Introducing dropout in the network, or variance (a) in
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flow matching, where a injects Gaussian noise along the interpolation path, consistently reduced performance. contrast, adding covariance to the source distribution produced results comparable to deterministic encoding. Likewise, using variational objective in the action autoencoder performed similarly to the deterministic action autoencoder, whereas applying a variational objective to the image encoder significantly harmed performance (see Figure TQ).
These findings resonate with our observation that DP underperforms FM or VITA on ALOHA tasks that require high precision. DP based on SDE (stochattic differential equation) while FM deterministic ODE formulation. FM introduces stochasticity only through sampling the Gaussian prior, VITA goes even further by removing the Gaussian prior sampling, instead using visually grounded and deterministic initial state for the flow. Together, these results can suggest a broader trend: for fast and precise real-time control, reducing stochasticity can beneficial e.g., speeding up convergence, producing more precise and faster policies.
B.8.2 PRECISION CRITIIALL ROBOT CONTROL TASKS
We followed DP and ACT implementation from LeRobot (Cadene et al. 2024). However, DP and ACT perform poorly (00% SRs compared to 80%-90% of VITA and FM) ALOHA tasks such as ThreadNeedle e and PourTest Tube, which demand high precision.
This section examines the stringent success criteria of these tasks and explains why even small action errors lead to failures. We further show that VITA and FM achieve substantially better action precision with far fewer training steps, which contributes to their superior performance on these precision-demanding tasks.
Stochasticity helps model action multimodality, which has been considered key factor in the success of generative policies (Chi et al. 2023). However, VITA does not involve stochastic sampling by default. Recently, Pan al. (2025) show that multimodality alone does not explain the success of generative policies. We further show that precision can be more critical for many robotics tasks, and deterministic sampling can help improve precision.
Success Criteria. We observed that DP and ACT learn reasonable trajectories, but small millimeter- level errors lead binary failures. For example, because success requires completing all five subtasks on Thread aaaadNeeeeee failing at the third stage still counts average reward exceeds In contrast, as depicted in Figure [3] both VITA and FM learn sufficiently precise control to complete the final subtask with fewer training steps, achieving high success rates on these tasks.
full failure, yielding low SRs when the
80
m
4
w
(a) Success Rate
60
(a) Reward
MM
40
2
20
VITA
DP
DP
0
40
60
70
80
90 100
10
70
80 90 100
Step (* 10 4)
Step (* 10 4)
Figure 13: Success rate and reward curves VITA and DP on ThreadNeedle.
As shown in Figure 14, DP can complete multiple subtasks yet fail the episode due to millimeter-level precision errors, such threading needle through very small opening. In contrast, we find that VITA and FM exhibit higher control precision on these tasks.
Action Precision. We examine why VITA and FM overall achieve higher action precision. As shown Table VITA outperforms all baselines on most tasks, and FM achieves SRs comparable to VITA. To better understand this, analyze the offline action MSE during training for VITA, FM, DP,
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Failure
Success
Figure 14: A failure and success case on ThreadNeedle, e. DP may complete most subtasks but still fail the final insertion due to millimeter-level errors.
and ACT in Figure [5| We observe that VITA and FM consistently converge to lower MSEs, whereas ACT plateaus substantially higher errors, and DP much more training steps converge. This trend aligns with prior findings (e.g., on image generation) showing that flow matching methods enjoy faster convergence (Lipman al. 2024 and can achieve higher generation fidelity (Gupta & Taiwade 2025).
PickBall
StoreDrawer
ToothBrush
VITA
VITA
Action MSE
ACT
ACT
ACT
10-1
10-1
10-5
10
20
10
20
10 Step (* 10"4)
20
Step (+ 10"4)
Step (* 10
Figure 15: Comparison of action MSE on three real-world single-arm ALOHA tasks.
B.9 AUTOENCODER Loss SELECTION
We utilize the L1 loss for the autoencoder loss, LAE. We found it outperforms the 1.2 loss, which is prone to mode-averaging and can result blurry action reconstruction.
