OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning
Authors: Fanqi Lin
Ruiqian Nai
Yingdong Hu
Jiacheng You
Junming Zhao
Yang Gao
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language-Action Models
Embodied Reasoning
Primary area: applications to robotics, autonomy, planning
TL;DR: We present UniVLA, a single unified vision-language-action model capable of both reasoning and acting, and can adaptively decide when to reason and when to act.

Abstract: General-purpose robots capable of performing diverse tasks require synergistic reasoning and acting capabilities.
However, recent dual-system approaches, which separate high-level reasoning from low-level acting, often suffer from challenges such as limited mutual understanding of capabilities between systems and latency issues. 
This paper introduces OneTwoVLA, a single unified vision-language-action model that can perform both acting (System One) and reasoning (System Two). Crucially, OneTwoVLA adaptively switches between two modes: explicitly reasoning at critical moments during task execution, and generating actions based on the most recent reasoning at other times.
To further unlock OneTwoVLA's reasoning and generalization capabilities, we design a scalable pipeline for synthesizing embodied reasoning-centric vision-language data, used for co-training with robot data. We validate OneTwoVLA's effectiveness through extensive experiments, highlighting its superior performance across four key capabilities: long-horizon task planning, error detection and recovery, natural human-robot interaction, and generalizable visual grounding, enabling the model to perform long-horizon, highly dexterous manipulation tasks such as making hotpot or mixing cocktails.

Published as a conference paper at ICLR 2026
ONETWOVLA: A UNIFIED VISION-LANGUAGE- ACTION MODEL WITH ADAPTIVE REASONING
Fanqi Lin 1,2.3+
Ruiqian Nai
Yingdong Hu H
Jiacheng You!.2 1.2
Junming !
Yang Gao 1,2,3f
TTsinghua University
2 Shanghai Qi Zhi Institute
$ SSpirit.Al
*Equal contribution
iCorresponding author
Project page: https://one-wwo--a..iiuuubb
ABSTRACT
General-purpose robots capable of performing diverse tasks require synergistic reasoning and acting capabilities. However, recent dual-system approaches, which separate high-level reasoning from low-level acting, often suffer from challenges such as limited mutual understanding of capabilities between systems and latency issues. This paper introduces One OneoTwVVLA a single unified vision-language- action model that can perform both acting (System One) and reasoning (System Two). Crucially, OneTTwoVL TLo adaptively switches between two modes: explicitly reasoning critical moments during task execution, and generating actions based on the most recent reasoning at other times. To further unlock OneTwoVVe VLA's rea- soning and generalization capabilities, we design a scalable pipeline for synthe- sizing embodied crasoning-centre vision-language data, used for co-training with robot data. We validate OneTwo effectiveness through extensive experi- ments, highlighting its superior performance across four key capabilities: long- horizon task planning, eтrоr detection and recovery, natural human-robor inter- action, and generalizable visual grounding, enabling the model to perform long- horizon, highly dexterous manipulation tasks such as making hotpot or mixing cocktails. Project page: http://one-to-vll.giiieee
Reason
Act
Act
Act
Reason
Act
Reason
Scene
Grip
AT
Grip
Grip
Scene:
AR
Grip
Scenc:
Plan:
Plan: ...
Plan
Historical Summary
Historical Bummary. I've added syrup. Now need to: Add orange juice.
Grip
Historical Summary:
cone Nothing.
Syrup & juice added.
Now need
syrup.
AT
AR
Grip
AT
AR
Grip
AT
ARR
Grip
AT
AR
Grip
Now I Ined to: Add vodia.
OneTwoVLA
Make
Vodia Surriee"
Figure 1: Overview. OneTwoVLA is single unified vision- Aanguage-action model capable of both reasoning and acting. Crucially, One can adaptively reason at critical moments during exccution (c.g., upon completing subtasks, detecting errors, or requiring human inputs), while generating actions at other times.
INTRODUCTION
distinctive characteristic of human physical intelligence the ability to both reason and act (Varela Francisco et al.) 1991 Anderson 2003). Crucially, these processes not separate but flexibly interleaved, creating powerful synergy reesonnnannn guides our actions, while actions provide feedback that informs subsequent reasoning. Consider someone preparing dish: reason- ing enables them to develop a comprehensive understanding of the scene and goal (e.g., interpreting the recipe, planning the sequence of steps), while acting corresponds the physical execution (e.g..

