From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
Authors: Zhengshen Zhang
Hao Li
Yalun Dai
Zhengbang Zhu
Lei Zhou
Chenchen Liu
Dong Wang
Francis E. H. Tay
Sijin Chen
Ziwei Liu
Yuxiao Liu
Xinghang Li
Pan Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-language-action Model
Robot Learning
Robotics
3D Understanding
Primary area: applications to robotics, autonomy, planning
TL;DR: FALCON injects 3D spatial tokens from foundation models into the action head of a VLA model, enabling robust spatial understanding and SOTA performance across diverse manipulation tasks without disrupting vision-language alignment.

Abstract: Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce **FALCON (From Spatial to Action)**, a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an *Embodied Spatial Model* that can optionally fuse depth, or pose for higher fidelity when available, without retraining or architectural changes. To preserve language reasoning, spatial tokens are consumed by a *Spatial-Enhanced Action Head* rather than being concatenated into the vision-language backbone. These designs enable FALCON to address limitations in spatial representation, modality transferability, and alignment. In comprehensive evaluations across three simulation benchmarks and eleven real-world tasks, our proposed FALCON achieves state-of-the-art performance, consistently surpasses competitive baselines, and remains robust under clutter, spatial-prompt conditioning, and variations in object scale and height. Project page: https://falcon-vla.github.io/

Published as a conference paper at ICLR 2026
FROM SPATIAL TO ACTIONS: GROUNDING VISION-LANGUAGE-ACTION MODEL IN SPATIAL FOUNDATION PRIORS
Zhengshen Zhang Hao Li,, Yalun Dai Zhengbang Zhu', Lei Zhou?, Chenchen Liu?, Dong Wang', Francis E. H. Tay². Sijin Chen', Ziwei Liu", Yuxiao Li. Xinghang Li Pan Zhou 'ByteDance Seed, National University of Singapore, NNnyyngg Technological University, "Tsinghua University, 'Singapore Management University zhengshen zhang& @ u. nus edu liuyuxiao. 8 8760bytedannee com 1 lixingha233 200mml 1s tsinghua edu. cn
panzhou edu. sg
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Figure I: We propose FALCON, a vision- sasion-lagguag--attee model that achieves robust 3D spatial un- derstanding by effectively integrating spatially rich tokens and semantic features. FALCON demon- strates notable modality transferability by performing robustly with both RGB-only and multi-modal inputs, superior spatial understanding in tasks involving unseen object sizes, heights and abstract spatial instructions, and strong few-shot generalizability in real-world scenes. The model achieves state-of-the-rs thrt performance across a diverse range of benchmark evaluations.
ABSTRACT
Existing vision- saaioo--anguag--acttoon (VLA) models act in 3D real-world but are typi- cally built on 2D encoders, leaving a spatial reasoning gap that limits generaliza- tion and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. this work, we introduce FALCON (From Spatial to Action), novel paradigm that injects rich 3D spa- tial tokens into the action head. FALCON leverages spatial foundation models deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose for higher fidelity when available, without retraining or architectural changes. To preserve language rea- soning, spatial tokens are consumed by a Spatial-Enhanees Action Head rather than being concatenated into the vision-language backbone. These designs enable FALCON to address limitations in spatial representation, modality transferability. and alignment. In comprehensive evaluations across three simulation benchmarks and eleven real-world tasks, our proposed FALCON achieves state-of-thi the-art per- formance, consistently surpasses competitive baselines, and remains robust un- der clutter, spatial-prompt conditioning, and variations in object scale and height. Project page: https:/ htt////aloaoo caalcon-tee vva. github. io/
"Works done during internship at ByteDance Seed. 'Corresponding authors. PProecct lead.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advances in vision- casiionlanguage-ace action models (VLAs) have significantly advanced the pursuit of generalist robotics, enabling robots to interpret natural language instructions and exccute intricate action sequences (Brohan et al., 2023; Kim et al., 2024; Black et al., 2024; Li et al., 2024a; Zhang al., 2024; Intelligence al., 2025; Liu et al., 2025h). Most advanced VLAs are built on 2D foun- dation models like Vision Language Models (VLMs) (Liu et al., 2025a; Shao et al., 2025; Alayrac al., 2022: Radford al., 2021; Peng et al., 2023; Liu al., 2023), aligning 2D images with text and leveraging the strong language understanding of the Large Language Models (LLMs) for infor- mation processing. This design provides strong semantic understanding and supports manipulation tasks conditioned on language and camera inputs.
However, while VLMs operate purely in the 2D domain, VLAs must interact with the 3D physical world. This discrepancy results in a critical gap: current VL.Ss lack reliable 3D spatial understand- ing, leading to persistent challenges in generalization and adaptability. Specifically, the absence of explicit 3D awareness causes VLAs to struggle in scenarios that require reasoning about geometry, depth, or spatial relations. First, they exhibit limited generalization, failing to transfer robustly to novel scenes, backgrounds, or object variations (Ze et al., 2025). Second, they lack adaptability to environmental changes, such as height variations or object scale differences (Li et al., 2025). These limitations now form a major bottleneck in developing reliable generalist robot policies.
To address this gap, recent works incorporate 3D information into VLAs (Li et al., 2025; Sun et al., 2025: Zhen et al., 2024), most often by providing explicit 3D) inputs (e.g.. point clouds depth maps). While effective under ideal conditions, these methods suffer from low modality transferabil- ihy-the ability to function and improve under different input modalities (RGB-only, RGB-D, point clouds, camera pose) without retraining or collapsing. This stems from two fundamen- tal issues. First, acquiring high-quality 3D inputs requires specialized sensors that are expensive and difficult to deploy in practice. Second, many large-scale manipulation datasets (e.g.. Open X- Embodiment dataset (O'Nei) "eill al., 2024)) lack aligned 3D annotations, limiting scalability. As result, such methods are tied to specific input modalities and break down when those inputs are unavailable.
An alternative direction introduces weak 3D cues, e.g., peeudo-depth estimates (like Zoe (Bhat et al., 2023)) or learnable spatial embeddings (Qu et al., 2025). However, these approaches face three fundamental limitations. (1) Limited spatial representation. The learnable spatial embed- dings provide only weak geometric signals within the nigh-dimensional space of LLMs, failing to capture robust 3D priors necessary for tasks like reasoning about height differences or object sizes in grasping. (2) Lack of modality transferability. While encoding some 31) cues, they cannot effectively exploit higher-quality 3D inputs when available. (3) Challenges in alignment. Concate- nating spatial embeddings with text tokens risks disrupting the original linion-language alignment. The scarcity of 3D data makes it difficult properly align modalities, causing embedding drift that degrades zero-shot generalization, especially in tasks requiring high-level reasoning like spatial prompts (Qu et al., 2025).
Contribution. We propose FALCON (From Spatial to Action), a novel paradigm that integrates richer and more representative 3D spatial tokens into VLAs through an improved injection scheme.
To solve limitation (1) i.e., weak 3D spatial representation, we leverage insights from spatial foun- dation models that encode scenes into token sequences for holistic 3D reconstruction. FALCON adopts broader and richer tokens from these foundation models, and delivers comprehensive spatial information from RGB signals alone, improving robustness when explicit 3D inputs are absent.
For limitation (2) of poor modality transferability, we introduce an Embodied Spatial Model that can optionally integrate extra 3D modalities (e.g., depth, poses). Unlike prior methods that require specific 3D inputs (Li et al., 2025; Sun et al., 2025; Zhen et al., 2024), our design is flexible: when RGB-D cameras or calibrated scenes are available, the model gains additional accuracy; when not, it still performs effectively with RGB-only input. This optional pathway substantially improves transferability by flexibly utilizing 3D signals from sensors without requiring rentuiingg architectural changes.
To overcome limitation (3) of alignment challenges, draw inspiration from the brain's 'ss division of labor. The VLM (cerebrum) handles high-level reasoning and semantics, while the action head (cerehellum) manages fine-grained motor control and sensorimotor integration (Rochefort et al.,
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2011: Figure, 2024). Motivated by this, design Spatial- Entrrned Action Head that directly incorporates spatial tokens into action decisions, which is a more natural fit since precise control depends detailed spatial cues. This departs from prior approaches that forcibly align spatial and text tokens within VLMs (Fan et al., 2025; Wu et al., 2025).
In this way, FALCON provides (i) robust spatial reasoning, (ii) strong modality transferability, and (iii) principled integration of 3D priors into VLAs. As shown in Fig. 1, extensive experiments across three simulation benchmarks and 11 real-world tasks which including cluttered-scene manipulation, few-shot adaptation, and spatial capability evaluation, demonstrate that FALCON consistently out- performs existing baselines, achieving state-of-the-ar performance with strong robustness and gen- eralization (e.g., spatial promppt conditioning, height-aware manipulation, object-scale variation).
2 METHODOLOGY
In this section, we first formulate the problem of task-oriented, language-guided robot control (Sec. 2.1). We then introduce the overall architecture (Sec. 2.2). training objective (Sec. 2.3) of FALCON, and detail its two key components: the Embodied Spatial Model (ESM) for 3D geo- metric representation (Sec. 2.4), and the Spatial- Action Head for multimodal fusion and action generation (See. 2.5).
2.1 PROBLEM DEFINITION
We study the problem of lask-oriented robot control, where robot must interpret visual observa- at time step t and a natural language instruction L to generate an action
tions O = . 18 sequence A, [q.... ................................ a++c-1] through mapping function F(-). Each action is 7D vector encoding the 6-DoF gripper pose (c.g., Euler angles) and a binary open/close state, with C denot- ing the action horizon. Our focus is table-top manipulation with robot arm, using inputs from a static side camera I,5rd (global scene context), a wrist-mounted camera 7hand (fine-grained object details), or both. Optional depth maps D, E IR Hx and camera poses P RT can further enhance spatial grounding when available but are not strictly required, ensuring robustness across different sensing setups. Formally,
A: F(O,, L, D., P), where D; and P are optional.
(1)
This setting spans diverse applications from service robots following language commands to indus- trial manipulators performing instruction-driven assembly, where robust performance in unstruc- tured environments requires tight integration of semantic understanding and geometric reasoning. To this end, propose FALCON, generalist robot policy that overcomes limitations of prior VLAs by integrating rich geometric priors from spatial foundation models while flexibly leveraging optional 3D modalities. The result is spatially enhanced VLA that unifies semantic reasoning with fine-grained geometric grounding for robust manipulation across diverse conditions.