B.10 SUCCESS RATE CURVES DURING VITA LEARNING
VITA exhibits efficient and stable learning AV-ALOHA and PushT (Figure 16) and Robomimic tasks (Figure 17
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thread needle
slot Insertion
pour test tube
6c
mmnw Why MM Ammhn
book package
Cabe transfeer
pusht
100
20
0.5
25
0.5
Stop 10144
stop 10744
5000 10
Figure Success rate curves of VITA training on six tasks (five AV-ALOHA PushT). The curves mean across three random seeds; the shaded region is =1 standard deviation.
Can
Square
100
100
80
80
Success Rate
Success Rate
40
40
10
15
20
25
15 Step 10*4)
10
20
25
Step
10^4)
Figure 17: Success rate curves of VITA training on two Robomimic tasks.
B.11 LATENT COLLAPSE
Our motivation for FLD stems from two key observations: first, the multi-stage pipeline inspired by latent diffusion utilizing pre-trained autoencoder is suboptimal for action data (Section B. 2 and second, joint training of the flow and target latent space leads to latent collapse (Section 5.3
Notably, very recent work Wang & Zhang 2025 resonates with these findings, and independently identified similar failure mode in latent diffusion for image generation. They observed that the diffusion objective Diffusion as Seff-Disiillation (DSD) Wang Zhang (0025) to stabilize latent representation learning. While FLD and DSD employ different mechanisms in distinct domains, both methods corroborate the necessity of end-to-end optimization while stabilizing latent representation learning.
undermine representation quality during end-to-end training, and proposed
C EXTENDED RELATED WORKS
Imitation Learning for Visuomotor Policy. Imitation learning enables robots to learn complex behaviors by mimicking expert demonstrations. Behavioral cloning, prominent imitation learning
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paradigm, frames this supervised learning problem, learning policy that maps observations to actions (Zhao et al. 2023] Lee et al. 2024). Recent advancements in behavioral cloning have widely
adopted generative modeling, which learns conditional distribution of actions given an observation. This category includes policies based on conditional variational autoencoders (CVAFs) (Zhao et al.)
22033 Lee et al. 22224) well diffusion (Dasari et al., 2024] Chi et al. 2023) and flow match- ing Zhang & Gienger 2024] Zhang et al., (025). Autoregressive methods, which tokenize actions and frame policy learning as a sequence modeling task, are also well-studied. These methods predict action tokens sequentially, using next-token prediction (Fu et al. 2024a), next-scale prediction (Gong all.|.2200 (002)). or bi-directional prediction (Su et al.) (025). Generative models ubiquitously require extra conditioning modules (e.g., Pross-attention (Dasari et al. 2024), AdaLN (Dasari et al. 2024), FiL.M (Perez et al. 2018 Chi et al. al. ) to inject observations at each step of the generation process. Furthermore, generative and autoregressive methods commonly employ large, expressive networks such as U-Nets or transformers to succeed on complex, high-dimensional robotics tasks. VITA re- duces this complexity by formulating the policy noise-free and conditioning- free vision-to-action flow.
Diffusion and Flow matching for Generative Modeling. Diffusion (Ho et al. 2020), grounded in stochastic differential equations (SDEs), generates complex data distributions by sampling from simple source distribution (typically Gaussian) and iteratively denoising
the target distribu- tion (Sohl-Dickstein et al.] 2015). Flow matching (Lipman et al. [2023] Liu et al. (002bb) has been
proposed enable faster training and sampling (Liu et al.) [022b Tong et al.] 2024] Esser al.)
Lipman et al. (202) by modeling the map between source and target distributions with an ordinary dif-
Terential equation (ODE). Both diffusion and flow matching models have shown strong performance
across diverse generative tasks, such as image generation (Rombach et al. 2022 Peebles & Xie
2023 Ma et al. 2024 Zhang al.] 2023] Liu et al.] [024bb [en et al. |l.||2..00 2024b). video generation (Ho) et al. 2022 Li et al. 2023). and visuomotor policies (Chi et al.3| Dasari et al., 2024 Black et a Liu et al. (024c). Unlike diffusion, flow matching theoretically places no constraints on the choice of source distribution (Tong et al. 2024), and a few works have explored leveraging this property to learn the direct transport within the same modality (Albergo & Sunden-Eijndee 2022 Tong et al. 2023b), e.g., for image-to-image generation tasks (Fischer et al. (003) Liu et al. (022a). Recently, Liu et al.
