Title: ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation
Venue: NeurIPS 2025
Accepted: yes
Keywords: VLA

Robotics

Contact-rich Manipulation

Robotics and Control
Abstract: Vision-Language-Action (VLA) models have advanced general-purpose robotic manipulation by leveraging pretrained visual and linguistic representations. However, they struggle with contact-rich tasks that require fine-grained control involving force, especially under visual occlusion or dynamic uncertainty. To address these limitations, we propose \textbf{ForceVLA}, a novel end-to-end manipulation framework that treats external force sensing as a first-class modality within VLA systems. ForceVLA introduces \textbf{FVLMoE}, a force-aware Mixture-of-Experts fusion module that dynamically integrates pretrained visual-language embeddings with real-time 6-axis force feedback during action decoding. This enables context-aware routing across modality-specific experts, enhancing the robot's ability to adapt to subtle contact dynamics. We also introduce \textbf{ForceVLA-Data}, a new dataset comprising synchronized vision, proprioception, and force-torque signals across five contact-rich manipulation tasks. ForceVLA improves average task success by 23.2\% over strong $\pi_0$-based baselines, achieving up to 80\% success in tasks such as plug insertion. Our approach highlights the importance of multimodal integration for dexterous manipulation and sets a new benchmark for physically intelligent robotic control. Code and data will be released at https://sites.google.com/view/forcevla2025/.

ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation

Abstract

Vision-Language-Action (VLA) models have advanced general-purpose robotic manipulation by leveraging pretrained visual and linguistic representations. However, they struggle with contact-rich tasks that require fine-grained control involving force, especially under visual occlusion or dynamic uncertainty. To address these limitations, we propose ForceVLA , a novel end-to-end manipulation framework that treats external force sensing as a first-class modality within VLA systems. ForceVLA introduces FVLMoE , a force-aware Mixture-of-Experts fusion module that dynamically integrates pretrained visual-language embeddings with real-time 6-axis force feedback during action decoding. This enables context-aware routing across modality-specific experts, enhancing the robot's ability to adapt to subtle contact dynamics. We also introduce ForceVLA-Data , a new dataset comprising synchronized vision, proprioception, and force-torque signals across five contactrich manipulation tasks. ForceVLA improves average task success by 23.2% over strong π 0 -based baselines, achieving up to 80% success in tasks such as plug insertion. Our approach highlights the importance of multimodal integration for dexterous manipulation and sets a new benchmark for physically intelligent robotic control. Code and data will be released at website.

1 Introduction

Robotic learning has advanced rapidly with the rise of embodied AI, driven by large-scale manipulation datasets and the emergence of foundation models [1, 2, 3]. These models exhibit strong adaptability, enabling rapid generalization to novel tasks with minimal supervision [4, 5, 6]. In parallel, Vision-Language Models (VLMs) have achieved remarkable generalization through largescale multimodal alignment [7, 8], learning transferable representations that support a wide range of downstream tasks.

Building on this progress, OpenVLA [9] introduced Vision-Language-Action (VLA) models to bridge perception and control for real-world robotic manipulation. By leveraging VLM-based encoders, these models demonstrate strong performance in semantic grounding, language following, and zeroshot generalization. π 0 [10] further enhances this framework using stronger VLM backbones [11] and flow-based action generation, showing that pretrained multimodal VLA models can acquire robust physical-world priors and can be fine-tuned efficiently with only a few demonstrations.

However, contact-rich manipulation demands more than semantic grounding and spatial planning-it is fundamentally driven by interaction forces [12, 13]. Existing VLA models rely heavily on visual and linguistic cues, often overlooking force sensing, a modality critical for precise physical interaction. In contrast, humans naturally integrate tactile and proprioceptive feedback to adapt their manipulation

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strategies [14]. As a result, VLA models frequently struggle with tasks such as insertion, tool use, or assembly-especially under occlusion or poor visual conditions-leading to brittle behavior or task failure. Moreover, force requirements evolve across different task phases: delicate grasping, controlled insertion, and compliant surface contact-each requiring distinct forms of force modulation. Current methods lack mechanisms to perceive and adapt to these dynamic variations, limiting their ability to reason over time about physical interactions.

To address these limitations, we introduce ForceVLA , a novel framework that augments VLA models with a force-aware Mixture-of-Experts (MoE) module, enabling effective reasoning and context-sensitive, force-informed action generation in contact-rich manipulation tasks, as illustrated in Figure 1. ForceVLA is grounded in the key insight that 6D external force sensed at the robot's end-effector, should be treated as a first-class modality and formally integrated into the action expert module to enable phase-aware action generation based on force feedback during physical interaction. To realize this integration, ForceVLA incorporates a force-aware MoE module, named FVLMoE , designed to perform modality- and phase-aware fusion of visual-linguistic representations with real-time force feedback from embodied interaction during action planning. Through a gating mechanism, FVLMoE computes dynamic routing weights over expert subnetworks, each specialized for different modalities across task execution phases. By adaptively activating these experts based on high-level task instructions and low-level interaction feedback, ForceVLA captures subtle yet critical, phase-dependent variations during physical interaction and generates precise, phase-aligned, and force-aware action chunking. To facilitate research in force-aware manipulation, we introduce ForceVLA-Data , a carefully curated dataset comprising synchronized vision, proprioception, and 6-axis force-torque signals collected across five representative contact-rich manipulation tasks. This dataset addresses the scarcity of publicly available force-annotated robotic manipulation data and provides a valuable resource for training and evaluating multi-modal robotic learning systems. Our main contributions are:

We present a novel framework that integrates force, vision, language, and action for improved precision and stability on contact-rich manipulation tasks. Key to our approach is a force-aware Mixture-of-Experts-based fusion module, which enables dynamic processing and deep integration of force, visual, and language features during action generation, significantly enhancing physical interaction capabilities in VLA systems.

We introduce a new dataset featuring synchronized vision, proprioception, and force-torque signals across five contact-rich manipulation tasks. We also provide a complete data collection

pipeline-including teleoperation tools and data converters-and commit to open-sourcing all resources to advance research in force-aware robotic manipulation.