2.2 OVERALL ARCUITECTURE
As illustrated in Fig. 2, FALCON is end-to-end VLA consists of three core components: (1) a 2D VLM for multimodal semantic representation, (2) an ESM for extracting 3D structural features, and (3) Opatial-Enbanceer Action Head that combines both streams to generate precise robot actions.
At each timestep f. given image observations O, and a language instruction L, FALCON employs a pre-trained 21) VLM (e.g., Kosmos-2 (Peng et al., 2023)) extract a contextualized representation of the scene and task. The visual and textual inputs are tokenized and formed into a unified multi- modal sequence. A learnable action token tar appended
and the corresponding output hidden state Eact E R. Pmc, where Daet represents the feature dimension, is extracted as the semantic action representation, encapsulating task-oriented behavior grounded in multi-modal context.
In parallel, the ESM processes third-view image Tndd along with optional geometric inputs such as depth D. and camera poses P, to extract spatial structure representations. Through a spatial encoder of spatial tokens Tepl, encoding global 3D geometric priors essential for scene understanding, Further details are provided in Sec. 2.4.
(-p it outputs
The extracted semantic action token Ext and spatial tokens Typ are then integrated in the Spatial- Enhanced Action Head, collectively guide action generation. We introduce lightweight fusion
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Fi Maudas Yes Plodte alucki by
00
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Vision-Language Model [Kosmos-2111
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Action Token
Spatial Token
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Figure 2: Overview of FALCON framework. FALCON integrates 2D VLM (e.g., Kosmos- 2), an Embodied Spatial Model, and a Spatial-Enhanced Action Head. At timestep t, the VLM processes visual observations O, and language instructions L to produce semantic action token tas- Concurrently, the Embodied Spatial Model encodes third-view image 1 and optional geometric inputs into spatial tokens spl. These are fused by the Opatial-Enhanee Action Head to generate precise robot actions Ag, enabling robust manipulation through joint semantic and spatial reasoning.
mechanism that aligns and combines these complementary representations (see Sec. 2.5 for more details), serving as input to an action predictor that outputs action sequences A,. This novel design ensures that both high-level semantic context and 3D structural representation are directly incorpo- rated into the policy, significantly improving precision and generalization in manipulation tasks.
2.3 TRAINING OBJECTIVE
During the training process of FALCON, the objective for action sequence generation is formulated as the minimization of a composite loss function over the predicted action horizon. Specifically, we compute the discrepancy between the predicted action sequence 0t:t--11 and the corresponding ground truth sequence 0 using two complementary loss components: the Mean Squared Error (MSE) and the Binary Cross-Entropy (BCE). The overall loss function is defined as:
1+C-1
C
MSE(ar.pose ai.pose) + A BCE (i grippers as gr.ggrpp)r).
(2)
i=!
where the MSE term penalizes errors in the first six dimensions of the action vector and the BCE loss is applied the last gripper dimension. The weighting factor balances the contributions of the two loss terms, ensuring stable and representative learning across heterogeneous action components.
To integrate spatial awareness into the VLA model while preserving the pre-trained components' capabilities, we carefully design a two-stage post-training pipeline. Drawing inspiration from (Liu al., 2023). in Stage 1 we freeze all pre-trained components and optimize only lightweight adapter to achieve initial alignment between spatial tokens and the VLA's feature space. Building upon this aligned foundation, Stage 2 unfreezes both the VLM and adapter for joint refinement while keeping the remaining parts frozen, thereby enabling the VLM implicitly refines it's semantic fea- tures to incorporate spatial cues, which subsequently inform action prediction. The detailed training paradigms of the FALCON model are available in Appendix A.3.
2.4 EMBODIED SPATIAL MODEL
Although recently proposed Spatial Foundation Models (Wang al., 2024; 2025a) have shown promising results in image-only reconstruction, they cannot exploit additional 3D modalities com- monly available in robotics, such RGB-D cameras and calibrated poses. To address this limitation, we propose an Embodied Spatial Model that injects 3D conditions (i.e., depth, pose) to build more accurate spatial representations, enabling our action head to predict precise trajectories in space.
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Specifically, given an imput image I at time t, we follow VGGT (Wang al., 2025a) to encode it into spatial tokens ssl E IR TRMxP, P,, where M is the token number per image and D, is the token dimension. The image is first tokenized into visual tokens Tvis via DINO (Oquab et al., 2024). These are then concatenated with a learnable camera token taam E RDD and fed into a Spatial Encoder (.), which consists of N cross-attention and self-attention blocks:
(Tapls team) = Spi (T team).
The resulting spatial tokens Tap and refined camera tokens feam are passed a depth predictor and camera predictor, respectively, to achieve accurate scene reconstruction. Notably, the spatial tokens Top,, which encapsulate rich spatial priors, have shown significant benefits for spatial understanding when integrated into VLMs (Fan et al., 2025; Wu et al., 2025).
3D Conditions Encoding. First, as shown in Fig. 2, given camera pose P E IR? input, we encode the intrinsic and normalized extrinsic of the side camera into the GT camera token fam-cam E R using an MLP-based camera encoder Eoam (-). Given depth D, E R and its valid map Map E IR H input, we first normalize depth Dj Dr (Norm(:)) to handle any depth ranges at train and test time. The normalized depth map D; is concatenated with its corresponding validity mask Map enabling us to capture frame-wise incomplete depth information. The resulting tensor is then passed through a depth encoder Edpe()). which consists of stack of convolutional layers with a kernel size 14 x 14, effectively partitioning it into a sequence of tokens Tap that are aligned in size with the image tokens T vis- The formulation is shown below:
Egt-cam
Eçam (team)
Tap- (D))
|| Mapl), Tapt IR BxD,
where [Il-] denotes channel-wise concatenation.
3D Conditions Injection and Training Strategy. After obtaining the GT pose token tg--cam and depth tokens Tap. our objective not only to achieve accurate reconstruction through the recon- struction head under 3D-conditioned settings, but also to enhance the geometric grounding ability of the spatial tokens spl generated by the Spatial Encoder. To this end, we replace the learnable camera token team with the GT camera token tgt-cam. and fuse the depth tokens Tdps with the image tokens Tvis via glement-wise addition. Meanwhile, robotic applications, depth sensors or accu- rate camera poses may not always be available (e.g., Open X- X-Embodiment dataset). To preserve the model's 's ability to reason about spatial structure even without 3D conditions, we design a stochastic conditioning strategy. Specifically, we randomly decide whether to inject depth and/or pose during training, formulated as:
(T team) = Exp( Tvis + baTes bpt.t-ccm + (1 bp ttam)))
(5)
where ba, 6p Bernoullipp). This strategy ensures the model can exploit depth and pose cues when available, while retaining strong image-only spatial reasoning when they are absent. As for supervision, we follow VGGT (Wang et al., 2025a) to adopt depth, point map, and pose losses to formulate multi-task talti-taskaan supervision.
2.5 SPATIAL-ENHANCE ACTION HEAD
As illustrated in Fig. the proposed Spatial-Enhanced Action Head integrates geometric repre- sentations Tep from the ESM with semantic features fac from the VLM, enabling more accurate and spatially-aware policy learning.
Modality Fusion Strategies. To combine these complementary representations, first compress the spatial tokens Tap into a unified vector tapl E D: through a max-pooling operation, then project it into the VLM's feature space to obtain Esp using a lightweight MIP adapter DD The aligned spatial feature Enp is then fused with the semantic action token face through highly efficient element-wise addition (see Sec. 3.3 for detailed ablations about different fusion strategies). The overall fusion process is formulated as:
Expl= R Dar D (tspl), Frused
East Esp..
(6)
This dedicated fusion mechanism preserves the pre-trained representation space and generalizable capabilities of the VLM while enriching VLA with geometrically grounded structural awareness.
Action Predictor. The fused feature vector is forwarded to an action predictor æ to generate robot actions. We explore two distinct architectures for this predictor: An based predictor directly
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Published as a conference paper at ICLR 2026
maps the current fused feature vector to action output: A, (ffussa)). For long- aang-horioon robotic
tasks that involve sequential decision decision-making making, we employ a predictor based on the long short-term memory (LSTM) network (Hochreiter & Schmidhuber, 1997; Chung et al., 2014) that utilizes history of feature representations. This approach processes the sequence fused pt-#++1 ..... fused through the LSTM network, followed by an MLP that produces the final action chunk prediction: A, = (ft fissed): where H denotes the history length. Each fissed obtained through the same л fused feature fusion process described previously.
By integrating spatially rich tokens from the ESM with semantically grounded features from VLM, model achieves enhanced spatial perception capabilities while retaining strong semantic align- ment with language instructions. Moreover, the proposed fusion strategy significantly enhances the performance of the real-world spatial awareness tasks of the policy, demonstrated in Sec. 3.2.
EXPERIMENTS
Benchmarks. For simulation, we evaluate on the widely used benchmarks CALVIN (Mees al., 2022) and SimplerEnv (Li et al., 2024c). For real-world tasks, we design settings that span from simple interactions (e.g., lifting a yellow pepper) to long-horizon, spatially demanding activities (e.g., placing red coke can on the bottom shelf), thereby thoroughly testing robustness and spatial reasoning. Further benchmark details are provided Appendix A.6.
Implementation Details. FALCON is built on Kosmos-2 (Peng et al., 2023) VLM backbone (~1.6B parameters), combined with a 1.08-parameter Embodied Spatial Model and a Spatial- Enhanced Action Head, totaling 2.9B parameters. Training conducted cluster of 32 A100 GPUs. Additional training and deployment details are available in Appendix A.4 and A.5.
3.1 SIMULATION EXPERIMENTS
CALVIN Evaluations. Tab. presents the evaluation results the CALVIN benchmark. Our method achieves SOTA performance in both the ABC-D and ABCD-D settings, significantly out- performing all prior approaches. These results highlight FALCON's strong ability tackle diverse tasks and execute long-horizon, language-condttioeed manipulation. Notably, in the challenging zero-shot ABC-D setting, FALCON surpasses previous methods that rely ground-truth point clouds (e.g., 3DDP (Ze et al., 2024) and 3D Diffuser Actor (Ke et al., 2024)), improving the Arg. Len. by 4. 13 and 1.05, respectively. This provides clear evidence of the effectiveness of implicit spatial information integration strategy.
Table I: Long-horizon robotic manipulation evaluation on the CALVIN benchmark.