(2024a) and He et al. (2025) extended this to more challenging cross-modal generation between text and image. VITA focuses on learning to bridge vision and action for visuomotor control, where the target modality, action, has sparser data and lacks semantic structures, compared to text or images, presenting unique challenges. Different from flow matching for image generation, which typically pre- mre-trains and freezes the image autoencoder when learning flow matching or diffusion models for image generation (Rombach et al.) 2022] He et al., 2025 Liu et al., (024a). VITA resorts to a fully end-to-end pipeline training to effectively learn the latent action space from limited and sparse action data. Furthermore, to enable effective joint training of flow matching and target latent spaces, we propose flow latent decoding to backpropagate action reconstruction losses through the latent action generation process (ODE solving steps) during training. To the best of our knowledge, VITA is the first policy to jointly learn flow matching and a latent action space end-to-end.
D SIMULATED AND REAL- WORLD TASKS
To comprchensively evaluate the effectiveness of VITA across varying levels of difficulty and action dimensionality, we conduct extensive experiments on both single-arm and bimanual manipulation tasks. The action dimensionality spans from 2 to 21, and the tasks include both short- and long-horizon settings. Overall, AV-ALOHA tasks are particularly challenging due to their 21D action spaces, non-stationary observations introduced by the active-vision camera, and long- horizoo precision- demanding task structure (see Figure [8] for real-world examples). Single-arm ALOHA tasks are also challenging due to randomness, such as varying object types and object poses.
Following the practice of AV- ALOHA (Chuang et al.) 2024), we train all policies at 8.33 FPS (25/3) for simulated AV-ALOHA tasks and at FPS for real AV-ALOHA tasks, and interpolate to 25 FPS and 33 FPS, respectively, for inference.
The specifications for each dataset are shown in Table 6
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Dataset
State Dim
Action Dim
FPS
Image Size
Camera
AV-ALOHA (Sim)
21
21
25
240x320
eed_camnaa cam_left
AV-ALOHA (Real)
21
21
33
240x320
Left_eye__oo
overnead_cam,
ALOHA (Real)
7
33
240x320
ri right_wrist
Rohomimic
20
256x226
agentview_imee
PushTT
20
96x96
image bead cam
CloseBox
20
256x256
Table 6: Comparison of dataset specifications.
D.1 AV-ALOHA SIMULATION TASKS.
CubeTransfer: Pick up red cube with the right arm and transfer the left arm (200 episodes).
SlotInsertion: Use both arms to pick up a green stick and insert it into pink slot (100 episodes).
HcokPackage: Use both arms to pick a red box and hook it onto a blue wall-mounted hook (100 episodes).
PourTestTube: Pick up two test tubes and pour small red ball from one into the other (100 episodes).
ThreadNeedle: Pick up green needle and thread it through the hole of pink object (200 episodes).
D.2 ALOHA REAL TASKS
(a) HiddenPick
Reveal Box
Look Inside
Pick Up
Place
(b)
Look Inside
Pick Up
Transfer
Place
Figure 18: Autonomous rollouts VITA on two challenging real-world AV-ALOHA tasks, HiddenPico ck, and TransferFromBox.
D.2.1 BIMANUAL MANIPULATION WITH ACTIVE VISION
To evaluate the effectiveness VITA in real-world settings, we deploy the policy on two challenging bimanual manipulation tasks using AV-ALOHA (Chuang al. (024). Both tasks are mogghorizon and contain multiple stages to succeed. Examples of autonomous rollouts are shown in Figure [8] Both tasks require precise and coordinated control of three arms, including vision camera, and two arms for bimanual manipulation.
carrying active
Hi HiddenPicks ick: Lift and open fabric cover from box, then pick object from inside (50 episodes).