Through experiments on five challenging tasks, ForceVLA achieves up to 80% task success and improves average performance by 23.2% over baselines, demonstrating strong generalization to novel objects, occlusions, and physical perturbations.

2 Related Works

Robotic VLA domain. Recent research in Vision-Language-Action (VLA) models has focused on leveraging large-scale multimodal pretraining to generalize robotic policies across tasks and embodiments [4, 6, 9, 15, 16, 17, 18, 19, 20]. These models typically map visual and language inputs to low-level control signals via end-to-end learning. Flow-based architectures such as π 0 [10, 21] integrate pretrained vision-language encoders with fast action decoders to achieve high-frequency outputs. Other works incorporate reasoning mechanisms [22, 23, 24, 25], action space compression, or 3D point cloud inputs [26] to improve instruction grounding and task execution. Diffusion-based models [5, 27, 28, 29, 30] introduce stochastic generation for diverse, long-horizon behaviors, though they often incur high training and inference costs. Despite these advances, most VLA approaches remain limited to vision and language inputs, making them less effective in contact-rich or occluded manipulation scenarios where tactile feedback is critical.

Contact-rich manipulation domain. Traditional vision-only methods struggle with dynamic interactions requiring fine-grained feedback. To address this, recent works integrate force sensing [31, 32, 33, 34], enabling improved motion stability and accuracy. Xie et al. [35, 13] provide foundational studies on the role of force feedback in robotic control. Tactile sensing has also emerged as a powerful modality: TLA [36] and Tac-Man [37] demonstrate enhanced performance in fine manipulation and articulation tasks. VTLA [38] explores sim-to-real transfer using fingertip tactile sensors with a new simulation dataset. In contrast to VTLA's simulation-first approach, our work adopts a real-world-first paradigm with 6D force-torque sensing and introduces a novel MoE architecture for online force modulation rather than focusing on sim-to-real transfer. Multimodal fusion methods [39, 40] show promise in complex environments. FuSe [41] proposes a finetuning recipe to adapt existing generalist models to incorporate new modalities. While FuSe focuses on adaptation strategies, our contribution lies in designing a complete end-to-end policy architecture with deep integration of force feedback through an MoE-based routing mechanism. However, current approaches are often limited to static modality fusion and lack dynamic routing or unified modeling frameworks. Furthermore, few evaluate cross-task generalization in real-world contact-rich settings.

Contact-rich manipulation datasets. Large-scale datasets have been instrumental in advancing robotic learning. RH20T [42] provides a valuable collection of diverse manipulation demonstrations, offering raw data with basic reading tools. Factory [43] introduces a powerful simulation toolkit for contact-rich interactions at scale, providing virtual assets and controllers for policy training in simulated environments. IndustReal [44] demonstrates an approach that explicitly avoids force-torque sensors, instead relying on alternative cues to manage contact during manipulation. While these pioneering efforts have advanced the field in task diversity, simulation capabilities, and sensor-free approaches, they have limitations that our work addresses. Specifically, existing datasets often lack out-of-the-box usability for modern training frameworks, rely on simulation that introduces a sim-toreal gap, or deliberately exclude explicit force sensing. In contrast, our ForceVLA-Data provides three unique contributions: (1) pre-processed data in the SOTA Lerobot format with conversion tools for plug-and-play compatibility, significantly reducing data-wrangling overhead; (2) a realityfirst methodology with data collected entirely in the real world, bypassing the sim-to-real gap; and (3) deep integration of high-resolution 6D force-torque sensor data, positioning our dataset as an essential resource for developing and benchmarking policies that explicitly leverage force feedback for dexterous manipulation.

MoE architecture-related work. Mixture-of-Experts (MoE) architectures improve model scalability and efficiency by activating sparse expert subnetworks [45, 46, 47, 48, 49]. Follow-up work [50] improves MoE training stability and task transferability. In the multimodal domain, LIMOE [51] integrates sparse expert layers for joint vision-language learning. Recent applications in robotics [52, 29] adopt MoE layers within VLA models to enhance policy generalization and adaptability. However, these methods largely omit explicit modeling of the force/tactile modalities, and lack mechanisms for dynamically routing across multimodal signals in contact-intensive tasks.

3 Preliminary

Problem Formulation. Figure 2 shows the setting of robot manipulation tasks. The robot's observation at timestep t consists of base and hand visual inputs V b t and V h t , the proprioceptive state s t ∈ R 7 , and a single-timestep external force-torque reading f t ∈ R 6 , which are collectively denoted as O t = { V b t , V h t , s t , f t } . Given a language instruction L , the objective is to learn an end-to-end policy π ( A t | O t , L ) that outputs low-level, executable action chunk A t = { a t , a t +1 , ..., a t + H -1 } [10] maximizing the likelihood of completing the contact-rich task, where s t is a vector of TCP pose concatenated with gripper width. TCP position is represented by Cartesian coordinates ( x, y, z ) and orientation is represented by Euler angles ( α, β, γ ) . f t is the estimated external wrench applied on TCP at timestep t and expressed in world frame, which consists of R 3 force and R 3 moment: f t = { f tx , f ty , f tz , m tx , m ty , m tz } .

Experts (MoE)[49, 47] as our fusion layer. The core idea is to distribute different modalities to a larger set of smaller, specialized 'expert' subnetworks, only a fraction of which are activated for any given input token. An MoE layer typically comprises a set of N expert networks , denoted as { E i } N i =1 and a gating network (also referred to as a router), denoted as G . This network takes an input token x and dynamically determines which of the N experts should process it. In prevalent sparse MoE implementations, for an input token x , the gating network G ( x ) produces scores or logits that are used to select a small subset of k experts (typically k = 1 or k = 2 , where k ≪ N ) from the total pool of N experts. The input token x is then routed only to these k active experts. The outputs of these active experts, E i ( x ) , are subsequently aggregated, commonly through a weighted sum where the weights g i ( x ) are also derived from the gating network. The final output y ( x ) of the MoE layer can be expressed as: y ( x ) = ∑ i ∈ TopK ( G ( x )) g i ( x ) E i ( x ) , where TopK ( G ( x )) denotes the set of indices of the topk experts selected by the gating network for input x .