Method
Task
Tasks Completed in a Row (%)
Avg. Len. 1
3
4
5
MCII (Lynchh& & Sermanet, 2020)
ABCD-s1
37.3
2.7
0.2
0.0
0.0
0.40
RT-1 (Brohan al., 2022)
ABCD++D
84.4
61.7
43.8
32.3
22.7
2.45
Gobo-Flamingo (Li et al., 2024b)
ABCD-+D
96.4
89.6
82.4
74.0
66.0
4.09
GR-1 (Wu et al., 2024)
ABCD-+D
94.9
89.6
84.4
78.9
73.1
4.21
UP-VT.A (Zhang al., 2025)
ABCD-11
96.2
92.1
87.9
84.2
81.2
4.42
RoboVLM (I.i al., 2024a)
ABCD-1D
96.7
93.0
89.9
86.5
82.6
4.49
FALCON (ours)
ABCD-$D
97.2
93.3
90.3
88.0
84.0
4.53
3DDP (Ze et al., 2024)
ABC-DD
28.3
2.3
0.0
0.0
0.0
0.27
MCIL (Lynch & Sermanet, 2020)
ABC-D
30.4
1.3
0.2
0.0
0.0
0.31
RT-1 (Brohan al., 2022)
ABC->D
53.3
22.2
9.4
3.8
1.3
0.90
Robo-Flamingo (Li al., 2024b)
ABC-D
82,4
61.9
46.6
33.1
23.5
2.47
GR-I (Wu et al., 2024)
ABC-D
85.4
71.2
59.6
49.7
40.1
3.06
3D Diffuser Actor (Ke et al., 2024)
ABC-D
93.8
80.3
66.2
53.3
41.2
3.35
UP-VLA (Zhang al., 2025)
ABC->D
92.8
81.5
76.9
69.9
4.08
RoboVLM (Li et al., 2024a)
ABC->D
98.0
93.6
85.4
77.8
70.4
4.25
Seer-Large (Tian et al., 2024)
ABC >D
96.3
91.6
86.1
80.3
74.0
4.28
FALCON (ours)
ABC-D
98.4
94.5
88.6
82.5
75.5
4.40

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Table 2: SimplerEny evaluation across different policies on WidowX Robot tasks. Put Spoon:
Put Spoon Towel. Put Carrot: Put Carrot on Plate. Stack Block: Stack Green Block on Yellow Block. Put Eggplant: Put Eggplant in Yellow Basket.
Method
Put Spoon
Put Carrot
Stack Block
Put Eggplant
Average
RT-I-X X (O'Neill et al., 2024)
0.0%
4.2%
0.0%
0.0%
1.1%
VLA (Kim et al., 2024)
0.0%
0.0%
0.0%
4.1%
1.0%
Octo-Base (Octo Model Team al., 2024)
12.5%
8.3%
0.0%
43.1%
16.0%
RoboVLM (I. et al., 2024a)
45.8%
20.8%
4.2%
79.2%
37.5%
SpatialVLA (Qu al., 2025)
16.7%
25.0%
29.2%
100.0%
42.7%
FALCON (ours)
62.5 %
41.7%
20.8%
100.0%
56.3%
Table 3: SimplerEnv evaluation across different policies Google Robot tasks. Open/Close: Open Close Drawer. Drawer Apple: Open Top Drawer and Place Apple.
Method
Pick Coke
Move Near
Open/Close
Drawer Apple
Average
RT-1-X (O'Neill al., 2024)
%
31.7%
$9.7%
21.3%
42.4%
RT-2-X (O'Neil) et al., 2024)
78.7%
77.9%
25.0%
3.7%
46.3%
Octo-Base (Octo Model Team al., 2024)
17.0%
4.2%
22.7%
0.0%
11.0%
OpenVLA (Kim et al., 2024)
16.3%
46.2%
35.6%
0.0%
24.5%
TraceVLA (Zheng el al., 2024)
28.0%
53.7%
57.0%
34.7%
RoboVLM (Li al., 2024a)
77.3%
61.7%
43.5%
24.1%
51.7%
SpatialVLA (Qu et al., 2025)
86.0%
77.9%
57.4%
0.0%
55.3%
FALCON (ours)
90.7%
79.2% %
39.8%
41.7%
62.9 %
SimplerEnv Evaluations. Tab. reports the results the Bridge- Wildoe-WWiwwWX setup, where FALCON consistently outperforms all baselines and achieves best performance. The notable improvements observed in challenging tasks like Pul Spoon on Towel (16.7% vs. 62.5%) and Pul Carrot on Plate (25.0% vs. 41.7%), demonstrating FALCON's superior adaptability and effectiveness.
Tab. summarizes the performance of various generalist policies the Google Robot setup. FAL- CON achieves an overall success rate of 62.9%, surpassing all baseline methods. Notably, on the challenging task Open Top Drawer and Place Apple, most baselines show near-zero success rates. Even the large-scale closed-source model RT-2-X (O'Neill et al., 2024) with 55B param- eters achieves only 3.7% success, while FALCON delivers an impressive 41.7%, highlighting its exceptional generalization and spatial perception capabilities.
3.2 REAL-WORLD EXPERIMENTS
To enable a more comprehensive evaluation, we conduct series of carefully designed real-world experiments covering diverse object manipulation scenarios with varying task variations. The ex- periments are organized into three distinct settings: Base Tasks, Few-shot Adaptation, and Spatial mixture of the
Understanding Capability Evaluations. All models initially pre-trained Open X- E-moodiment dataset (O Neile et al., 2024) and then fine-tuned with multi-task real-robot data. Relevant qualitative results are provided in Appendix A.1I.
Real-Warid Hobot Manigulation Base Tasks
71
Figure 3: Evaluation of base tasks in cluttered scene. Base Tasks contains total of nine dis- tinct task suites, encompassing language grounding (cluttered scenes with random distractors) and semantic understanding (unseen object poses).

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Larger Block
Smaller Block
Cup height change
jut
Setling: Siow Chenge (Largec)
Setting: Size Change (Smaller)
Seiling: Height Change
Setting: Spatial Prompe [1)
Setting: Spatial Prompt (2)
Succees Bate
100
75.70.0
50.0
Succode Rate
60
60.0
Suncarso Ralee
100"
100,0
Succees Itate
100
80
80.0
40.0
75
60.0
20.0
50.0
80
50.0
20.0
20.0
10,0
20.0
50
40.0
30.0
80
30.0
8
A
C
25
A
8
B
D
Muthasl
Metbods
Meiloods
Methods
Meilhol
Figure 5: Spatial Understanding Capability Evaluations consist of four tasks with varying levels of spatial complexity, designed to further investigate the spatial perception capabilities of FALCON.
Base Tasks. As shown in Fig. 3, FALCON achieves the highest average success rate of 70.0% across all nine task suites, outperforming the advanced method SpatialVLA (Qu et al.. 2025) (44.4%) by 25.6%. Moreover, in the task pick banana and place on red plate, while RoboVLM (Li et al., 2024a) and VLenVLA-OFT (Kim et al., 2025) often erroneously place the banana onto a yellow plate, FALCON consistently places it on the correct red plate, demonstrating precise instruction following capability and superior scene understanding
Few-shot Adaptation. As shown in Fig. 4, FALCON achieves the highest performance across all settings, significantly outperforming the second-best model by
Setting: Simple
Unseen Average Results
60.0
Sucess Rale
880
Success Rale
60
27.5% in Simple and 27% in Unseen Average. No- tably, in the Unseen Object variation of the task open drawer and place bread (Fig. 1), FALCON achieves an impressive success rate of 80%, while other mod- els demonstrate near-zero success. Success rates for individual variants and sub-tasks are provided in Ap- pendix A.11.2.
60.0
60
50.0
40
33.7
28.3
40
27.5
20
14.6
20
B
Methods
Methods
Figure Performance comparison of different methods. Simple setting (left): includeing four challenging tasks selected from Base Tasks. Unseen scenarios (right): containing three unseen variations: Unseen Object, Background, and Task Description to evaluate the robustness and generaliza- tion of all models.
Spatial Understanding Capability Evaluations. As illustrated in Fig. 5, FALCON demonstrates supe- rior spatial understanding, outperforming all existing policies across the evaluated tasks. Notably, baseline methods such as RoboVLM often struggle with ob- jects of varying sizes. For larger blocks, collisions fre- quently occur during the placement of the blue block, while smaller blocks are prematurely released before placement, leading to task failure. In contrast, our method exhibits strong robustness to scale variations, avoiding these issues and achieving the highest success rates in both scenarios.
3.3 IN-DEPTH ANALYSIS
Table 4: Ablation studies on spatial token injection methods and fusion strategies. Results confirm that the standard FALCON paradigm achieves the best per- formance, validating it the optimal design choice.
Fused Fost
Action Tokan L..
TowkaitTkkeni,
Linear
Add
Method
Task
Tusks Completed Row (5)
Avg. Lon.
Cross-Attn.
FILM
FALCON PALCCON CONALAAAC
ABCID
92.9
85.4
79.4
68.1
4.000
M-Gaeel
ABCD-11 -D
ABCD-ID
93.7
93.8
85.7
80.2
75.4
67.5
69.6
3.98
4.04
(al Crosz-Atten
(b) FiLM-Gated
[c) Element Add
PALCON tours)
ABCI 3D
94.0
86.7
80.8
76.4
70.9
4.08
FALCON-EMMSSE
ABC-D
94.2
85.2
75.6
66.1
57.6
3.79
Figure 6: Different modality fusion strategies between spatial and seman- tie action tokens.
Cross Attention
ABC-ID
91.3
81.9
72.9
64.9
57.2
3.68
M-LM-iittdd
ABC-D
92.9
83.7
58.4
3.26
FALOON
ABC-DD
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Published as a conference paper at ICLR 2026
Spatial Token Injection. To verify the effectiveness of our strategy for injecting 3D information into the action head, we evaluate a variant following the approach of most 3D-based VLAs, where spatial tokens from the ESM are directly injected into the VLMs (denoted FALCONVI.N Mokens) on CALVIN. As shown in Tab. 4, this approach results in significant performance degradation com- pared to the standard FALCON paradigm. Notably, in the zero-shot ABC-+D setting, the Avg. Len. drops significantly from 3,91 to 3.79. These results indicate that introducing fine-grained spatial fea- tures into the VLM disrupts its pre-trained semantic representation space, negatively impacting its generalization capability. In contrast, injecting spatial tokens directly into the action head preserves the VLM's integrity while effectively utilizing geometric cue, making it the superior strategy.