FransfexFromBox: Pick an object from a box with the right arm, transfer it to the left arm, and place it in another box (50 episodes).
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Table 7: SRs of VITA on two real-world bimanual manipulation tasks with active vision on AV- ALOHA. Each task is decomposed into three subtasks, and SRs are reported per subtask.
HiddenPick Pick
SransterFromer
Reveal
Pick
Place
Pick
Transfer
Place
VITA
1.00
0.65
0.65
1.00
0.95
D.2.2 SINGLE-ARM MANIPULATION TASKS
Each single-arm ALOHA task consists of multiple stages, and includes substantial environment randomization as detailed below.
PickBall: Pick up a ball, then place into the box (50 episodes). Both the ball and the target box appear in varying positions.
ToothBrush: Pick up the toothbrush from the side slot of the toothbrush cup. lift up. and place it into the toothbrush cup (50 episodes). The cup location and the orientation of the side slot are randomized.
StoreDrawer: Pick up the object, put it in the drawer, and close the drawer (100 episodes). We randomize the shapes and colors of the objects as well as the positions and partial openings of the drawers. We also evaluate the out-of-distributt success rates for unseen combinations of object colors and shapes (see Appendix
D.3 ROBOMIMIC TASKS
Robomimic a benchmark of single-arm imitation learning tasks) Mandlekar al. (202I). We adapt the environment for compatibility with the LeRobot (Cadene al. 2024) codebase. The robot state includes arm joint positions (encoded with sin and cos), joint velocities, end-effector pose, gripper finger positions, and gripper finger velocities. The action space consists of six values for delta position control of the end-effector pose and one value for the absolute position of the gripper.
Squares Pick up square nut and insert it onto matching square peg (175 episodes).
Can: Pick up red can and place it into a box (192 episodes).
D.4
OTHER TASKS
PushT: Push 2D T-shaped object into a matching T-shaped target region on the plane: achieving coverage score 0.95 is deemed success (200 episodes).
CloseBox: Close the lid of a paper box by manipulating the flap (200 episodes).
E
ADDITIONAL EXPERIMENT RESULTS
E.
ROBUSTNESS ONLINE PERTURBATIONS
As depicted in Figure 19 VITA demonstrates strong robustness online perturbations during
real-time control.
E.2 GENERALIZATION TO UNSEEN OBJECTS
As shown in Figure 20 we train the StoreDrawer task using 7 objects. Table that VITA achieves the highest success rate on these in-distribution objects. To further evaluate generalization, introduce four unseen test objects with novel geometries, such triangular prism and star- shaped block. On this out -fitt---istributiin (OOD) set, VITA succeeds picking all objects and storing them in the drawer.
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Figure 19: Robustness to online perturbations during inference on F PickBall. We manually move the ball multiple times before the pick, and move the box multiple times after the pick. both cases, the arm continues to adjust in real time and successfully reaches the correct ball and box positions.
Table 8: OOD SRs on four unseen objects for the StoreDrawer task.
VITA
DP
FM
ACT
4/4
4/4
3/4
2/4
E.3 ONE-STEP DENOISING
One-step generation has been widely explored in diffusion and flow matching models (Song et al. 2023: Zhang et al., 2025). While we employ OT-CFM (Tong et al., 2023a) with a 6-step Euler ODE solver in this work, VITA is compatible with other flow matchers, including one-step methods such as MeanFlow (Geng et al. 2025; Sheng et al. 2025). Integrating these methods enables further inference acceleration, albeit at the expense of generation quality. For instance, applying McanFlow to the PushT task yields approximately 2x faster inference, but reduces the success rate from 88% to 74%. Exploring these extreme efficiency improvements while mitigating action precision degradation remains a promising direction for future work.
F TRAINING
In each plot of Figure 21 we tune a single hyperparameter while holding all other hyperparameters at the default (see Table 9). and visualize the success rates training steps. With these experimental results, a robust configuration on Thr ThreadNeedle includes moderate weights for both AE and FLD (typically in (0.5, 1.0)), moderate FLC, minimal contrastive penalties, and KL regularization action and visual latents.