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Published as a conference paper at ICLR 2026
chopping, mixing) that grounds abstract reasoning in the real world. This paper aims to imbue robots with a similar synergistic relationship between reasoning and acting.
Current approaches (Ahn al. 2022; Hu et al. 2023 Shi et al., 2025 Team et al.,
2025 Figure
2025) often draw inspiration from Kahneman's 's dual-system framework (Kahneman, 2011 Typ-
vision- vision-language models (VLMs) (Beyer et al. 2024 Karamcheti et al.) 2024), is dedicated to slow high-level reasoning, generating intermediate
ically, System Two, such as
reasoning contents. Meanwhile, a System One, such as waan--llagggggatiinnoo models (VLAs) (Kim at. 2024 Black et al. 2024 Bjorck et al. 2025), translates these intermediate contents into precise low-level robot actions. However, this explicit decoupling results in both systems lacking mutual awareness of each other's capabilities; System Two may produce intermediate contents that System One cannot execute (Shi al. 2025). Furthermore, in real-world deployment, issues such as latency may cause System Two to respond belatedly, providing outdated or irrelevant guidance.
We argue that achieving stronger reasoning-acting synergy demands unified model. Indeed, the recent trend towards unifying capabilities within single models is proving crucial for advancing AI (Yao al., 2023 OpenAT 2025), and we believe this approach holds particular promise for robot learning. In light of this, we introduce OneTwwVVLA, a single unified vision-languaage-ccioo model capable of both acting (System One) and reasoning (System Two). Importantly, it adaptively determines when to engage each mode. As shown in Fig. [ Two triggers natural language reasoning at key steps like completing a subtask, detecting an error, or requiring human input producing outputs such as scene descriptions, task plans, historical summaries, and next-step instructions. Otherwise, it generates actions informed by its most recent reasoning outputs. key advantage of this unified model is its natural support for co-training with dision-language data, sig- nificantly enhancing reasoning and generalization. To facilitate this, we develop a scalable pipeline for synthesizing high-quality, embodied reasoning-centric lision-language data.
Our extensive experiments validate TneTwoVLA's effectiveness, demonstrating its ability to inte- grate diverse capabilities within single model: 1) sons to formulate, track, and dynamically adjust task plans based on execution feedback, signifi- cantly outperforming flat VI_A (by 30%) and dual-system VI.A (by 24%) baselines. Vision-language co-training further enables generalization to novel task instructions (e.g., planning coffee prepara- tion for "Help me stay awake"). 2) Error detection and recovery: One detects execution errors in real time, reasons about corrective strategies, and performs agile recovery actions. 3) Natu- ral human-rohe robot interaction: OneTwo adjusts actions immediately upon human intervention and proactively seeks clarification when faced with ambiguity. 4) Generalizable visual ground- ing: Two exhibits superior understanding of spatial relationships, object attributes, and semantic features, even generalizing to objects absent from its robot training data.
task planning: OneTww rea-
2 RELATED WORK
Vision-Languaag-Acttion Models.
Initialized from pre-trained vision-language models
(VLMs) (Chen et al. 2023 Beyer et al.
2024, Liu et al. 2024a Wang et al. 2024 Lu et al. 2024).
vision-language-aettoo action models (VLAs)
Driess et al.) 2023 Brohan et al. 2023] Kim et al.
2024
Black et al. 2024, Pertsch et al. 2025 Team et al., 2025 Bjorck et al. 2025
Wen et al. 2025
Huang et al. 2025) have emerged as a promising approach for building general-purpose robots.
These VLAs, trained on large robot datasets (Mandlekar et al. 2018 Gupta et al.
201 Dasari et al.)
2019 Cabi et al. 2019: Fang et al., 2020 Brohan et al. [022 Jang et al. 2022 Walke et al. 2023
O'Neill et al.
et al. 2224]LLn et al. (2024) can handle a wide range of real-world
manipulation tasks. However, these VLAs exhibit limited reasoning capabilities (Hu al. 2023 Shi
ct al. 2025 Bjorck et al.) 2025), showing vulnerability when confronted with long-horizon tasks or complex dynamic environments. Morcover, their generalization performance degrades substantially when facing novel objects instructions outside the training distribution (Kim et al. 2024 Black et al. 2024 In contrast, our work enhances reasoning and generalization capabilities through a unified model architecture and co-training framework.
Reasoning for Robot Control. Previous works (Stone et al. 2023: H Huang et al. 2023 Li et al.