Modality Fusion Strategies. To combine the aligned spatial feature tspl with the semantic action token Cast, we further investigate three distinet fusion approaches:
The action token fact serves as the query, while the projected spatial feature spl provide key and value inputs. Multi-head attention enables adaptive feature recalibration based on cross-modal relevance.
1) Cross-Attention Fusion:
2) FiLM-Gated Modulation: This method uses the spatial feature Espl to generate affine parameters (x, B) for feature-wist wise linear modulation of the action token lact. followed by a gating mechanism that learns to blend the modulated semantic and original spatial features.
3) foamnnt-wise Addition: A direct, non- panaaaamettric combination of the two feature vectors.
As shown in Tab. 4, element-wise addition (our standard FALCON) consistently delivers the best performance across all experimental settings. This method achieves the highest task success rates while remaining both simple and computationally efficient, as it introduces additional parame- ters. The results underscore the effectiveness of a straightforward, parameter-free fusion strategy for combining spatial and semantic representations in VLA models.
Table 5: Performance comparison of different
modality input on CALVIN benchmark. Kosmos-
VLA (w/ rgb) is a 2D VIA without ESM. Kosmos- VLA (w/ rgb-d) is point cloud-based variant where the ESM is replaced by lightweight point cloud en-
Sutting:
Setling: Height Change
100 90.0
80.0
Seccees lotee
coder (Ze et al., 2025) while retaining other parts.
60.3
60
50.0
50.0
40.0
20.0
20.0
Method
Task
Tasks Completed in Row (5)
Len.
2
5
Methods
Methods
Kosmee VLA (w/ 1ph)
ABCD-D
92.5
85.4
79.1
14.9
68.7
4.01
Kosmes-Vi VLA (w// (g/h/))
ABCT>-1
90.4
85.3
80.0
76.5
70.5
4.05
Figure 7: Performance comparison of different modality input on real- world tasks. Left: lift the yellow pep- per. Right: put white cup on pink cloth- cup height change.
PALCON (ob
ABCD-10
94.0
36.7
$0.8
76.4
70.9
4,58
BALCON (Aat w/th
ABCD-DD
93.8
36.6
$1.8
76.5
71.3
4,69
FALCON (p-
ADCD-DD
91.0
87.0
$1.3
76.8
70.3
4,09
FALCON (Aec) vol d)
ABCD-D
90.7
86.4
80.9
76.8
69.8
4,07
Kosmee VLA ow/ (w/r2b)
ADC-D
91.6
30.0
68.3
58.8
49.0
3.88
Kosmee- VLA (2//))hdd)
ABC-D
90.6
860
75.6
73.3
66.3
3.88
FAILCON (ol rph
ABC-D
90.7
86.9
77.9
70.3
62.2
3.91
PALCON (Aast widh)
ABC-D
94.2
$8.4
79.4
70.9
61.8
3.95
BALCON (vo qph-go d)
ABC-D
94.7
86.7
79.1
72.4
64.4
3.97
RALCON (Aost wol d)
ABC-D
94.0
87.0
78.6
71.4
63.8
3.95
Modality Transferability. To evaluate the modality transferability of FALCON, we conduct exten- sive experiments on both the CALVIN benchmark and real-world tasks to demonstrate the benefits of additional modality inputs for our approach. As shown in Tab. 5, under identical input conditions, FALCON outperforms Kosmos-VE VLA in the ABCD+D setting. In the zero-shot ABC-D setting, both methods achieve comparable performance under RGB-D input. Furthermore, FALCON using only RGB input achieves competitive performance compared to Kosmos- VLA with RGB-D input and surpasses it with same input modality by 0.43 in Avg. Len..
Besides, we conduct experiments under varying test-time depth conditions while keeping the train- ing modality fixed. Specifically, we examine two scenarios: (1) model trained only on RGB but evaluate with additional depth input (test w/ d), and (2) model trained on RGB-D but evaluate with- out depth (test w/o d). As summarized in Tab. 5, under ABCD-D setting, providing depth at test-time improves the Avg. Len. from 4.08 to 4.09. Conversely, when depth is omitted at test- time for model trained with RGB-D. performance remains robust. A similar trend is observed in the more challenging ABC-D setting: RGB-only FALCON benefits from test-time depth input, as Avg. Len. increases from 3.91 to 3.95, matching the performance of model trained with full RGB-D.

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Published as a conference paper at ICLR 2026
Table Zero-shot monocular depth estimation with diverse input modalities for ESM on CALVIN benchmark. 1.25: measures the accuracy under 1.25 threshold (percentage of predictions with relative error < 25%). Abs. Rel: computes the average abso- lute relative error between prediction and ground truth.
Method
Depth
Camera
5 < 1.25 (56)
Abs, Rel
VGGT (Wang al,, 2025g)
Ours
x
90.91
8.61
Ours
99.79
0.91
Figure 8: Depth map Visualization.
Ours
99.47
Real-world experiments further validate that incorporating depth and camera poses significantly enhances FALCON's robustness (Fig. 7), increasing task success rates from 60% to 80% scenarios involving objects of varying heights. These findings highlight FALCON's effective utilization of additional geometric information and its adaptability across different sensory modalities.
Embodied Spatial Model. To further investigate the role of ESM, conduct additional zero- shot experiments on CALVIN to evaluate the monocular depth estimation results of ESM. These experiments further validate why ESM effectively leverages additional modality inputs achieve performance gains. As shown in Tab. 6, our ESM achieves performance comparable to VGGT when using only RGB input. Moreover, its performance improves significantly when additional depth or camera pose information is accessible. This demonstrates the inherent strength of FALCON's modality transferability, which stems from the ability of our proposed ESM to seamlessly benefit from diverse 3D modality inputs. Fig, 8 presents ESM predicted depth maps and corresponding error maps (darker colors indicate smaller errors) under different input modalities.
4 RELATED WORKS
3D-Enhanced TBioo-Languagg-Actiion Models. While VLAs (Brohan et al., 2023; Kim et al., 2024: Li et al., 2024b;a; Cheang et al., 2024; Black et al., 2024; Kim al., 2025; Cheang et al., 2025; Zhang et al., 2025; Su et al., 2025a;b) have advanced generalist robotics by leveraging pre- trained VLMs, their 2D nature limits spatial reasoning. Recent works address this by integrating 3D cues through two main approaches: (1) explicit 3D inputs like point clouds (Li et al., 2025; Sun al. 2025), which require specific sensors and lack modality transferability; (2) embedding 3D features into VLM input space (Zhen et al., 2024; Qu et al., 2025), which often disrupts pre-trained alignments and requires costly retraining. FALCON overcomes these limitations by decoupling spatial processing from the VLM, preserving semantic integrity while ensuring strong modality transferability.
Spatial Foundation Models. Recent advancements in deep learning have introduced novel alter- natives to traditional SfM methods. Recently, VGGT (Wang et al., 2025a) proposes multi-view architecture that processes multiple images simultaneously, moving beyond pairwise processing to improve reconstruction consistency and robustness. However, their integration into generalist robot policies remains challenging, often requiring complex feature alignment suffering from informa- tion loss when adapted to action spaces. See comprehensive review related works in Appendix A.2
5 CONCLUSIONS
In this work, we introduce FALCON, a vision-language-actionn model that augments generalist robot policies with robust 3D spatial understanding. FALCON makes three main contributions: (I) inte- gration of spatial tokens from foundation models to provide strong geometric priors; (2) an Em- bodied Spatial Model that optionally incorporates 3D modalities (e.g., depth, camera poses) while preserving RGB-only functionality; and (3) a Spatial-Enhanced Action Head that injects spatial tokens directly into the control policy, avoiding disrupting vision-language alignment within the VLM. Experiments across both simulation and real-world tasks show that FALCON consistently surpasses existing VLA methods, achieving state-of-the-an performance and robustness on spatially demanding tasks.
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ACKNOWLEDGEMENTS
We would like to thank all members at ByteDance Seed Robotics team for their continuous support throughout this project. We also want to extend our gratitude Wenke Xia for his essential assis- tance with the robotic hardware setup, to Haosong Peng for his valuable discussions, and to Hang Li for his leadership of this team.
ETHICS STATEMENT
This work focuses on improving spatial reasoning for robotic manipulation. While our model is trained standard open-source datasets and tested in controlled settings, acknowledge that any Al system may potentially exhibit biases or produce unexpected behaviors, Our research intended for academic exploration only, and emphasize that any such outcomes do not reflect the views of the authors. We support the development of Al technologies that are ethical, safe, and aligned with societal values.
REPRODUCIBILITY STATEMENT
We detail our work the Methodology section (Sec. and describe implementation details the beginning of Sec. and Appendix A.3, A.4, A.5, A.6.
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A APPENDIX
A.1 DECLARATION OF LLM USAGE
In the preparation of this work, the authors used LLM (e.g., GPT-4) in order to improve the read- ability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
A.2 RELATED WORKS
A.2.1 3D-ENIIANCED VISION-LANGUAGE-ACTION MODELS
The development of generalist robot policies has been significantly advanced by VLAs (Brohan et al., 2023; Kim et al., 2024; Li et al., 2024b;a; Cheang et al., 2024; Black et al., 2024; Kim et al., 2025; Cheang al., 2025; Zhang et al., 2025; Su et al., 2025a:b), which leverage large-scale pre-trained VLMs to connect visual and linguistic understanding with action generation. For in- stance, RT-2 (Brohan et al., 2023) and Ope (Kim et al., 2024) fine-tune VLMs on robot data, representing actions as language tokens. While demonstrating impressive instruction-follen following ca- pabilities, these methods operate primarily in the 2D domain, lacking explicit mechanisms for 3D geometric perception, which is a critical limitation in tasks requiring precise spatial understanding. To address this, recent works have integrated 3D perceptual cues into policy learning. One line of search directly consumes explicit 3D representations like point clouds for action prediction, seen in PointVLA (Li et al., 2025) and GeoVLA (Sun et al., 2025). While enhancing geometric aware- ness, these methods rely on explicit 3D inputs and specific sensor configurations, limiting modality transferability when such inputs are unavailable. An alternative strategy embeds 3D features into the VLM's input space, exemplified by 3D-VLA (Zhen et al., 2024), Spatial (Qu et al., 2025), and Evo-O (Lin et al., 2025), but this often disrupts pre-trained vision-language alignment, necessi- tating costly embodied instruction tuning to recover performance. FALCON overcomes these issues by decoupling spatial processing from the VLM, preserving its semantic integrity while achieving strong modality transferability and robust performance across diverse sensory conditions, reducing dependency on specialized sensor setups when deploy in the real-world.