G IMPLEMENTATIONS
G.1 VITA
VITA encodes observations into latent vector 2g using ResNet- 18 backbone. The flow network. which learns the mapping from 2u to &1, is implemented solver. The latent action 21 is translated to action chunks by a lightweight MLP-based action decoder. summary of all hyperparameters and loss weights is provided in Table 9
MLP. We use 6-step Euler ODE
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(a) Objects used during Training
(b) Objects used during OOD Evaluation
Figure 20: OOD evaluation on StoreDrawer. Training uses in-distribution object-color combi-
nations (left). Evaluation uses four unseen objects with novel shapes (right), including a triangular prism and star-shaped block.
enc ric_recoo_vve wt
flow_recon pee_eecon_ve wt
enc_conme cnc_contra___ wt
80
6.
Success
40
0.01
0.5
0.5
1.0
0.0
0.001
2.0
5.0
0.2
step (+ 10" 4)
step ( 10"4)
step ( 10"4]
flow_comm Now_contra_rg wt
consistency_td wt
action kl_wt
80
60
Success
40
0.0
0.0
0.0001
0.0001
0.1
0.001
0.2
2.0
2
3
step (" 10"4)
step (" 10"4)
step (* 10"4)
Figure 21: Hyperparameter ablations on ThreadNeedle, Each sub-plot varies a single coefficient while holding all others their default values in Table Top: action encoder reconstruction weight, flow latent decoding weight, action encoder contrastive weight. Bottom: latent flow contrastive weight, latent flow consistency weight, action KL weight.
G.2 FLOW MATCHING POLICY
The FM policy learns velocity field using transformer backbone, with AdaLN for conditioning.. A 6-step Euler solver is used for solving ODEs. The hyperparameters and loss weights are summarized in Table I0
G.3 DIFFUSION POLICY
We follow the DP Chi et al. 2023) implementation in LeRobot (Cadene et al. 2024). The core the policy is U-Net noise predictor, which uses FiL.M (Perez et al. 2018 for conditioning. The model
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Table 9: VITA hyperparameters.
Horizons & Observation
Observation horizon
Prediction horizon
16
Action horizon
Observer backbone
resnet18
Observer tokenize
false
VITA core (latent & losses)
Latent dimension (Danes)
512
Decode flow latents
true
Consistency weight
1.0
Encoder contrastive weight
1e-4
Flow contrastive weight
0.0
Latent noise std
0.0
Flow matcher
/DD
Flow Matcher
OT-CEK
0.0
# sampling steps (Euler)
6
Action AE
AE recon loss type
11
Encoder recon weight
0.5
Flow (FLD) weight
0.5
variational (VAE)
false
KL weight (if variational)
1e-6
Freeze encoder / decoder Pretrained path
false / false
None
AE network (encoder/decoder)
Encoder type / hidden dim
/ 512
Decoder type hidden dim
simple / 512
Latent dim (AE)
Num heads MLP ratio Dropout Num layers
8 /
0.0
trained for 100 timesteps using a cosine beta schedule. During inference, trajectories are generated using 10-step DDPM sampler. All hyperparameters and loss weights are listed in Table []
G.4 ACTION CHUNKING TRANSFORMER
We follow the ACT Zhao et al. 2023 implementation LeRobot (Cadene et al. 2024 ACT is conditional variational autoencoder (c (AE) that generates action chunks conditioned on vision. Its architecture consists of a vision encoder for processing observations and a transformer-based decoder that models the distribution of future actions conditioned on the visual input. complete list of hyperparameters and loss weights is provided in Table [2
29

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Published as a conference paper at ICLR 2026
Table 10: Flow Matching (FM) Policy hyperparameters.
Horizons Observation
Observation horizon
Prediction horizon
16
Action horizon
8
Observer backbone
resnet18
Observer tokenize
fal se
Flow matcher / ODE
Flow Matcher
Target
0.0
sampling steps
6
Flow network architecture
Backbone
Flow_transforme
Conditioning
adaln
(options: adaln, cross, crosa_adaln)
Hidden dim
512
Num layers
4
Num heads
8
MLP ratio
4
Dropout
0.1
Table 11: Diffusion policy hyperparameters.