2023a Belkhale et al.) 2024, Liu et al.) 2024b; (Shi al. 2024: Zhi al. [204]] Zhao et al.) 2025 Li et al. 2025) demonstrate that high-level reasoning can enhance lewel policy performan in robot control. In particular, many studies (Ahn et al. 2022
Huang al. 2024 Hu et al. 2023 Shi
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Task Times with Reasoning
Algorithm Inference Pipeline of OneTwoVLA
Excoution
Require:
VLA model Ne. language instruction E
1: E 0, 1 +- initial image, R
250
Simul. lime
2: while R # "Task Finished" do
3:
DT Te. To.decide(- mm 1i R)
Task time
200
176
1.99
184
4:
if DT = [BOR] then
150
5:
R ~ Tg.reason
T R)
6:
R <- ft, 1dt &- else if DT [BOA] then
100
7:
8: A Ne. act((- L L, Rt)
Execute At
0
10:
end if
mg
Dual-System
OneTwoVLA
11:
f 1-111
Figure Task completion times on Tomato-Bgg.
12:
end while
For experimental settings, see Sec. 4.1
et al. 2025 Team et al. 2025 Bjorck et al. 2025
Figure 2025) explore dual-system frameworks,
where a foundation model (e.g., VLM) serves as System Two to perform high-level reasoning, while low-level policy operates as System One to generate actions based on reasoning outputs. While this dual-system framework proves effective for accomplishing long-horizon manipulation tasks, it inherently suffers from limitations such as the two systems lacking mutual awareness of each other's capabilities (Shi et al. 2025) as well as latency issues with System Two. Recently, To.5 (Intelligence et al., (2255) employs a single model to predict subtask before each action, but this reasoning is simple and infformation-limitee reasoning at every step, it significantly impacts inference efficiency (Zawalski et al. (024). To mitigate this, ECoT-Lite (Chen et al. 2025) avoids producing reasoning during test time, but this leads to degraded performance and prevents effective human-robot interaction. To address these limitations, we propose a unified model capable of adaptively deciding when to reason versus when to act, allowing for both informative reasoning and efficient execution. For related work co- training for robot learning, please refer to the Appendix B
this inflexible paradigm generates extensive
3 METHOD
In this section, first introduce the framework of One in Sec. 3.1 including its formu- lation, adaptive inference, and model instantiation. We then describe how we curate robot data to enable synergistic reasoning and acting in Sec. 3.2 Finally, we present scalable pipeline for synthesizing vision-language data enriched with embodied reasoning in Sec. 3.3
3.1 FRAMEWORK OF ONETWOVLA
The central problem investigated in this work is how to develop robotic control policy ne capable of both reasoning and acting, with the critical ability to autonomously decide at each timestep t whether to reason or act. Formally, the policy operates in two modes. When in reasoning mode, the policy takes as input the current image observations from multiple cameras _!" ,!....,," 1td (denoted as I! where n is the number of cameras), the reference images from the latest reasoning timestep 1/ ... : Ne (denoted as 7l.x which introduces observation histories to prevent ambiguous states), the language instruction f, and the latest reasoning content R. The policy performs reasoning in the form of textual output, generating updated reasoning content ft ~ To(-11,30 1 1 E, R). Sec. 3.2 provides further details on the specific content of this reasoning process. In acting mode, the policy additionally incorporates the robot's 's proprioceptive state 8 and generates an action chunk A, based on the latest reasoning content: A, ~ ( [1 I L, R, st).
Problem Formulation.
present the detailed process of how OneT- woVLA autonomously decides whether to reason or act. We introduce two special decision tokens (DT): beginning of reasoning ([BOR] ) and beginning of action ( ([BOA] ].. Given the prefix (com- prising image observations 1, reference images / 117 instruction E, and the latest reasoning con- tent R), the model first predicts either [BOR]
Adaptive Inference of One In [
[BOA]. When [BCR] is predicted, the model enters reasoning mode and generates textual reasoning content R until producing an end of sentence ([EOS]) token. Conversely, when [BOA] is predicted, the model enters acting mode and directly generates the action chunk A,. This adaptive framework yields high inference efficiency: during task execution, the model operates primarily in acting mode, invoking reasoning only at few crit-
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Published as a conference paper at ICLR 2026
ical steps, which adds only minimal overhead. As shown in Fig. for completing long-horizon task, OneTTwoVLA achieves total times that match those of a flat VLA without language reason- ing (To (Black et al. (2044). In contrast, a Dual-System approach that "always reasons" (c.g., Hi Robot (Shi et al. (025), ViLa (Hu et al. (2033) incurs sigmificant latency due to extensive reasoning. Moreover, our framework inherently supports error recovery and human-robot interaction: when the policy detects an error (e.g., failing to grasp an object), it autonomously enters reasoning mode to determine a corrective strategy and execute agile recovery actions. When human interaction occurs, any interaction text will be consistently added to the language instruction & in subsequent steps.