A.2.2 SPATIAL FOUNDATION MODELS
Recent advancements in deep learning have introduced novel alternatives to traditional SfM meth- ods. DUSt3R (Wang et al., 2024) represents a significant deviation from conventional SIM pipelines by predicting point clouds from image pairs without relying geometric constraints inductive biases. Unlike traditional SfM, which depends on keypoint matching and geometric optimization, DUS(3R generates predictions in a shared coordinate frame, enabling robust reconstruction across diverse scenes. This approach addresses several challenges inherent in classical methods, such as sensitivity to initialization and sparse correspondences. Building on this paradigm, several works have proposed variations with distinct architectural innovations. MASt3R (Leroy et al., 2024) im- proves the estimation of the pixel-wise correspondence between image pairs, strengthening the effi- cacy of unconstrained feed-forward models for SfM tasks. CUT3R (Wang et al., 2025b) introduces a recurrent formulation of DUS13R, achieving computational efficiency at the expense of marginal accuracy degradation. More recently, VGGT (Wang al., 2025a) proposes multi-view architec- ture that processes multiple images simultaneously, moving beyond pairwise processing to improve reconstruction consistency and robustness. However, their integration into generalist robot policies remains challenging, often requiring complex feature alignment or suffering from information loss when adapted action spaces.
A.3 TRAINING PARADIGM
A.3.1 FALCON TRAINING PARADIGM
Rather than incorporating spatial information during pre-training, which would significantly in- crease computational cost and complicate optimization, we adopt post-training approach that preserves the original pre- mre-trannig efficiency while enabling efficient integration of 3D geometric cues.
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Let θv, AA Gg and O denote the parameter sets of the VLM V, action predictor ESM G and lightweight adapter D, respectively. The overall training objective is to minimize the expected action prediction loss L (refer to Eq. 2) over the target data distribution:
Stage 1: Feature Space Alignment. In this initial stage, we freeze all pre-trained components ( AA θc) and optimize only the adapter parameters @p. The adapter architecture employs zero- initialized final linear layer, which ensures the spatial tokens Tep initially contribute minimally the fused representation, thereby preserving the pre-trained feature space and ensuring stable optimization. The training objective is:
min min (o. (= (V(O,, D(MaxXoolnng(((4"))
BD
where L.O.,L.A,)~S denotes the expectation over the dataset S of image-language-actinn tuples. This stage facilitates gradual alignment of spatial tokens with the semantic feature space without disrupting pre- vre-trinnd representations.
Stage 2: Joint Feature Refinement. Building upon the aligned features from Stage 1, we unfreeze both the VLM parameters Öy and adapter parameters θp. while keeping 4 and θg frozen. This allows the VLM to adapt its feature representation effectively incorporate spatial information while maintaining linguistic understanding. The optimization objective becomes:
min Bio,LLAA [c (A., (V(O,, L) + ((MaxPooling((((]")))) Op
(8)
This phased approach ensures stable convergence and prevents the spatial features from overwhelm- ing the semantic representations during initial learning phases.
The proposed training strategy offers several advantages: (1) it maintains the integrity of the pre- trained VLA's knowledge while incorporating
spatial capabilities; (2) zero-initialization in Stage 1 ensures training stability and avoids disruptive feature shifts; (3) gradual unfreezing in Stage 2 enables balanced feature adaptation. This paradigm allows FALCON to achieve robust 3D spatial perception while maintaining strong performance across diverse tasks and scenarios.
A.3.2 EMRODIED SPATIAL MODEL TRAINING PARADIGM
ESM is trained following the dataset and preprocessing procedures of VGGT (Wang et al., 2025a). For each training batch, we randomly sample 1-12 frames from a randomly selected scene, resulting total of 24 images per batch. Training is performed using the AdamW optimizer with differenti- ated learning rates: 1e-6 for the large unified transformer backbone and le-5 for the depth, camera, and point heads. We set Bernoulli probability = 66% throughout ESM training. The complete training process requires 16 A100 GPUs and runs for approximately 2 days.
A.4 PARAM-PRRAMETERS AND TRAINING DETAILS
Simulation Benchmarks. As FALCON employs a two-stage post-training strategy, we first pre- train a 2D VLA (denotes as Kosmos-VIAA VLA-21)) the target datasets (CALVIN (Mees et al., 2022), OXE dataset (O" (O'Neill et al., 2024)) without involving the ESM. The pre-training uses a learning rate of 2e-5, a global batch size of 128, and a warmup ratio of 0.25 epochs for CALVIN and 2.5k steps for OXE. Subsequently, we conduct post-training based on the pre-trained weights of Kosmos-VLA- 2D in two stages: Stage uses learning rate of le-4, a global batch size of 128, and warmup. Stage 2 Sayper-parameter, are detailed in Tab. 7.
World Tasks. For real-world evaluation. we initialize the model with pre-trained weights from Kosmos-VLA-2D
VLA-2D (OXE) and ESM. Stage 1 training uses learning rate of Ic-4, a global batch size of 512, and no warmup. Stage hyper-parameters are provided in Tab. 7. We evaluate all VLAS under the following training settings: (1) Base Tasks: models trained separately on three scenarios. (2) Few-shot Adaptation: multi-task training across all four tasks. (3) Sixatial-Prmmpee efficient fine-tuning
two tasks. Baseline models (SpatialVLA (Qu et al., 2025), RoboVLM (Li et al., 2024a)) use the same hyper-parameters as FALCON. For Open VLA-OFT (Kim et al., 2025) (LoRA-only). we use learning rate of 5e-4, LoRA rank 32, chunk size 5, and Base Tasks train for 150k iterations, other two settings for 100k iterations. All VLAs trained on real-world datasets use only the side camera input.
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Across all experiments, both training stages for FALCON use the same number of epochs/iterations, a Constant learning rate scheduler, and the Adam optimizer. All input images (including depth maps) are resized to resolution of 224x224. Besides, we follow (Li et al., 2024a) to set the loss weighting factor = 0.01 for fair comparisons.
Checkpoint Selection. Following the findings of (Li al., 2024a), we note that policy performance does not fully correlate with offline evaluation metrics (e.g., validation loss) due to compounding er- rors in long-horizon rollouts, making checkpoint selection challenging. To ensure fair comparisons, all VLAs are trained for a fixed number of epochs or iterations. Specifically:
On CALVIN, models are trained for 5 epochs with a batch size of 128 truncated trajecto- ries, and the final checkpoint is evaluated.
On SimplerEnv, models are trained for 150K iterations (batch size 128), with evaluation at 10K-interval checkpoints, and the best-performing model is reported.
In Real-World experiments, all models are trained for 30 epochs or same iterations for OLenVL with batch size of 512 truncated trajectories, and only the final check- point is evaluated.
This consistent protocol ensures comparable evaluation across all baselines.
A.5 IMPLEMENTATION DETAILS
As demonstrated in Tab. 7, on the CALVIN benchmark, the VLM receives side and wrist camera images with a history length of 16 frames, while the ESM processes third-view images also with same length historical context. Predictions are made using an LSTM-based action predictor that outputs chunk of = 10 future actions. For SimplerEnv and the real-world benchmark, both the VLM and ESM receive a single-step third-view image, and an MLP-based action predictor predicts an action chunk of length C 5. During inference, we employ two different action execution strategies: for CALVIN and SimplerEnv, the policy executes the ensemble actions before generating the next chunk. In the real-world setup, the entire action chunk is executed at once. FALCON requires 12.8 GB of GPU memory and runs at approximately 57 Hz a single NVIDIA RTX 4090 GPU during real-world evaluation.
Table 7: Hyper-parameters setup of FALCON for different experiments. Abbreviations: Ep: Epochs, Iters: Iterations
Experimcnt
Action Pretictor
Chank Input lupat View
Learning
CALVIN Periomancer (Bé 1)
LSTM
10
Sidk-Wiis
Stde
1236
5e-5 (ABCCDDDDDD
2e.5 (ABC-ID)
Spp
Simpeella Periomance (Thh 2-3)
MILP MILP
Side
Side
175
Rad5
150K hers
Wodd Performonen (Plg 1-5, Pig
1
Side
Side
512
Rpd1
30
CALVIN Ablation (Tab. 4.5, Tal. Tab. 10)
MLP
1
10
Side-Wiitt
Side
128
2e (ABCD (D)
So-5 (ABC-E))
SEp
A.6 BENCHMARK DETAILS
CALVIN (Mees et al., 2022) a simulation benchmark designed for evaluating long-horizon, language-conditioned robotic manipulation. It consists of four scene splits (A. B, C, and D), each representing a distinct environmen configuration and featuring 24k human-telcoperated demonstra- tions annotated with language instruction. Each trajectory less than 64-time steps, which includes 34 pre-defined basic skills: rotate blue block right, move slider right, lift red block slider, place slider, turn off light bulb, turn off led light, push in drawer, lift blue block drawer, close drawer, lift pink block slider, lift pink block table, move slider left, open drawer, turn on light bulb, rotate blue block left, push blue block left, rotate red block right, turn on led light, push pink block right, push red block left, lift blue block table, place in drawer, rotate red block left, push pink block left, lift stacked blocks, 1ift blue block slider, push red block right.
We train and test FALCON and VLA baselines on different training/test splits to fully analyze the capabilities. Standard evaluation protocols such ABC-D and ABCD-+D are employed assess
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the models' generalization capability to unseen environments and its robustness in Long-horizon task compositions. During evaluation, the robot is required to complete a set of consecutive tasks. The metrics are the success rates of finishing these sequential tasks and the average length of achieved tasks (Arg. Len.). All evaluations are implemented on D split, with 1000 rollouts and 5 consecutive sub-tasks for each rollout.
SimplerEnv (Li et al., 2024c) provides a benchmark for evaluating the transfer and generalization capabilities of models trained on large-scale real-world video data. It supports diverse manipulation setups across both the WidowX and Google Robot platforms, incorporating variations in lighting conditions, object textures, color distributions, and camera viewpoints. By faithfully replicating real-world conditions in a simulated environment, SimplerEny enables reproducible and controlled evaluation of robot policies, facilitating rigorous benchmarking under settings that closely mirror private real-world systems such as Bridge V2 (Walke et al., 2023) and Google Robot (Brohan et al., 2022; 2023).