Horizons & Observation
Observation horizon
Prediction horizon
16
Action horizon
Observer backbone
resnet18
Observer tokenize
fal.s
Mask loss for padding
false
Diffusion scheduler
Type
DDPM
Training timesteps
100
Beta schedule
squaredcos_cap_tt
Beta start end
1e-4/2e-2
Prediction type
epsilon
Clip sample range
truc/1.0
U-Net architecture
Down dims
[512, 1024, 2048] or [256, 512, 1024]
Kernel size
Group norm groups
Diffusion step embed dim
128
FiLM scale modulation
true
Optimization
Adam LR / backbone scale
1e-4/0.1
Adam betas cps
(0.95, 0. 0999 / 1e-6
Weight decay
Scheduler
cosine, warmup 500 steps
Training & inference
Total training steps Inference steps
200000 DDPM 10
30

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Published as a conference paper at ICLR 2026
Table 12: Action Chunking Transformer (ACT): hyperparameters.
Sequence horizons
Action horizon
8
Prediction horizon
16
Observation horizon
1
Observer / backbone
Image encoder
ResNet-18
Tokenize
false
Transtormer (policy head)
Pre-norm
false
Model dimension dmodel
512
Attention heads
8
Feedforward dim
3200
FFN activation
ReLU
Encoder layers
4
Decoder layers
Dropout
0.1
Latent VAE block
Use VAE
true
Latent dim
32
VAE encoder layers
4
ACT KL. weight
10.0
Optimization
Optimizer
Adam
Learning rate
1 x 10-
Betas
(0.9, 0.999)
1 10-8
Weight decay Backbone LR scale
1 x 10-1
0.1
LR scheduler & validation
Scheduler
Cosine 2000
Warmup steps
Online validation frequency
2000 steps
31


Reviews:
[{"review_id": "KRKf5Cxqyk", "reviewer": "Reviewer_7Vvz", "summary": "This paper is about VITA, a noise-free and conditioning-free policy learning framework that attempts to directly map visual representations to latent actions using flow matching. The authors introduce an action autoencoder to align the dimensionalities of the vision and action spaces and propose FLD to stabilize the joint training by backpropagating the reconstruction loss through the ODE solving steps.", "questions": "- Regarding the tasks where VITA underperforms FM, what is the hypothesis for this performance drop? Does the reliance on the globally pooled visual feature bottleneck performance on these precision tasks?\n\n- What is the actual overhead in training time and memory usage when enabling FLD?\n\n- Why did Diffusion Policy fail so completely on ThreadNeedle and HookPackage despite extended training? Were the hyperparameters for the baselines thoroughly tuned?\n\n- In Section 4.3, paper mentions that a pre-trained and frozen action AE is ineffective. Could you provide the empirical results for this ablation?\n\n- Have you experimented with representations that retain more spatial information instead of the global average-pooled vector? If so, would the MLP architecture still be sufficient?\n\n- How does VITA handle multimodal action distributions? In conventional diffusion/FM, stochasticity comes from the initial noise sampling. In VITA, the process seems largely deterministic once the image latent z0 is fixed. How is multimodality captured?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "- I like the core idea of using the visual latent space as the source distribution for the flow matching process. Removing the explicit conditioning mechanisms (like cross-attention or FiLM) that are standard in diffusion/flow policies simplifies the architecture and naturally leads to faster inference.\n\n- The resulting architecture is lightweight. It is compelling that an MLP-only network for the flow matching and decoding components (following the ResNet encoder) can handle complex bimanual tasks like those in the ALOHA suite.", "weaknesses": "- I find the claim of being \"conditioning-free\" (L014) slightly misleading. While explicit conditioning modules are removed, the flow is inherently conditioned on the visual input because the visual latent is the source distribution (z0). The velocity field must learn the transport from this specific starting point. This feels more like implicit conditioning via the ODE initial state rather than a fundamental removal of conditioning.\n\n- The approach seems heavily constrained by the architecture. The MLP-only implementation relies on a global average-pooled ResNet-18 feature vector. This severely compresses spatial information and likely bottlenecks the policy's ability to handle tasks requiring fine-grained spatial understanding. It is unclear how this approach would scale if we switched to representations that retain spatial information (e.g., spatial tokens), which often require more complex backbones like U-Nets or Transformers, potentially nullifying the efficiency gains.