4 ForceVLA

4.1 Overview of ForceVLA

ForceVLA is an end-to-end multimodal robotic policy designed for contact-rich manipulation. Its pipeline is illustrated in Figure 3. Building upon the π 0 framework [10], it integrates vision, language, proprioception, and 6-axis force feedback to generate actions through a conditional flow matching model [53, 54]. Visual inputs from multiple RGB cameras and task instructions are encoded by a SigLIP-based [55] vision-language model (based on PaliGemma [11]) into contextual embeddings. These embeddings, combined with proprioceptive and force cues, condition an iterative denoising process that predicts the action trajectory.

FVLMoE is the core module enabling effective force integration. Force readings are linearly projected into dedicated tokens and fused with vision-language embeddings via a Mixture-of-Experts (MoE) module. Inspired by MoE's strength in multi-task and modality-specific learning [56, 51], FVLMoE adaptively routes and processes multimodal inputs. Its output serves as a rich guidance signal for the flow model, allowing ForceVLA to handle subtle contact dynamics and visually ambiguous scenarios with greater precision and robustness.

4.2 FVLMoE Architecture

The FVLMoE module is specifically designed for the fusion of multimodal sensory information. Its design enables the model to integrate rich contextual understanding from vision and language with the immediate, fine-grained dynamics captured by force-torque sensors. This fusion is critical for enabling robust and adaptive behavior in contact-rich manipulation tasks. The architecture and operation of the FVLMoE can be detailed in the following stages:

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Input Mapping for Multiple Modalities. How to determine the optimal stage and method for a novel force modality incorporation poses a significant design challenge. Following extensive experimentation, we established an approach where the force modality is introduced after the primary VLM has processed visual and linguistic inputs. Specifically, force features are fed as distinct inputs into the FVLMoE module, positioning it to function akin to a higher-level cortical association area responsible for integrating the VLM's pre-trained visual-linguistic representations with the newly introduced force tokens. This strategy contrasts with introducing force prior to, or concurrently with, the VLM's initial fusion of visual and language modalities. The empirical justification for this architectural decision is elaborated in the Ablation Studies section (Section 5.4).

The FVLMoE module, in line with this design choice, ingests a sequence of token embeddings E in formed by the concatenation of visual-linguistic features and a dedicated force token. The VL features, denoted as E V L ∈ R N V L × D model , are outputs from the primary Vision-Language Model, encapsulating contextual understanding derived from processed image streams and textual instructions. Concurrently, the raw 6-axis force-torque sensor data, f raw ∈ R 6 , is transformed by a linear projection ϕ F into a force token embedding E F = ϕ F ( f raw ) ∈ R D model . The final input to the FVLMoE is thus the concatenated sequence E in = [ E V L ; E F ] ∈ R ( N V L +1) × D model , where the force token is appended to the visual-linguistic context for subsequent joint processing within the MoE architecture.

Multimodal Routing and Fusion Computation. Once the combined multimodal sequence E in ∈ R ( N V L +1) × D model is formed, it undergoes hierarchical processing within the FVLMoE module. E in is passed through an encoder layer for shared refinement to facilitate holistic interaction among all constituent force, visual, and language tokens. This layer is composed of a multi-head selfattention mechanism with N heads attention heads and a subsequent FFN, yielding E enc ∈ R D model . Subsequently, E enc is channeled into a sparse Mixture-of-Experts layer. This layer employs E = 4 distinct expert networks, each realized as an independent MLP. A dynamic gating network determines the routing, selecting the most appropriate single expert (top k = 1 ) for each token in E enc based on learned dispatch weights. The output from the MoE computation is then integrated back with the input to the MoE layer via a residual connection, yielding E fused . The resulting sequence of fused multimodal features is passed through a final linear projection layer to match the dimensionality of the action expert.

Injecting Fused Features into the Action Flow Head. The sequence of fused multimodal features produced by the FVLMoE module serves as a guidance signal for the action generation process, which is formulated as a flow-based denoising model. This guidance is materialized by first extracting a specific sub-sequence, G FVLMoE ∈ R H action × D a , comprising the final H action tokens from E FVLMoE; these tokens encapsulate the most pertinent fused guidance for each step in the H action-length action plan. G FVLMoE is then combined via element-wise addition with S suffix ∈ R H action × D a obtained from the primary VLM's processing of the current proprioceptive robot state s t ∈ R D s and the noisy

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action trajectory a τ t ∈ R H action × D a at denoising step τ , where D s and D a are the dimensionalities of the state and action spaces, respectively. This additive injection mechanism ensures that the rich, contact-aware contextual understanding developed by the FVLMoE directly modulates and refines the generated action sequence at each step of the predicted trajectory.

4.3 Datasets

To train ForceVLA, we curated a new dataset specifically focused on contact-rich manipulation tasks, emphasizing the synchronized capture of visual, proprioceptive, and force-torque data. Existing datasets often lack the comprehensive force interactions or the diversity of contact-driven scenarios necessary to develop robust force-aware policies.

Our data collection was performed using a Flexiv Rizon 7-DOF robotic arm equipped with a Dahuan adaptive gripper. Visual data was captured from two RGB-D cameras: one static third-person view (RealSense D435 at 1280x720, 30 FPS) and one wrist-mounted camera (RealSense D415 at 640x480, 30 FPS) providing egocentric perspectives. Data was collected via human teleoperation using a Quest3 VR interface with custom mappings to robot end-effector control. Five expert operators performed a total of 5 distinct contact-rich tasks: bottle pumping, plug insertion, USB drive insertion, whiteboard wiping, and cucumber peeling , as described in Section 5.1. For each task, operators were instructed to complete the objective while ensuring diverse and successful interaction patterns. We varied object positions and orientations across demonstrations to enhance data diversity.