We adopt the following tasks in the WidowX + Bridge setting:
put the spoon on the towel. In this setup, the spoon is positioned at one corner of a square on the tabletop. with the towel placed a different corner. The square has sides measuring 15 cm in length. The orientation of the spoon alternates between horizontal and vertical, requiring the robot to adjust the orientation of its gripper accordingly. This configuration results in total of 24 trials.
put carrot on plate. This setup is similar to put the spoon on the towel, but the spoon replaced with carrot and the towel substituted with a plate.
stack the green block on the yellow block. In this experiment, a green block is positioned at one corner of square on the tabletop, while yellow block is placed at a different corner. Both blocks measure em size. Two square configurations with 10 cm and 20 cm side lengths are used. This setup results in a total of 24 trials.
put eggplant into yellow basket. An eggplant is positioned randomly within the right basin of sink, while a yellow basket is placed in the left basin. The eggplant's placement varies in both location and orientation but is carefully arranged to remain easily graspable, avoiding proximity to the sink's edges. total of 24 trials are conducted under this setup.
For the Google Robot setting, we test the following tasks:
pick coke can. The task assigned to the robot is to pick up an empty Coke can from the table and lift it. Under the standard configuration, the environment is kept free of any distracting elements. The Coke can is arranged in three distinct positions: lying flat horizontally, lying flat vertically, and standing upright. For each of these positions, the can is placed at 25 specific grid points within a defined rectangular area on the table. This setup results in 25 experiments per position, totaling 75 trials across all orientations.
move {objl} near {obj2} In the experiment, set of three objects was arranged on the table in triangular formation. For each trial, one object was assigned the role of the source, another was designated as the target, and the third served as a distractor. This setup resulted in six distinct trials for each triplet and triangular configuration. From total of eight objects-bli plastic bottle, Pepsi can, orange, 7up can, apple, sponge, Coke can, and Redbull can-five triplets were randomly selected. Additionally, two triangular patterns, upright and inverted, were employed. This design produced a total of 60 trials.
(olse/close) (top / middle / bottom) drawer. In this setup, the robot is placed facing a cabinet equipped with three drawers and tasked with opening or closing a specific drawer. This experiment evaluates the robot's capability to handle articulated objects. The robot is positioned at nine distinct locations rectangular area on the floor. With three drawers and two possible actions (opening or closing), the setup results in total of 54 trials.
predefined grid within
open top drawer; place apple into top drawer. In this experiment, the robot tasked with opening the top drawer and transferring an apple from the surface of the cabinet into the drawer. This setup evaluates the robot's ability to execute tasks that require
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multiple sequential actions. The robot is positioned in three distinct locations the floor, while the apple is placed at nine specific grid points on the cabinet surface, resulting in total of 27 trials. At the start, the robot operates under the instruction open the top drawer. Once the robot either signals task completion with "terminate" token or reaches the midpoint of the allotted time, the instruction transitions to directing the robot place the apple into the drawer.
Side Cam RGB
Side Cam Depth
Figure 9: Real-world setup of the xArm 6 robotic system used in the experiments. The system equipped with a side camera that provides both RGB and depth images for visual observation and spatial perception.
Real World Benchmark comprises 1, 1,030 expert trajectories collected through human teleoperation, spanning five distinct robot learning scenarios and individual tasks. These range from simple object interactions, such lift the yellow pepper, to long-horizon sequential activities, such as place the red coke can on the bottom shelf, comprehensively assessing the model's robustness and spatial perception capabilities.
Base Tasks are organized into three scenarios (Dining Table, Bedroom, and Kitchen) containing a total of nine distinct task suites, encompassing language grounding (cluttered scenes with random distractors) and semantic understanding (unseen object poses). Each task is evaluated over 10 dif- ferent scene layouts with 10 trials, resulting in total of 90 rollouts. Besides, for each task, collected 100 demonstration trajectories (except lift the yellow pepper for 50 trajectories). resulting total of 850 trajectories for training.
Few-shot Adaptation includes four challenging tasks selected from the Base Tasks that require more spatial perception capabilities. For each task, we collected 20 demonstration trajectories, resulting in total of 80 trajectories for training. In addition to this base setting (denoted as Simple in Fig. 15), we introduce three unseen variations: Unseen Object, Unseen Background (by changing two different colored tablecloths), and Unseen Task Description, to evaluate the robustness and general- ization of models low-data regimes. Each task is evaluated across 5 different layouts with trials per layout.
Spatial Understanding Capability Evaluations consist of four tasks with varying levels of spatial complexity: two spatial-prompt tasks adapted via efficient fine-tuning (each task we collected 50 demonstrations), two zero-shot tasks, one from Base Tasks involving explicit height variation (pur white cup pink cloth with two 3em blocks below cup), and the other from Few-shot Adaptation featuring objects different sizes (stack blue block on red block with larger block size: 5cm, and smaller block size: 3cm. Regular size for training is 4cm). This suite of tasks is designed further
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investigate the spatial perception capabilities of the FALCON model. Each task is evaluated across 5 different layouts with 2 trials per layout.
The physical setup consists of an xÁrm 6 robot arm equipped with Robotiq parallel gripper and an Intel RealSense D435i depth camera positioned approximately 0.6 meters away to provide a third- person view, as illustrated in Fig. 9. All fine-tuning datasets collected via human teleoperation using a Spacemouse device, sampled at 10Hz. We use absolute Cartesian control as the action space for policy training and deployment (Liu al., 2025b).
A.7 ABLATION STUDY
A.7.1 LOss WEIGHTING FACTORS
Overall
Table 8: Performance comparison of different loss weighting factors on CALVIN benchmark,
Method
Task
CA 0.25)
PALOON (A 0.01)
ARC-D
ABC-D
91.
93.7
8588
869
77.9
774
69.5
70.3
604
622
391
587
30c
Figure 10: Loss curves of FALCON under different weighting factors during train- ing Stage 2 CALVIN benchmark.
For all experiments in our paper, we set À = 0.01 for fair comparisons. To further ablate about the sensitivity of different X, conducted additional experiment for FALCON with X 0.05 under RGB-only input on CALVIN zero-shot setting. As shown in Tab. 8, performance decreased slightly when À increased from 0.01 to 0.05 (Arg. Len. dropped from 3.91 to 3.87). Analysis of the overall train loss curves (Fig. 10) reveals that larger À leads to increased oscillation during training, while the overall convergence trend remains the same, which indicates that the model is not highly sensitive to small variations in X.
A.7.2 LSTM HEAD PARAMETERS
Oveall Loss EThe aneellingh dial largh dai
4hibbrrbrr Heghh
Table 9: Performance comparison of Kosmos- VLA (LSTM) under different parameters CALVIN benchmark.
3:5
Completed a (%)
Asg.
2
LSTM (R--6, C=10)
ABC-DD
96.5
91.4
85.6
78.7
70.7
4.23
LSTM 07-16 C-3)
ABC-D
95.0
19.6
80.3
70,5
02.3
339
LSTM (fEA, (-
ABU-DD
91.
35.3
77.2
704
652
330
Figure 11: Loss curves of Kosmos- VLA (LSTM) under different parameters on CALVIN benchmark.
To systematically evaluate the effects of history length (H) and chunk size (C), we conducted experi- ments with Kosmos-VI.A (RGB-only) using an LST action predictor under the CALVIN ABC-DD setting.
Performance Comparison. We tested three configurations: (a) H=16, C=10, (b) H=16, C=5, and (c) H-8, C=10. The results shown in Tab. 9 demonstrate that H=16, C=10 achieves the strongest per- formance (Avg. Len. 4.23). Reducing either parameter degrades model performance. Specifically, shorter history length (H=8) caused the most significant performance drop (Avg. Len. decreased from 4.23 to 3.90), underscoring that richer historical context is crucial for long-horize tasks in robotic manipulation.
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Training Stability. The overall train loss curves (Fig. 11) reveal that while all configurations tually converge, reducing history length significantly impacts initial training stability. Specifically, H=8, C=10 shows slower descent and greater oscillation in early stages (0-15k steps), whereas ducing chunk size (C from 10 to 5) maintains stable convergence. This indicates that sufficient history length is vital not only for final performance but also for stable optimization.
A.8 POTENTIAL FUTURE WORKS
Table 10: mark.
Performance comparison of wrist camera input for ESM on the CALVIN bench-
Method
Task
Tasks Completed in Row (%)
Avg. Len.
3
4
w/o wrist
ABCD-D
94.0
86.7
80.8
76.4
70.9
4.08
w/ wrist
ABCD-D
94.1
87.2
81.6
77.0
70.6
4.10
The integration of wrist camera images into the ESM further enhances FALCON's performance, as evidenced in Tab. 10. For instance, in the CALVIN ABCD-D setting, the Avg. Len. increases from 4.08 4.10 when wrist images are incorporated. This improvement suggests that multi-view inputs can provide complementary geometric cues. Future work could investigate multi-view camera systems that offer more consistent geometric perspectives, potentially further boosting robustness in diverse sensor configurations.
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A.9 ROLLOUT EXAMPLES IN CALVIN
ABC D
rotaze right
nove slider right
lif: red block slider
in slider
turn 1ightbulb
rotate red block right
Lift pink block slider
EUTP on
oper: dewwer
Figure 12: Rollouts the ABC-> split of the CALVIN benchmark.
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A.10
ROLLOUT EXAMPLES SIMPLERENV
Put Carrot Plate
Put Eggplant in Yellow Basket
Spoon on Towe!
Block
Open The Bettom Drawer
Close The Bottom Drawer
Open The Midcle Drawer
Close The Middle Drawer
Close
Open Apple
Figure 13: Qualitative results for SimplerEnv tasks.
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A.11 ROLLOUT EXAMPLES WORLD TASKS
A.11.1 BASE TASKS ROLLOUTS
lift the yellow pepper
pick banana and place red plate
put carrot in the basket
put white cup pink cloth
stack blue block on red block
open drawer and place bread
close the drawer
place the coke the shelf
place green sprite can the top shelf
Figure 14: Qualitative results Base Tasks.
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A. 11.2 FEW-SHOT ADAPTATION ROLLOUTS AND DETAILED RESULTS
Bosic
Instruction
Colba
dhen ded peepken
CRANT
Rowe
iden
Elonet bone
botor out
beton thek
betion yOa
betto
shaf
guer LON
jaddeeile
Figure 15: Qualitative results for Few-Shot Adaptation tasks.