\n\n- The experimental results are mixed and the baseline comparisons are questionable. VITA underperforms conventional FM on 'Slot Insertion' (0.76 vs 0.83) and 'PourTest Tube' (0.79 vs 0.84). Furthermore, the baseline performance is suspiciously low in some cases (e.g., DP achieving 0.00 on ThreadNeedle), even though the authors mention increasing training steps for DP and ACT significantly. This makes it difficult to gauge the true strength of VITA.\n\n- The necessity of FLD introduces a significant hidden cost. FLD requires backpropagation through the ODE solver steps during training. This substantially increases the computational graph complexity and memory requirements during training. The paper focuses heavily on inference speed, but the practical training overhead of FLD is not discussed or quantified.\n\n- The efficiency gains presented in Table 3 mix architectures and conditioning methods. VITA (MLP) is compared against FM implemented with DiT or U-Net. A fairer comparison would be conventional FM using the same MLP architecture and a simple conditioning mechanism to isolate the benefit of the noise-free approach from the architecture choice.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uu9Wpt8E6p", "reviewer": "Reviewer_od34", "summary": "This paper introduces a framework that maps visual observations directly into latent action spaces through a flow-matching approach, avoiding traditional denoising or conditioning pipelines. By integrating an action autoencoder with a latent flow decoder, the proposed method (VITA) aligns vision and action representations and improves training stability. Implemented with lightweight MLP networks, it achieves comparable results to diffusion-based and flow-based policies while delivering faster inference across simulated and real-world control benchmarks.", "questions": "(1) Have the authors evaluated whether the choice of an MLP backbone influences the observed improvements? Specifically, if the same MLP architecture were used for the baseline methods, would their inference times and performance still trail behind VITA? How much of the reported speed-up is due to the backbone rather than the proposed flow-matching formulation itself?\n\n(2) Have the authors evaluated whether the choice of an MLP backbone influences the observed improvements? Specifically, if the same MLP architecture were used for the baseline methods, would their inference times and performance still trail behind VITA? How much of the reported speed-up is due to the backbone rather than the proposed flow-matching formulation itself?\n\n(3) Why do the sets of baselines differ between experiments (e.g., Table 1 vs. Table 3)? What was the rationale for omitting certain conditioning-based approaches such as AdaLN, FiLM, which seem directly relevant to the claimed simplifications? Are the results expected to hold under a unified baseline setup, or were baselines adjusted for computational or implementation reasons?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The idea of simplifying visuomotor policy learning by removing conditioning mechanisms is potentially interesting. The paper is generally clear in presentation, and experiments are competently executed. The implementation details are sufficiently documented, and the inclusion of ablation studies is appreciated.", "weaknesses": "The main conceptual motivation is underdeveloped. The authors state that removing conditioning simplifies the process, yet the argument remains superficial. It is not clear why direct flow from vision to actions should be advantageous, or what specific drawbacks the previous conditioning-based methods introduce. Without a stronger analysis, the contribution feels somewhat incremental.\n\nThe use of MLP backbones further complicates interpretation of results. Introducing such a lightweight architecture changes the comparison dynamics, and without explicit ablations, it’s unclear whether performance and efficiency gains originate from the proposed framework or simply from the reduced model complexity. This limits the credibility of the reported advantages.\n\nOverall, while the approach is clean and runs efficiently, it does not convincingly establish the necessity or distinctiveness of the proposed design choices.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jzIfQPiSDI", "reviewer": "Reviewer_cek7", "summary": "Paper introduces a noise-free, conditioning-free framework that directly maps visual inputs to latent actions using flow matching, eliminating complex denoising and conditioning steps. By combining an action autoencoder with a flow latent decoding mechanism, VITA bridges vision–action dimensional gaps and stabilizes training. Implemented with simple MLPs, it achieves state-of-the-art performance and 1.5×–2.3× faster inference on simulated and real-world robotic tasks compared to conventional diffusion and flow-based policies.", "questions": "1. Can authors provide std or (better) confidence intervals for Table 3? It should be possible, given that you used more than one random seed.