The resulting dataset, which we term ForceVLA-Data , comprises a total of 244 trajectories, amounting to 140 thousand synchronized timesteps. All sensor streams were synchronized based on timestamps. Images were resized to 480x640 pixels and normalized. Actions were represented as target TCP pose and gripper width. The ForceVLA-Data dataset, along with our data collection code and processing scripts, will be made publicly available at website to facilitate further research in learning force-aware manipulation policies.

5 Experiments

This section presents a comprehensive suite of real-world contact-rich manipulation experiments and analytical studies to empirically validate the ForceVLA model. The evaluation is structured around four core research questions: (1) the overall effectiveness of ForceVLA compared to baselines that incorporate force without our specialized fusion mechanism; (2) the model's ability to generalize across unseen object instances, environmental variations, and task conditions; (3) the efficacy of the proposed FVLMoE architecture in achieving optimal multimodal fusion for contact-rich manipulation; and (4) the ability of the Mixture-of-Experts module to appropriately process heterogeneous input modalities and learn meaningful routing behaviors across expert networks.

5.1 Experimental Setups

To evaluate the effectiveness of ForceVLA, we conducted experiments on five diverse contact-rich manipulation tasks: Bottle Pumping, Plug Insertion, USB Drive Insertion, Whiteboard Wiping, and Cucumber Peeling, as in Figure 4. These tasks were chosen to assess fine-grained control, adaptability to varied initial conditions, and the utility of multimodal feedback, particularly force sensing. Each task introduces unique physical challenges: Bottle Pumping requires precise vertical pressing; Plug

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and USB Drive Insertions involve accurate alignment and force-controlled insertion; Whiteboard Wiping demands smooth trajectory control and surface contact; and Cucumber Peeling tests the ability to apply and maintain controlled force during continuous surface interaction.

We trained ForceVLA using approximately 50 expert demonstrations per task. Evaluation was conducted over 20 trials each for the insertion and pumping tasks, 10 trials for the more timeconsuming whiteboard task, and 15 trials for the cucumber peeling task, each involving 15 peeling strokes. Success was defined by task-specific criteria such as complete insertion, effective wiping motion, or substantial cumulative peel coverage. These tasks were designed to rigorously probe ForceVLA's capacity to model and control complex, uncertain dynamics through the integration of vision, language, and force modalities.

Evaluation Metrics and Baselines. Model performance is primarily evaluated using the task success rate across all five challenging contact-rich manipulation tasks. For specific task like cucumber peeling, average peel length and minimum peeling times are also reported to provide a more nuanced assessment. To contextualize the performance of our proposed ForceVLA model, we compare it against several carefully selected baselines derived from the state-of-the-art π 0 [10] architecture, which serves as our foundational model. The specific variants include π 0 -base[10] w/o F (standard π 0 without force input), π 0 -base[10] w/ F ( π 0 with force signals directly concatenated to state inputs), and corresponding π 0 -fast[25] configurations ( w/o F and w/ F), representing potentially faster alternatives. The selection of π 0 -base[10] allows comparison with a strong existing VLA method, while the 'inputForce' variants are crucial for demonstrating the efficacy of our FVLMoE fusion strategy over simpler force integration approaches.

5.2 Main Results

Overall Performance. As demonstrated in Figure 5, ForceVLA achieves an average success rate of 60.5% across all five tasks, significantly outperforming all baseline configurations. Compared to the standard π 0 -base model without force feedback ( π 0 -base w/ F), which achieved an average of 37.3%, ForceVLA shows an improvement of 23.2%. This highlights the substantial benefit of

incorporating and effectively processing multimodal information, including force. Table 1 further highlights ForceVLA's superior performance on the intricate cucumber peeling task. Our model excelled on both key metrics: it achieved the longest average peel length per stroke (14.12 cm ↑ ), indicating better ability to execute high-fidelity surface manipulation through stable tool orientation, adaptive contouring, and sustained surface contact compared to both π 0 -base w/ F (13.17 cm) and π 0 -base w/o F (10.27 cm). Concurrently, ForceVLA demonstrated superior overall efficiency by requiring the minimum number of strokes (7 ↓ ) to achieve a substantially peeled cucumber, significantly fewer than the 10 and 14 strokes needed by π 0 -base w/ F and π 0 -base w/o F, respectively. These combined results underscore ForceVLA's proficiency in maintaining consistent, effective tool-surface

interaction and executing efficient, goal-directed motions in tasks demanding continuous and precise force modulation.

Effectiveness of Force Fusion via FVLMoE. Introducing raw force signals into π 0 -base boosts performance from 37.3% to 40.2%, confirming the utility of force feedback. However, ForceVLA surpasses both with 60.5%, indicating that effective fusion-enabled by our FVLMoE module-is essential for fully leveraging tactile information. This demonstrates that beyond the presence of force data, how it is integrated is critical to performance gains.

Selection of π 0 -base and π 0 -fast model. For our foundational baseline, we evaluated π 0 -base and π 0 -fast variants. The π 0 -base architecture demonstrated superior overall performance: π 0 -base w/ F (40.2%) and π 0 -base w/o F (37.3%) significantly outperformed π 0 -fast w/ F (14.2%) and π 0 -fast w/o F (31.0%). While π 0 -fast variants exhibited a comparative advantage solely on the whiteboard wiping task-potentially due to a simpler action generation mechanism being more attuned to such motions, the π 0 -fast architecture's performance notably degraded (from 31.0% to 14.2%) when raw force input was directly added. We attribute this sensitivity to its highly optimized and compact token space, which is likely disrupted by naively projected force tokens lacking corresponding large-scale pre-training. Conversely, π 0 -base modestly benefited from direct force input, with its larger representational capacity presumably allowing for partial utilization of these new sensory signals. Given its superior aggregate performance and more robust handling of naive force integration, π 0 -base was selected as the primary baseline for developing and evaluating ForceVLA.

5.3 Model Generalization

To evaluate ForceVLA's generalization capabilities, we designed five experimental settings with increasing task variability and physical uncertainty, as illustrated in Figure 6. These settings include: (1) Object Gen. 1 , which varies the bottle type in the bottle pumping task; (2) Object Gen. 2 , which changes the plug type in the plug insertion task; (3) Height Gen. , which adjusts the initial bottle height and measures success under torque limits; (4) Visual Occlusion , where parts of the plug and socket are obscured; and (5) Unstable Socket , introducing physical instability via clutter beneath the socket. These variations test both perceptual robustness and physical adaptability, with results summarized in Table 2.