Setting: Simple
Setting: Unseen Object
Setting: Unseen Hackground
Setting: Unseen Description
77.5
87.5
60.0
A Oun
Dun Sael MY.A (.
A Dus
Sucess Rate
80
Rowiil Ceewikkorr
Success Rate
Sucooss Rate
45.0
Success Rate
55.
60.0
60
50.0
27.5
28.5
45.0
40
15.0
20
15.0
40
27.5
22.5
20
20
A
Methods
Methods
Methods
Methods
Figure 16: Performance comparison of different methods in the Simple setting and four variants.
Table 11: Few-shot Adaptation performance under Simple Settings. We report both the final
success rates (Success) along with the sub-task success rates (e.g., Grasp Block). Stack Block: Stack Blue Block on Red Block. Place Bread: Open Drawer and Place Bread. Place Coke Can
Place the Red Coke Can on the Bottom Shelf. Place Sprite Can: Place the Green Sprite Can on the
Top Shelf.
Method
Block
Can
Sprite Can
Average
Grusp Block
Success
Open Drawer
Success
Graup Can
Success
Grasp Can
Success
OpenV1IA A-FFT (Kim 2025)
30.0%
30.0%
30.0%
30.0%
40.0%
30.0%%
60.0%%
20.05
27.5%
RoboVLM 2024a)
Vp L (Qu et al,, 2025)
60.0%
50.0%
90.05
70.0%
50.0%
40.0%
80.0%
50.0%
50.0%%
FALCON (ours)
90.0%
80.0%
100.0%
100.0%
80.0%
80.0%
90.0%
90.0%
87.5%
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Table 12: Few-shot Adaptation performance under Unseen Objects variants. We report both the
final success rates (Success) along with the sub-task success rates (e.g., Grasp Block). Stack Block:
Stack Orange Block on Green Block. Place Tennis Ball: Open Drawer and Place Tennis Ball. Place
Strawberry Can: Place the Strawberry Juice Can the Bottom Shelf. Place Grape Can Place the Grape Juice Can on the Top Shelf.
Method
Stack Block
Place Temnis Hall
Place Strawberry Can
Place Grape Can
Average
Grasp Block
Success
Open Drawer
Success
Grasp Can
Success
Grasp Can
Success
UpenVLAA-OF LL- 2025)
20.0%
0.1%%
60.0%
30.0%
10.0%
60.0%
20.0%
RoboVLM (Li et al,, 2024a)
40.0%
0.0%
60.0%
0.0%
60.0%
20.0%
80.0%
40.0%
15.0%
SpatialVLA (Qu et 2025)
30.0%
10.0%
70.0%
20.0%
70.0%
30.0%
60.0%
50.0%
27.5%
FALCON (ours)
40.0%
40.0%
100.0%
80.0%
60.0%
60.0%
60.0%
60.0%
60.0%
Table 13: Few-shot Adaptation performance under Unseen Background variants. We report
both the final success rates (Success) along with the sub-task success rates (e.g., Grasp Block).
Stack Block: Stack Blue Block on Red Block. Place Bread: Open Drawer and Place Bread. Place Coke Can Place the Red Coke Can on the Bottom Shelf. Place Sprite Can
Place the Green Sprite
Can on the Top Shelf.
Method
Stack Block
Place Bread
Place Coke Can
Place Sprite Can
Averuge
Grasp Block
Success
Open Drawer
Success
Grasp Can
Grasp
Success
OpenVLA OFT (Kim al., 2025)
20.0%
10.0%
20.0%
20.0%
0.0%
0.0%
20.0%
20.0%
12.5%
RoboVLM (1- et al,, 20246)
20.0%
0.0%
20,056
20.055
20.0%
0.0%
20.0%
20.055
10.0%
SpaiiaVLA (Qu al., 2025)
40.0%
20.0%
20.0%
FALCON (ours)
40.0%
40.0%
60.0%
60.0%
40.0%
40.0%
40.0%
20.0%
40.0%
Table 14: Few-shot Adaptation performance under Unseen Background variants. We report
both the final success rates (Success) along with the sub-task success rates (e.g., Grasp Block).
Stack Block: Stack Blue Block on Red Block. Place Bread: Open Drawer and Place Bread. Place Coke Can Place the Red Coke Can on the Bottom Shelf. Place Sprite Can Place the Green Sprite
Can on the Top Shelf.
Method
Stack Block
Place Bread
Place Coke Can
Place Sprite Can
Average
Grasp Block
Success
Open Drawer
Success
Grasp Can
Succoss
Grasp Can
Success
OpenVLA OFT (Kim al., 2025)
40.0%
10.0%
20.0%
20.0%
20.0%
10.0%
40.0%
20.0%
15.0%
RoboV/LM al., 2024a)
20.0%
40.0%
Spa (Qu et al., 2025)
30.0%
50.0%
40.0%
30.0%
50.0%
40.0%
50.0%
30.0%
32.5%
FALCON (ours)
40.0%
40.0%
60.0%
60.0%
60.0%
60.0%
60.0%
40.0%
50.0%
Table 15: Few-shot Adaptation performance under Unseen Task Description variants. We report both the final success rates (Success) along with the sub-task success rates (e.g., Put Cube). Put Cube: Put the Blue Cube on Top of the Red Cube. Put Bread: Unlock the Drawer and Put the Bread Inside. Put Coke Can: Put the Red Coke Can on the Lower Shelf. Position Sprite Can Position the Green Sprite Can on the Upper Shelf.
Method
Put Cube
Put Bread
Put Coke Can
Position Sprite Can
Avernge
Grasp Block
Success
Open Drawer
Success
Grasp Can
Grasp
Suceess
OpenVLA OFT (Kim et al.,, 2025)
40.0%
40.0%
20.0%
20.0%
40.0%
20.0%
40.0%
10.0%
22.5%
RaboVLM LM (1. et al., 2024a)
60.0%
40.0%
100.0%
60.05
100.0%
$0.0%
100.0%
40.0%
55.0%
Spoiia (Qu al., 2025)
80.0%
40.0%
40.0%
45.0 45.0%
FALCON (ours)
70.0%
70.0%
100.0%
100.0%
80.0%
80.0%
80.0%
60.0%
77.5%
26

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Published as a conference paper at ICLR 2026
A.11.3 SPATIAL UNDERSTANDING CAPABILITY EVALUATION ROLLOUTS
place the fruit that is dooeet the robot the outting board
put the tomato on the plate that between the blue and red car
put white cup on pink cloth (cup height change)
stack blue block on red block (regular)
stack blue black on red block (larger)
stock blue block on red block (smaller)
Figure 17: Qualitative results for Spatial Understanding Capability Evaluations.
27


Reviews:
[{"review_id": "84Rqq4tQCC", "reviewer": "Reviewer_xvie", "summary": "The paper proposes a new VLA model, FALCON, which tries to incorporate the spatial 3D information into the VLA implicitly. The goal is to be able to utilize 3D information, if available, or otherwise be robust to different modality setups (full 3D, depth only, camera only, no 3D). The idea to achieve this is to utilize 3D feature information in models like VGGT, and additionally supply 3D information to the encoders of these models if they are available. Then, these spatial features are added to the action features before passing to the action head. This lets the action head utilize the 3D information for predicting actions. The paper conducts experiments on CALVIN, SIMPLER and real-world tasks. They show that the proposed method works better than prior 3D policies and 2D/3D VLAs, and the ablations show that the model works well in various modality setups and can leverage 3D information to improve performance, when available.", "questions": "As I describe in the weakness section, I suggest testing the proposed methods on richer settings like RL-Bench and compare with explicit 3D methods to eke out if the proposed implicit 3D representation is enough.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is very well-written and easy to follow\n- The model design and ideas are clearly novel and overall make sense\n- The problem setting is interesting and important — to utilize 3D information when available, but be robust in cases when it is not available. \n- The ablations are adequate and clearly show that the model is robust to various modality scenarios and can benefit form 3D data, when available.", "weaknesses": "My main concern is that the evaluation benchmarks chosen, especially the simulation ones are weak and do not necessarily benefit from 3D understanding and information. \n\nFor instance, the paper makes this statement: “FALCON surpasses previous methods that rely on ground-truth point clouds (e.g., 3DDP (Ze et al., 2024) and 3D Diffuser Actor (Ke et al., 2024)), improving the Avg.\nLen. by 4.13 and 1.05, respectively. This provides clear evidence of the effectiveness of our implicit\nspatial information integration strategy” This statement appears wrong because the ablations in table-4 show that actually on Calvin, the performance is similar between RGB-only or RGB-D versions of their model, showing that additional 3D input doesn’t help much in this benchmark. And while it can be argued that FALCON’s implicit geometry is enough and that’s why explicit 3D doesn’t help, that is probably not true as the same ablations show that in real-world benchmarks performance increase from 60 to 80%. This makes me think that it is just that Calvin tasks don’t benefit much from 3D. Thus, it is unclear if the gains over prior methods especially the explicit 3D models like 3DDA is due to the implicit spatial information strategy or more parameters / better data. Concretely speaking, it is not convincing if implicit 3D, as proposed by this method, is enough or we need more richer 3D-aware models. \n\nIt would be more convincing to show results on RL-Bench which contain several high precision tasks and where 3D policies (like 3DDA) have worked significantly better than 2D policies. Experiments there would help understand if the implicit 3D is strong enough or not.  \n\n\n(Minor) \nTable-5 looks like it is testing monocular depth estimation and camera pose estimation — however that is not obvious from the description. It might help to explicitly say this", "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": "ohlcSp6K1V", "reviewer": "Reviewer_x5su", "summary": "The paper introduces FALCON, a spatial to action paradigm that injects richer and more representative 3D spatial tokens into VLAs through an improved injection scheme. The approach combines a 2D VLM backbone, an embodied spatial model for 3D encoding, and a spatial-enhanced action head for fusion. Experiments on CALVIN, SimplerEnv, and real-world tasks show better performance and improved modality transferability.", "questions": "1. As weakness 3, can you explain why freeze ΘA and ΘG in stage 2?\n2. In Eq. 6, λ balances MSE and BCE losses, but its selection criteria are not described. Is λ chosen empirically, through grid search, or via adaptive re-weighting? How sensitive is the model’s convergence and performance to λ?\n3. The ESM uses multi-task supervision, how each supervision type contributes to downstream VLA performance? What happens if one component is removed?\n4. The pipeline adopts a two-stage post-training approach rather than joint training, how about joint end-to-end optimization? Why do two-stage?\n5. Is the sampling probability p in Eq. 4 fixed over training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper focus on spatial reasoning in VLAs, which is a crucial research problem.\n2. The experiments cover multiple benchmarks on both simulation and real-world. \n3. Clear qualitative visualizations are provided to support the contribution.\n4. The pipeline is complete, which combines 2D VLM backbone, embodied spatial model for 3D encoding, and spatial-enhanced action head for fusion.", "weaknesses": "1. The core concept of this paper “3D spatial tokens” is ill-defined. This paper claims that 3D spatial tokens provide robust geometric priors. However, in line 132, the depth information is optional, and in line 224, the depth and/or pose is randomly injected. The resulting spatial tokens Tspl derives from DINO and an encoder with cross/self-attention. Thus, these spatial tokens are not truly 3D, they are 2D correlations.\n2. In Eq. 4, they randomly inject depth and/or pose. The proposed stochastic conditioning breaks the well-defined mapping of Eq. 1. So, the expectations or joint distribution forms of F should be redefined.