\n2. Why set of baselines differ between experiments (Table 1 & Table 3)? Won't the results differ from other conditioning methods, e.g. AdaLN, cross-attention, FiLM? \n3. I think this line needs clarifications: “we use vector representations for both vision and action, further reducing time and space overhead”. To my knowledge, most existing methods represents observations as vectors… What is the difference specifically and why it reduces time overhead?\n4. Can baselines be trained with MLP backbones? Would they be faster or still slower than the proposed VITA?\n\nMisc:\n\ntypo on 81 line “collapse of targe latent action”", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Paper proposes novel solution to the timely and important problem, which seems to be quite significant and useful to the researchers and practitioners. \n\n- Paper itself is cleanly written, explaining the motivation behind the method ingredients. All claims are supported by evidence and additional ablations are provided to further support the proposed contributions. VITA performs on par with the baselines, while being much faster on inference, which is essential for real world applications. Paper provides sufficient information for reproducibility, including hyperparameters. \n\n- Overall, I do not have major concerns regarding the proposed method and think that results are significant.", "weaknesses": "I think the paper has two weaknesses. \n\nFirstly, it seems to me that the motivation is not sufficiently explained. After all, why is it important to flow directly from images into actions, rather than from noise with visual conditioning? Why does it introduces complexity? Where does the additional overhead come from? Given that this is not analysed further in the paper, I believe that a simple citation (e.g. line 55) of prior work is insufficient. I advise the authors to elaborate on their reasoning in greater detail.  \n\nSecond, It seems to me that the introduction of MLP-backbone is rather sudden and not particularly justified, in the sense that it is an additional confounder to the main method, which invalidates a fair comparison of approaches specifically for predicting actions. And there are no ablations on that. What if the main speed up (or even performance gains) come from the simpler backbone? How would baselines perform with similar backbones? If baselines will fail but VITA is not, what is the main reason?\n\nI can not answer this questions from the current paper results and this is critical.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "59jPfp7vv1", "reviewer": "Reviewer_2nF7", "summary": "This paper introduces Vision-to-Action Flow Matching Policy (VITA), which maps visual representations directly to latent actions using flow matching. Their approach is noise-free and conditioning-free, proposed to reduce time and space complexity. They use an action autoencoder to up-sample action representations to have the same high dimensionality as visual representations. Their approach is simple and relatively novel, achieving state-of-the-art results while being faster.", "questions": "1. I think the paper would be improved by a little more elaboration on the advantages of being noise-free and conditioning-free, and why others have not got this to work successfully in the past.\n2. If VITA used a Transformer/DiT instead of an MLP at roughly the same number of parameters, what would be its latency? (referencing Table 3)\n3. Does Table 3 latency include the visual conditioning time?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper's approach is simple and relatively novel in performing flow matching directly from visual representations to action latents. \n2. VITA matches state-of-the-art generative policies\n3. VITA has a faster inference speed relative to other methods, in part due to its simplicity.\n4. The method diagrams they use are simple and understandable (Figures 1 and 2). Their approach is well-motivated and their ablation of FLD and FLC is informative.", "weaknesses": "1. The evaluations seem relatively sparse, both in terms of the number of baselines compared and the tasks. For example, how well does FM (or one of the other policies) do on the real-world tasks?\n2. The paper alluded to VITA having lower memory footprint than other methods, especially those that use visual conditioning. Can you quantify/estimate this comparison?\n3. (minor) Citation formatting issues. For example, many cited papers lack parentheses in the first paragraph of the introduction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)