Across all settings, ForceVLA exhibited superior generalization, particularly in scenarios requiring fine physical interaction. In Object Gen. 1, it achieved an 80.00% success rate, outperforming baselines that lacked force input or processed it naively. In the Height Gen. setting, ForceVLA effectively scaled its interaction forces to variable depths, avoiding torque limit violations seen in other models. Furthermore, ForceVLA maintained high success under visual degradation (90.00% in Visual Occlusion), reflecting its reliance on multimodal feedback beyond visual cues. Notably, while π 0 -fast w/f performed best in the Unstable Socket task, this advantage stems from its frequent pausing behavior-when the end-effector pauses while in contact with the plug, the socket is less likely to wobble, effectively reducing the task difficulty. However, this stuttering limits its inference speed and reactiveness to real-time force changes, making it less competitive for general contact-rich manipulation tasks requiring prompt trajectory adjustments. These results underscore the critical role of the proposed FVLMoE architecture in intelligently integrating force information-not just for sensing contact, but for modulating action in response to dynamic physical conditions-enabling more versatile and robust robotic manipulation.

5.4 Ablation Studies

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To validate the architectural design of ForceVLA , particularly the integration of force feedback, we conducted comprehensive ablation studies shown in Table 3. We compared early, late, and our proposed fusion strategy. Early fusion methods, such as 'linear before VLM' and 'MoE before VLM,' which inject force data prior to the visual-language model (VLM), significantly degraded performance. Notably, the MoE-based early fusion failed entirely (0% success rate), highlighting that altering the input representations of a pretrained VLM disrupts its learned feature distributions and undermines its capacity to process visual-linguistic signals effectively.

Late fusion strategies fared better. The 'concatenate after VLM' method, equivalent to a basic baseline where force features are appended at the decoding stage, improved success to 60%-demonstrating the utility of force sensing. However, our proposed ForceVLA architecture achieved a markedly higher 80% success rate. By introducing force features after the VLM's core encoding and using the FVLMoE module for adaptive fusion, ForceVLA enables specialized routing and deeper multimodal inter-

action. These results confirm two core design insights: force should be introduced post-VLM to preserve pretrained representations, and sophisticated fusion (via FVLMoE) is essential to fully leverage force in guiding contact-rich robotic behavior.

5.5 Visualization and Case Studies

Figure 7 illustrates ForceVLA's ability to adapt motion in response to contact feedback during complex manipulation tasks. In the USB insertion task, when initial attempts failed due to misalignment, ForceVLA re-oriented or re-grasped the drive to achieve successful insertion-behaviors absent in baseline models, which repeated failed motions or applied excessive force. Similarly, in the 'Unstable Socket' scenario (Figure 7c), ForceVLA maintained compliant control as the socket shifted, dynamically adjusting the plug's pose to complete insertion, while baselines lost tracking and failed. These examples highlight a key insight: simply adding force input does not ensure closed-loop adaptation. ForceVLA's FVLMoE module enables deep fusion of force, vision, and language, supporting precise, context-aware control and robust generalization under dynamic physical conditions.

6 Conclusion

In conclusion, we propose ForceVLA, a framework that bridges the gap between high-level modality (vision/language) and low-level physical sensing (force) for contact-rich manipulation. At its core, ForceVLA introduces FVLMoE , a Mixture-of-Experts module that dynamically fuses visual, linguistic, and force modalities to enable fine-grained, context-aware control. Our experiments across five

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challenging tasks show that ForceVLA significantly outperforms strong π 0 -based baselines, achieving an average success rate improvement of 23.2% and up to 80% success on individual tasks. Ablation studies further validate the benefits of late-stage force fusion and expert routing. We also contribute ForceVLA-Data , a new dataset for multimodal contact-rich manipulation. Through the co-design of our architectural approach and dataset, we demonstrate meaningful progress toward VLA systems that exhibit greater adaptive behavior and physical intelligence in manipulation scenarios.

Limitation. Firstly, ForceVLA currently utilizes estimated external wrench values. While this approach has proven effective, these estimations may not always capture the full precision afforded by direct high-fidelity measurements, particularly in scenarios demanding extreme haptic sensitivity. Potential enhancements include exploring the integration of superior sensors or advanced calibration techniques to further refine fine-grained control capabilities. Secondly, ForceVLA's experimental validation was predominantly conducted on robotic platforms with integrated, and typically high-cost, force-torque sensing, which can naturally limit broader accessibility. To promote wider practical deployment and help democratize force-aware manipulation research, we are actively assessing ForceVLA's adaptability and performance on more common, lower-cost platforms equipped with external or retrofitted force sensors. Thirdly, our work focuses exclusively on real-world data collection and evaluation to ensure findings are directly applicable to physical deployment scenarios, as multi-body contact dynamics are central to our problem. While this real-world-first approach addresses the sim-to-real gap, developing high-fidelity simulation environments for our diverse contact-rich tasks was beyond the scope of current resources. We recognize that large-scale training with simulated data represents a promising avenue for expanding the range of tasks our model can address and view this as an important direction for future work.

Acknowledgment

This work was supported in part by the National Key R&D Program of China (Grant No.2024YFB4707600), National Natural Science Foundation of China (No.62576109, 62072112), National Natural Science Foundation of China under Grant 52505029, Natural Science Foundation of Shanghai under Grant 25ZR1401191, Science and Technology Major Project of Jiangsu Province (No.BG2024041), the Shanghai Commitee of Science and Technology, China(Grant No.24511103200) by the National Key Research and Development Project of China (No.2022ZD0160102), Shanghai Artificial Intelligence Laboratory, XPLORER PRIZE grants.