Model Instantiation. OLL is designed to be general, allowing most existing VLAs be integrated with minimal modifications. For a specific instance, we employ wo (Black et al.] 2024) as the base VLA, which demon- strates strong performance across vari- ous tasks. The of Ty auto-regressively generates textual reasoning during inference and is su- pervised via a cross-entropy loss during training. To model complex continuous action distributions, we inherit the action expert architecture from To and train it using a flow matching loss (Lipman et al.
Boone: juice
Reasoning Mode: (SOR)
insed to ju juics
Action Chunk
Acting Model (SOK]
LEL
-1,0 a22
Vision Language Model
Action Expert
model
"Rlese
"Scene: need add syrup.
of Vodha Servioe
"Ghv me the object used degingg in sol
"Scene trowel is Food to pick up trowel"
MEE
Oba
Ret.
Instruction
Reasoning
State
Notse
Figure 3: Inference flow of Two in two modes.
2022 Liu 2022). OneTwoVLA's inference flow is de-
tailed in Fig. 3 See Appendix F.2 for more training details.
3.2 CURATING ROBOT DATA WITH EMBODIED REASONING
Most existing robotic manipulation datasets consist primarily of observation-action pairs and lack associated reasoning information. To address this gap, we introduce a novel robot data format. For given task, we first collect demonstration trajectories provided by human experts. Subsequently, each trajectory segmented into a sequence of intervals. There are two types of intervals: reason- ing intervals, which capture key steps requiring model reasoning (e.g., upon completing subtasks, detecting errors, or when human interaction is required), which we further annotate with textual reasoning content; and acting intervals, in which the model primarily learns predict actions based on observations and the latest reasoning content. During training, we supervise the decision tokens according the interval type and the freshness of reasoning content R. In reasoning intervals, the ground-truth decision token is [BOR] if the current reasoning R is stale (i.e., needs updating); once R has been updated, the ground truth becomes [BOA]. In acting intervals, the model always learns to predict [BCA]. See Appendix F. for more details.
Next, elaborate the embodied reasoning content. As shown in Fig. 4 left, it consists of four components: 1) a detailed scene description, primarily focusing on the locations of task-relevant objects; 2) a high-level plan that outlines the sequential steps to accomplish the task; 3) a concise historical summary to keep the model informed about the task's 's progress; and 4) the immediate next step that the robot needs execute. This comprehensive reasoning content encourages the model to understand the visual world, learn high-level planning, and track task progress. Furthermore, to equip the policy with error detection and recovery capabilities, we specifically collect and label robot data focused on recovery from failure states. To enable natural human-robot interaction, we annotate certain intervals of the demonstrations with interaction context (e.g., the robot's question and the human's 's answer shown in Fig. lefft..
We design a two-stage fully automated pipeline for labeling reasoning intervals and generating their reasoning content. In the first stage, interval annotation, we predefine a high-level plan with K ordered subtasks P- (pI, ........ ............. From each demonstration, we uniformly subsample N 32 frames S = [T.,, J.v and prompt Gemini 2.5 to identify reasoning intervals immediately after each subtask completion, yielding K + 1 intervals R {(r3. r In the second stage, reasoning
content generation, for each interval (r),r5) E R we take its midpoint frame and denote it as I5-
We then construct four reasoning fields: 1) a scene description Dj generated by Gemini from 1;: 2) the high-level plan P; 3) a historical summary H, (pi. (pi....,PP)) PP;]; and 4) the next step X, = Pj+1-
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Instructions:
Instructions:
Spatial: Get the item between teacups. Semantic: Get the item for pouring tea. Attribute: Hand object with spout. Pick up teapot between cups.
Spatial: Get the item in front of the scale. Semantic: Pass me the French food. Attribute: want that crusty baked good.
Instruction: Make me
Reasoning:
Reasoning: Pick up bread on board.
cup cocktail.
Robol: What kind?
Human: Mojito.