\n3. The training pipeline requires further explanation. In appendix A.3, Stage 1 freezes all pre-trained components and optimize only the adapter parameters ΘD. The input for D is fixed (as ESM output is frozen), this training does not really learn cross-space alignment, it merely compresses the ESM's output. Stage 2 unfreezes VLM parameters ΘV and adapter parameters ΘD, while keeps ΘA and ΘG frozen. If my understanding is correct, here although VLM can learn new representations, the action control component is not adapted to new spatial features. So, the model cannot learn how spatial features influence actions, thus the Loss L cannot truly drive the entire system to learn the mapping from semantics and spatial information to action.", "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": "6tdkn4jY4D", "reviewer": "Reviewer_Byfb", "summary": "FALCON is a VLA that also accepts optional depth and camera pose inputs. These additional modalities can help with fine-grained manipulation tasks.  The approach will benefit from continued improvement in pointmap and camera pose estimation, and that technology is itself rapidly improving beginning with Dust3r). \n\nThe proposed approach is to start with an off-the-shelf RGB + text VLA, Kosmos, and add a large downstream transformer that accepts the Kosmos original output plus optional additional depth and camera pose modalities. The majority of the paper discusses the design decisions around this second network, which the authors call the \"Embodied Spatial Module\" (ESM). Many of the design decisions build on VGGT (not worried about novelty; stating this to be clear). \n\nResults on CALVIN and SimpleBench allow comparisons to other VLAs, and results look strong. There are also some experiments on a real Franka.\n\nOverall I feel the paper makes a useful contribution to the community. The empirical results are convincing but there are some small claims that do not feel fully substantiated and the paper could use some polish. With presentational changes I could be convinced to raise my score.", "questions": "* How come the authors add together imd + depth tokens (equation 4) and use this strategy, instead of concatenating and either dropping tokens or masking them (like in flow matching/diffusion)?\n* When training and evaluating FALCON, do the authors use the depth and camera poses from the simulator/Realsense + IK? Or is VGGT used at test-time?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "### Overall\nOverall I feel the paper makes a useful empirical contribution -- especially if weights and code are released.\n\n* Strong empirical results\n* Straightforward method (this is a good thing!) with clear use case\n* Clear description of the approach\n* Good ablations of different design decisions\n* Clear figures\n\nPlease don't take the length of this section an indicator. Mainly, these are the strengths that I'm seeing -- hopefully these are the same things the authors see too.", "weaknesses": "### Presentation\nI found the paper a bit hard to read in its current form, and I feel the paper's impact may be limited by the writing and presentation.  Many parts of the paper could be tightened through removing unnecessary adjectives and reorganizing sections. \n\n**Organization:**\nIn the current version, even basic information about the training approach, including the losses, learning algorithm, and datasets are left entirely to the appendix. In contrast, the method section focuses almost exclusively on the architecture and has multiple paragraphs devoted to equations ablations the authors will run (e.g. Sec 2.4). \n\nIn my opinion, this section could be reduced to equation 5 plus a little context, and the ablations could be moved to the experiments or appendix. This would make room for information about the training of FALCON.\n\n**Wording**\nParts of the main paper are especially wordy and flowery.  Phrases like \"Seamlessly integrating\" are very common from LLMs but are hard to evaluate technically -- used three times in the paper. Wording like this could be cut to make space for some of the interesting ablations and details from the supplementary. I actually preferred the more direct writing in the supplementary!\n\n---\n### Experiments\n\n**Cross-modality generalization**\nThere are lots of claims and experiments about cross-modality generalization, but I didn't find the experiments especially convincing and I don't feel these claims add much to the paper in their current form. Or maybe the authors could clarify the definition of \"modality transferability\" in the paper. \n\nI would like modality transferability experiments showing that a task could be learned (ABCD->D) entirely without depth, and then benefit from depth at test-time (and/or be learned with depth, and not suffer too much from losing depth at test-time) -- these would be more convincing than the current table 5.\n\nHowever, I wanted to check my understanding about table 5, which the authors say L430-431\n> \"ESM achieves performance comparable to VGGT when using only RGB input. Moreover, its performance improves significantly when additional depth or camera pose information is accessible. This demonstrates the inherent strength of FALCON’s modality transferability\"\n\nVGGT is a pointmap estimation model (no actions), and based on the metrics, it looks like this is performance for depth estimation? So the ESM's depth estimation on CALVIN (which the model was trained on), is similar to VGGT (which VGGT wasn't trained on). And then when the GT depth is passed to the ESM, the depth estimation is close to perfect?", "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": "4eeNxLoe7w", "reviewer": "Reviewer_jhgj", "summary": "The paper proposes FALCON, a Vision-Language-Action (VLA) architecture designed to enhance 3D spatial reasoning by injecting rich 3D spatial tokens—derived from a Spatial Foundation Model—into a dedicated \"Spatial-Enhanced Action Head,\" rather than forcing them through the VLM (Vision Language Model) backbone. It introduces an \"Embodied Spatial Model\" (ESM) that (i) can operate RGB-only via VGGT-like spatial priors and (ii) optionally fuses depth and pose information without retraining or architectural changes, using a stochastic 3D-conditioning scheme to preserve robustness across modalities. Fusion strategies between the semantic action token and the spatial token were studied (cross-attention, FiLM-gated, element-wise addition), with element-wise addition selected for stability and generalization. Extensive experiments across CALVIN, SimplerEnv (WidowX/Google Robot), and 11 real-world tasks show that FALCON achieves SOTA or highly competitive results. Ablations demonstrate its modality transferability and support the claim that injecting tokens into the action head helps preserve VLM alignment.", "questions": "1. Could you quantify the representation drift in the VLM when spatial tokens are injected into the VLM vs. the action head (e.g., using CKA or feature-space distance to compare text-image alignment before/after injection)?\n\n2. How sensitive are results to the Bernoulli probabilities p for depth/pose injection? Please provide a sweep and show their effect on RGB-only inference performance.\n\n3. Can you provide success vs. number of real-world demonstrations curves for at least two tasks to substantiate few-shot efficiency?\n\n4. Have you tried swapping VGGT with DUSt3R/MASt3R/CUT3R as the ESM backbone, holding everything else fixed? Was there any change in performance or modality transferability?\n\n5. For long-horizon tasks (like CALVIN), what is the effect of the LSTM history length H and chunk size C on stability and success rate?\n\n6. What is the runtime overhead (e.g., latency or FPS) of enabling the depth/pose inputs for the ESM compared to RGB-only inference on a single GPU?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. Clear problem framing: Identifies the 2D–3D gap in VLA spatial reasoning and the modality transferability and alignment issues with prior explicit 3D pipelines and weak 3D cues.\n\n2. Architectural novelty at the integration point: Instead of injecting spatial tokens into the VLM (which can disturb alignment), FALCON routes rich spatial tokens to a Spatial-Enhanced Action Head. This design decision is well-motivated and experimentally supported by a key ablation.\n\n3. Practical modality transferability: RGB-only works via spatial foundation priors; optional depth/pose yields measurable gains, with a principled stochastic-conditioning scheme during training.\n\n4. Extensive experiments: Strong results on CALVIN and SimplerEnv, clear real-world evaluation across 11 tasks, and targeted spatial capability tests (scale/height/abstract spatial prompts). Ablations on fusion strategies and token injection positions are informative.\n\n5. Reproducibility-minded details: Training stages, losses, data splits, and hyperparameters are detailed; the ESM depth quality is quantitatively analyzed.", "weaknesses": "1. Positioning vs. contemporaneous 3D-enhanced VLAs: The related work section is broad, but could better differentiate FALCON from the latest VLA methods employing similar spatial decoupling strategies (e.g., avoiding direct modification of the VLM backbone). Clearer articulation of FALCON's specific innovations compared to potentially similar works and a systematic comparison of training costs (e.g., compute requirements) and data dependencies are recommended.\n\n2. Theoretical grounding of fusion choice: The paper empirically finds \"element-wise addition\" to be the optimal fusion strategy but lacks theoretical support explaining this counter-intuitive result. Why does this non-parametric method outperform more complex parametric fusions (like Cross-Attention)? Further analysis, for instance, by quantifying the alignment drift (Δ) in the VLM's representation space under different fusion strategies, could provide deeper insight into why element-wise addition better preserves generalization.\n\n3. Quantifying robustness: The proposed stochastic conditioning training strategy is an interesting design for enhancing modal robustness. However, the paper lacks a quantitative analysis of this robustness. It is recommended to add experiments that formally evaluate model performance under missing or noisy modal information (e.g., varying ratios r of valid depth pixels), potentially by plotting performance degradation curves.\n\n4. Sensitivity to spatial prior source: FALCON's ESM module draws inspiration from VGGT. Given the existence of other spatial foundation models (like DUSt3R and MASt3R, cited in the paper), how sensitive is FALCON's performance to the specific choice of spatial prior model? Ablation studies, such as replacing the ESM backbone with DUSt3R or MASt3R, are suggested to test the generality of the FALCON architecture and assess potential domain adaptation issues.\n\n5. Data efficiency substantiation: When evaluating few-shot generalization, the paper primarily reports final performance based on a fixed number (e.g., 20) of demonstrations. To provide a more comprehensive view of data efficiency, presenting learning curves—showing how success rate varies with the number of training demonstrations (e.g., 1, 5, 10, 20 demos)—is recommended to better illustrate the model's ability to learn from small samples.", "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)