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A Data Collection System

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Our data collection system is depicted in Figure 8(a). The setup features a robotic arm equipped with both a wrist-mounted camera and a static third-person view camera to capture diverse visual perspectives. An operator, wearing a Quest 3 headset and using hand-held controllers, teleoperates the robot arm. A nearby computer runs the necessary software for data acquisition, which includes programs for interfacing with the robot, synchronizing sensor streams, and managing communication with the VR teleoperation hardware. Figure 8(b) illustrates the operator teleoperating the robotic arm to collect demonstration data for the wipe board task.

B Training Details

The models were mainly trained on compute nodes equipped with 8 × NVIDIA RTX 4090 GPUs (24 GB VRAM each), 64 physical CPU cores, and 251 GB system RAM, using Adam optimization ( β 1 = 0 . 9 , β 2 = 0 . 95 ) with a peak learning rate of 2 . 5 × 10 -5 decaying to 2 . 5 × 10 -6 over 30,000 steps. Multi-task training utilized data parallelism across 2 GPUs (global batch size 16, effective 2048 via gradient accumulation) as additional GPUs provided diminishing returns due to communication overhead, completing 30,000 steps in ∼ 12 hours, while single-task training used 1 GPU for 10,000 steps ( ∼ 9 hours), both employing bfloat16 precision with gradient clipping ( ∥∇∥ = 1 . 0 ).

The dimensionalities and key parameters of ForceVLA's core processing modules: Input Projections, the FVLMoE block, and the Action Output Head are detailed in Table 4.

C Router Analysis

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To analyze routing dynamics, we first measured the probability distribution over expert selections for each token as it was processed by the router in the MoE module. For variable-length episodes, we applied percentile-based normalization: each task ( ∼ 10 episodes) was processed by segmenting every episode's token sequence into 100 temporally equidistant intervals, computing the mean top-1 probability per segment, and then averaging these means across episodes. This ensured cross-episode comparability while preserving temporal routing dynamics.

As shown in Figure 9, different tasks exhibit distinct expert utilization patterns. Some tasks (e.g., insert plug , peel cucumber ) show clear temporal specialization, where certain experts dominate specific phases of the task. Others (e.g., wipe board ) demonstrate a more consistent preference for a single expert throughout execution. These trends suggest that the router learns to allocate computation dynamically across experts based on task-specific semantics and temporal structure.

What's more, we found that Expert 0 dominates nearly half of the tokens across multiple tasks. This persistent activation suggests that Expert 0 may function as a general-purpose expert, responsible for the fusion of multiple modalities or routine control primitives that are shared across tasks. Its broad involvement contrasts with the more selective, phase-specific activation of Expert 1 or Expert 3, reinforcing the hypothesis of functional specialization among experts. Such asymmetry in routing frequency reflects not only temporal semantic variance within tasks but also architectural bias toward certain experts, potentially shaped during training.

D Multi-task Evaluation

The results of joint multi-task learning are detailed in Table 5. Notably, both π 0 -fast variants (0% average success rate) failed to acquire skills in this setting, indicating their limited capacity for diverse, concurrent learning. The π 0 -base w/o F model also performed poorly (5% average success rate), managing only 20% success on a single task. Adding direct force input ( π 0 -base w/ F) improved the average performance to 42.5%, primarily due to its success in the insert plug task. Our ForceVLA model demonstrated superior multi-task capabilities, achieving the highest average success rate of 67.5%. It obtained 80% success in both pump bottle and wipe board tasks, and matched top performance in insert plug (100%) and insert usb (10%). This robust performance across multiple distinct tasks indicates ForceVLA's capacity for concurrent skill learning, proficient instruction following for varied goals, and highlights the role of its FVLMoE architecture in utilizing multimodal cues, particularly force, within a shared policy.

E Detailed Breakdown of Failure Modes

Plug Insertion : w/o force models generally exhibit cruder behavior during insertion, unable to adjust their strategy based on interaction state changes. w/ force models can generally perceive changes in external forces but are not flexible enough in adjusting their trajectory. ForceVLA can better maintain continuous contact, follow the plug's surface, make fine adjustments to the force angle and duration within a more precise range, and terminate the action more promptly upon full insertion.

USB Insertion : The overall success rate for this task is low. A common problem across all five models is the difficulty in aligning with the USB port, which we attribute to insufficient visual clarity and the backbone model's inability to process fine-grained visual information. However, ForceVLA's success rate is slightly higher because it exhibits clear autonomous adjustment or re-attempt behaviors upon feeling external force from contact with the socket.

Bottle Pumping : Simply pressing a seen bottle could be completed with 100% success by the pi0-base models and ForceVLA. Therefore, during testing for this task, we introduced additional visual occlusions and background changes. Most of the failure modes occurred under these specific variations. w/o force models often missed the pump or did not press it fully. ForceVLA was more robust but sometimes pressed off-center.

Cucumber Peeling : w/o force models were significantly more prone to breaking the peel mid-way, unable to peel continuously from end to end, and sometimes peeled too deeply, indicating a lack of control over the peeling force. w/ force models peeled more stably with a wider peel, but still had issues with breaking the peel and not following the cucumber's curvature well. ForceVLA could overcome these issues.

Board Wiping : A common problem for all models was the inability to pick up the eraser, possibly because its placement was far from the base camera, resulting in low resolution, and its color was similar to the black table, making precise localization difficult. In the remaining trials, if a small part of the writing wasn't erased, we classify as a failure. Additionally, if a model didn't stop after 5 minutes, we counted it as a failure. The w/o force models started wiping in the air or pressed too hard, leaving scratches, due to their inability to perceive force. ForceVLA made closer contact with the board and applied a more appropriate wiping force than other models.

F Ablation Study: Force Input Masking

To directly assess the value of the force modality within our MoE architecture, we conducted an ablation study where the force input to the ForceVLA model was masked during inference. Table 6 presents the quantitative results comparing ForceVLA with and without force inputs across four contact-rich manipulation tasks. Note that the Peel Cucumber task was not included in this ablation due to time constraints.

As shown in Table 6, removing force inputs leads to substantial performance degradation in most tasks, with particularly severe drops in Plug Insertion (60%), Pump Bottle (37%), and USB Insertion (25%). The Wipe Board task shows minimal degradation (0.3%), likely due to its relatively simpler contact dynamics.