Reasoning:
Instruction:
Instruction:
Scene description: Lime & mint added. Rum is placed Soda at Plan: 1. Add lime mini. 2. Add rum. 3. Add soda. Historical Summary: I've added lime & mint, Now I need to: rum.
Clean up table.
Bake cookies.
Reasoning:
Reasoning:
Plan: 1. Put compass
Plan: 1. Roll out white
in drawer, 2. Put black
dough. 2. Cut shapes
pencil holder. 3. Put
from dough. 3. Bake
nuler in drawer 4. Fold
cookles. 4. Take out
drawing. 5. Put folded drawing drawer.
baked cookies from oven.
Figure 4: Left. Example of robot data with reasoning content. The reasoning content comprises a scene de- scription, high-level plan, a historical summary, and the next-step instruction. Interaction texts (c.g., the robot question and the human answer) are appended after the instruction. Right. Examples of synthetic embod- ied reasoiing-centric vision-language data. The top two examples illustrate visual grounding tasks, while the bottom two demonstrate long-horizon tasks. More examples are provided in Appendix
The tuple (D,, P. H, X3) serves as the reasoning content for interval j. This automated pipeline produces high-quality annotations. For example, in the Tomato-Esge task, 81.5% of intervals are judged correct by human annotators, and 83.3% of scene descriptions are deemed reasonable. Additional prompts and extensions to error recovery are provided in Appendix
3.3 SCALABLE SYNTHESIS OF VISION-LANGUAGER DATA WITH EMBODIED REASONING
The carefully curated robot data described in Sec. 3.2 allows the model to directly learn the de- sired task, but its size scales linearly with the costly human effort, making large dataset creation impractical. To endow our model with stronger generalization and the ability to cope with highly varied scenarios, we leverage off- off-the-shelf foundation models and design a fully scalable pipeline that synthesizes vision- language data enriched with embodied reasoning. This pipeline consists of three steps: 1) We prompt Gemini 2.5 Pro to generate diverse textual descriptions of tabletop layouts featuring common household items; 2) Based on these textual descriptions, we employ the text-to- image generation model FL.UX. 1-dev (Labs) 2024) to synthesize high-quality images depicting the tabletop layouts. We further augment the synthetic images by randomly applying fisheye distortion or compositing robot gripper with adaptive brightness, making the visuals more closely resemble real robot observations; 3) Finally, we utilize Gemini again to generate task instructions and corre- sponding reasoning contents for each synthesized image. Through this pipeline, we automatically generated 16,000 data samples, with examples shown in Fig. rrghht.
The generated task instructions fall into two categories: 1) Visual grounding tasks (Shridhar & Hsu 2018 Bhat et al. 2024| Kim al.) 2023). where the instruction implicitly refers an object in the image through spatial relationships, attributes, or semantic features. The accompanying reasoning must reveal the object's explicit name and, optionally, its location; 2) Long- Long-horizon tasks, where the instruction describes an extended, multi-step objective. The reasoning must supply a high-level, step-by-step plan for completing the task. For part of the dataset, we additionally instruct Gemini to incorporate elements of numan-robee interaction into the plan. A detailed quality analysis of the synthetic data, along with additional examples, is provided in Appendix B
4 EXPERIMENTS
In this section, we evaluate OneTwoVLA through extensive real-world experiments, demonstrat- ing its superior performance in versatile capabilities: long-horizon task planning (Sec. 4.1 error detection and recovery (Sec. 4.2). natural human-robot interaction (Sec. 4.3), and visual ground- ing (Sec. (.4). Additionally, we show that co-training with our synthetic lasion-l data yields generalizable behaviors and open- world visual grounding capabilities unseen scenarios and tasks.
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Long- Hong-Horizon Planning
Error Detection Recovery
Human-Robot Interaction
Generalizable Planning
Task Toma Ba-tto---ggg Egg
Task; Ho tpot
Task: Cocktail
Task: Give me an jey cola.
Instruction: "Prepare a plate of tormato-egg scramble."
Reasoning:
Human: "Use lemon vodka instead of orange vodka."
Reasoning:
Scene Description:
Beef and mushrooms are in strainer. But fail to grasp the strainer.
Plan: 1. Open fridge. 2. Get ioy
cola. 3. Close fridge.
Reasoning:
Reasoning:
Scene Description: Oil is located at Cooking machine
Plan: Y Dip beef. 2 Dip
Scene Description:
Task: Pass me an empty plate.
mushrooms.3 3. Cook, then lift strainer.
syrup. 2.g/ Add juice. 3. Add lemon vodke.