F.1 Failure Mode Analysis

Through detailed analysis of the failed attempts, we identified several characteristic failure modes when force information is unavailable:

Plug Insertion: The end-effector could only push a short distance in the insertion direction, with insufficient trajectory curvature to properly insert the plug into the socket. Alternatively, the endeffector would move to the plug's surface and perform an arcing push motion near the contact point without establishing actual contact, resembling an overfitted insertion trajectory.

USB Insertion: The gripper's closing width was excessively large, preventing it from securely grasping the USB drive. This behavior suggests overfitting to the gripper width values observed during training, resulting in an inability to achieve a firm grasp.

Pump Bottle: The policy repeatedly applied excessive force, causing the gripper to bend. Additionally, off-center pressing caused the bottle to spring away from the gripper, indicating poor force regulation.

Wipe Board: The number of wiping attempts was significantly reduced, resulting in most cases where the board surface was not adequately cleaned.

F.2 Discussion

We hypothesize that these failure modes arise from the following mechanism: Without force input, the FVLMoE module's increased parameter count makes the model more susceptible to overfitting. During inference, the policy exhibits stereotypical behaviors that are misguided by vision alone, reflecting an overfit to the action distribution of the training data rather than reactive responses to actual contact conditions.

Conversely, the fact that the full ForceVLA model with the additional force modality does not suffer from this overfitting strongly indicates that our architectural design effectively leverages force signals to regularize the policy. This regularization enables the model to achieve robust, reactive behaviors instead of merely memorizing training trajectories, thereby validating the importance of force feedback in our multimodal MoE framework.

G Real-world Experiments Visualization

In this section, we present key frames from real-world experiment videos. Each visualization contrasts failure cases of baseline models with successful task completions by our ForceVLA model under similar conditions.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents ForceVLA, a visual-language-action (VLA) model enhanced with force sensing and feedback. Force signals are perceived online and integrated into the model to guide action generation. To enable force awareness, the authors introduce a force signal processing layer and a mixture-of-experts (MoE) architecture for effective force integration. A teleoperation system is developed to collect data with force annotations, resulting in a curated dataset spanning five diverse tasks. Extensive experiments on these contact-rich tasks demonstrate the effectiveness of ForceVLA and its ability to generalize across different scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- Strong motivation. The paper is well-motivated. Contact-rich tasks pose significant challenges for standard VLA policies, making them ideal candidates for evaluating the integration of tactile and force feedback.\n- Well-designed method. The proposed approach is thoughtfully designed. The force signal processing and fusion mechanisms are clearly described in the method section. The fusion strategy, along with the MoE architecture, is a reasonable and effective choice for handling heterogeneous information.\n- Effective and generalizable. ForceVLA demonstrates strong performance on challenging contact-rich tasks such as plug insertion, USB insertion, and cucumber peeling. The authors further validate the model’s generalization capability across variations in object type, insertion height, and external disturbances through a comprehensive set of experiments.\n\nWeaknesses\n\n- Questionable task difficulty and quality. Some task setups raise concerns about difficulty and realism. For example, in the plug insertion task, the plug is initially half-inserted into the socket, significantly reducing the complexity—requiring the robot to simply reach and make a small movement to complete the task. In the demonstration video, the robot’s end-effector trajectory appears quite unsmooth, and it relies on trial-and-error to locate the correct contact point. The USB insertion task shows more impressive results; however, the trajectory near the socket is still jerky, and the robot even drops the USB at one point and needs to re-grasp it. In contrast, tasks like cucumber peeling and board wiping show better performance, though they do not require high precision in position or force control.\n- Task design. As noted, the initial configuration of the plug being half-inserted undermines the challenge of the task. In the board wiping task, the robot can succeed simply by applying a large force, without needing fine control over the force applied. A more demanding task—such as wiping a bristle bottle—would better justify the need for precise force sensing and execution. Additionally, many of the generalization experiments focus on variations in visual appearance or visual disturbances, which are typical in standard VLA setups and do not highlight the unique benefits of force modeling. \n- The website could be more organized.\n\nQUESTIONS:\n- Does the teleoperation setup provide force feedback to the human operator? What specific challenges do the authors face when teleoperating these contact-rich tasks? Additionally, what are the teleoperation success rates for each task?\n- Is the final ForceVLA model trained as a single multi-task policy, or are separate task-specific policies used? \n- While the method is framed as a VLA model, the language component appears limited to providing task instructions for a small number of predefined tasks. Since the model is not aimed at open-world generalization, developing the approach as a vision-based, force-aware policy may be more accurate and appropriately modest than purposely integrating \"L\" to make it align with popular VLAs. \n- How is the base policy $\\pi_0$ used in the training pipeline? Is ForceVLA trained from scratch, or is it fine-tuned from $\\pi_0$?\n- The model is trained on only a few hundred trajectories. Are these data sufficient to avoid overfitting, especially given the complexity of the tasks? What specific mechanisms or design choices encourage generalization, particularly given the relatively small training dataset?\n- Have the authors considered comparing Diffusion Policy or 3D Diffusion Policy? While these are not VLAs, they represent competitive approaches for visuomotor control.\n\nLIMITATIONS:\n- The overall quality of the results is somewhat restricted. Some task executions exhibit suboptimal behaviors such as unsmooth trajectories, retries, and dropped objects, which raise concerns about robustness and precision.\n- The originality of the method is a little bit restricted. Both feature fusion and MOE have already been proposed in previous works.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes ForceVLA, a Vision-Language-Action model enhanced with 6D force sensing. The core module, FVLMoE, is a force-aware Mixture-of-Experts that fuses force, vision, and language modalities during action generation. A new dataset, ForceVLA-Data, is collected for contact-rich tasks. Experiments on 5 real-world tasks show improved performance over π0 baselines.

STRENGTHS AND WEAKNESSES:
Strengths
Novel use of force sensing as a first-class modality in VLA models.
The FVLMoE fusion module is a technically sound idea.
Solid improvements in task success rates (+23.2%) across five contact-rich tasks.
Weaknesses
No simulation experiments, which limits reproducibility and accessibility.
In the "Unstable Socket" task, π0-fast w/f performs best, suggesting that a simple connection can be effective if the pre-training data contains enough torque sensor perception information.

QUESTIONS:
-Why are there no simulation experiments? Could that help with reproducibility or scalability?
-- π0-fast w/f performs best in the Unstable Socket task. Does this mean that simple fusion is sufficient if the pre-training data contains enough torque sensor data? Can the authors share insights on π0-fast competitiveness with sufficient torque sensor training data?
-Can ForceVLA generalize to low-cost robots without precise F/T sensors?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ForceVLA, a novel end-to-end manipulation framework that treats external force sensing as a modality within VLA systems. ForceVLA introduces FVLMoE, a force-aware Mixture-of-Experts fusion module that dynamically integrates pretrained visual-language embeddings with real-time. The paper also introduce ForceVLA-Data, a new dataset comprising synchronized vision, proprioception, and force-torque signals across five contact-rich manipulation tasks. ForceVLA improves average task success by 23.2% over strong π0-based baselines, achieving up to 80% success in tasks such as plug insertion.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper explores a timely and important direction—integrating force signals into vision-language-action (VLA) models for contact-rich manipulation tasks. The proposed method, a force-aware Mixture-of-Experts (MoE) fusion module, enables dynamic processing and deep integration of force, visual, and language modalities during action generation. This represents a meaningful advancement in the development of multimodal large action models, particularly for tasks requiring nuanced physical interactions. I believe the work would be of interest to the community who is working on multimodal learning, multimodal large action model, robot learning, and contact-rich manipulation.\n2. The authors benchmark their model against state-of-the-art approaches such as pi-0 and demonstrate superior performance in tasks like “Insert USB,” “Insert Plug,” and “Peel Cucumber.” Furthermore, under five challenging settings, ForceVLA consistently outperforms π0, highlighting its robustness and effectiveness across diverse contact-rich scenarios.\n3. The authors conduct ablation studies on various architectural designs of ForceVLA, with a particular focus on the integration of force feedback. This analysis is valuable for researchers interested in incorporating temporal signals into vision-language models.\n\nWeaknesses:\n\n1. One of the key contributions of this manuscript is the introduction of a comprehensive data collection pipeline tailored for contact-rich manipulation tasks. This includes the development of teleoperation tools, data converters, and the release of a new dataset. Such an infrastructure is highly valuable for advancing learning in physically grounded manipulation tasks where force feedback plays a crucial role.\n\nHowever, the paper does not sufficiently discuss the limitations of existing contact-rich manipulation datasets or clearly position its contributions in relation to prior work. Several recent efforts have made significant strides in this space:\n\n- **Fan et al.**, *RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot* (RSS 2023 Workshop), introduced a broad dataset for generalizable robotic skill learning.\n- **Narang et al.**, *Factory: Fast Contact for Robotic Assembly* (RSS 2022), focused on high-speed, precise contact interactions in assembly tasks.\n- **Tang et al.**, *IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality* (RSS 2023), tackled the sim-to-real gap for contact-rich assembly by leveraging simulation and domain adaptation.\n\nA more thorough discussion contrasting these works with the proposed dataset—in terms of task diversity, force sensing resolution, data volume, teleoperation fidelity, and real-world applicability—would strengthen the contribution and help the audience better appreciate the novelty and utility of this new resource.\n\n2. Limited discussion on failures: While the proposed method demonstrates favorable performance against existing state-of-the-art, it is unclear why the proposed method fails \"Pump Bottle,\" \"Wipe Board-1 and 2.\" The description of these failures is limited. Could different fusion strategies work better for these categories?\n\nQUESTIONS:\n1. Please provide a more thorough discussion contrasting these works with the proposed dataset.\n\n2. Limited discussion on failures: While the proposed method demonstrates favorable performance against existing state-of-the-art, it is unclear why the proposed method fails \"Pump Bottle,\" \"Wipe Board-1 and 2.\" The description of these failures is limited. Could different fusion strategies work better for these categories?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents ForceVLA, a method for enhancing vision-language-action (VLA) models with force information using a mixture-of-experts (MoE) policy architecture. The proposed approach is evaluated on a real-world robot across several manipulation tasks. While the idea of integrating force signals into VLA frameworks is appealing and timely, the paper leaves several architectural and conceptual questions unresolved.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n* Exploring force-enhanced VLAs is a timely and relevant problem.\n* Real-robot experiments are conducted on diverse tasks with promising results.\n* The mixture-of-experts formulation show potential to leverage multimodal signals.\n\n**Weaknesses:**\n\n* The related work omits several recent efforts combining vision, language, and tactile/force signals (e.g., VTLA, FuSe). A broader and more up-to-date literature review would better situate the paper.\n* From the introduction, one would expect experts in the MoE to specialize in different modalities (e.g., one for vision, one for force). However, the architecture does not enforce this: modalities are simply concatenated, and all experts receive the same fused input.\n* It is unclear what exactly comprises a \"force observation\"—e.g., Fig. 3 suggests a snippet of force trajectories rather than a single reading, but this is not explained clearly.\n* There is no force feedback during teleoperation, which raises doubts about whether the demonstrations are truly reactive to force or just to delayed visual cues.\n* Results are presented as tables of numbers with little analysis. Success rates remain low. Why? What are the main failure modes?\n* A comparison with MoE models trained without force input is missing, which would be important to isolate the value of the force modality.\n* In Table 2, performance of pi0 degrades when force is added (without MoE). Why does force hurt performance in this case?\n\nQUESTIONS:\n1. Can you clarify whether each expert in the MoE is intended to specialize in different modalities? If so, how is this encouraged during training?\n2. What exactly is included in a \"force observation\"? Is it a single reading, or a sequence?\n3. Given that the teleoperator receives no force feedback, how are successful force-sensitive demonstrations collected?\n4. Why does pi0 perform worse with added force information in Table 2?\n5. What are the typical failure modes that explain the relatively low success rates?\n6. Can you include a comparison to a MoE model trained without access to force inputs?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}