Plan:
Tomatoes and eggs
are at ... Spoon is at....
Now need to: Retract, align with strainer, to grasp.
Now need to: Put orange vodka down, add lemon vodka.
Plan: Add oil cooking machine. Add tomatoes and eggs. Grab spoon and wait for cooking. 4, Scoop out dish.
Execution:
Execution:
Reasoning:
Now need to: Add to cooking machine.
Plan: 1, Move grapes. 2. Move
apple. 3. Pick up plate.
Figure 5: Task illustrations and reasoning examples. In the three leftmost columns, we present three chal- lenging, long-horizon manipulation tasks. Completing these tasks requires not only planning abilities, but also error detection and recovery capabilities, as well as the the ability to interact naturally with humans. In the rightmost column, we demonstrate two tasks drawn from our experiments on generalizable planning. For every task, we include a sample of the model's 's reasoning content. See Appendix Door additional reasoning examples.
4.1 LONG-HORIZON TASK PLANNING
Hardware. We utilize two robot platforms. The primary platform consists of single 7-DoF Franka arm equipped with a parallel jaw gripper. A wrist-mounted GoPro camera with fisheye lens provides wide field-of-view observations. Most of our experiments conducted using this setup. Addition- ally, we employ a dual-arm platform featuring two 6-DoF ARX arms with three cameras (two wrist and one base), primarily for generalizable planning experiments. See Appendix Hor further details.
Long-horizon Tasks. We design three challenging long-horizon tasks (shown in Fig. 5) each re- quiring the robot understand the scene, plan accordingly, accurately track task progress, and generate precise actions throughout execution. We briefly describe these tasks here, with more de- tails provided in Appendix C.T 1) Sommato-Egg: The robot pours oil followed by tomato and egg liquid into a cooking machine Once cooking completes, it uses a spoon to scoop the scramble onto a plate-a contact-rich action demanding fine precision. 2) Hotpot: Four plates containing differ- ent food items are placed on the table. and their relative ordering is randomized. The robot must sequentially dip beef and one vegetable type, precisely place them into a strainer, and finally lift the juji, or Vodka Sunri ie), each requiring 3-4 steps of ingredient pouring. The robot must distinguish be- tween nearly ten visually similar ingredients and pour accurately. For all tasks, the initial placement of all manipulated objects is randomized within a 10 x 10 cm² area.
strainer. 3) Cocktail: The robot mixes one of three cocktails (Mo j iito, Mountain
Baselines. We compare OneTwoVLLA against two baselines: 1) state-of-the-art VLA model To (Black et al. 2024), which does not perform reasoning. To ensure fair comparison, we fine- tune To on the same dataset used for training OneTwwo VLA, and 2) dual-system approach inspired by Hi Robot (Shi et al., 2025). in which Gemini 2.5 Pro serves as the high-level System Two that decomposes complex instructions into sequences of atomic commands (i.e., the next step field in OneTWoVLA's reasoning content). In practice, we invoke System Two at fixed intervals (i.e., effec- tively "always reasoning"). ). For System One, we annotate our dataset with atomic commands and fine-tune To to execute the commands produced by System Two.
Experimental Results. As shown in Fig. 6 One achieves an average success rate of 87% across the three challenging tasks, outperforming To by 30% and the dual-system approach by 24%. VLL consistently generates correct plans, accurately tracks task progress, and outputs precise actions. In contrast, lacking explicit reasoning and historical context, To some- times loses track of its current step such staying stuck at the initial position when preparing Mo jito or repeatedly picking up beef in the Hotpot task. We also observe that explicit reasoning facilitates more fine- -nggiineedd action learning; To sometimes struggles grasp ingredients precisely in Hotpot task or scoops too lightly in the Tomato -ggg task, whereas TneTwo performs these delicate actions accurately. Regarding the dual-system approach, we found limitations arising
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Long-Horizon Task Planning
Generalizable Planning
100
07%
Success Rate (%)
Rate
35%
2%
5%
10%
Torato Egg
Horpet
Cocktall
Avg. OneTwoVLA
Getliyy Cola
Empty Piate
Toll Use
Prepare Drinks
Avg.
System
OneTwoVLA
OneTwoVLA-VL
Figure 6: Left: evaluation results on long-horizon tasks. OneTwoVLA excels in long-horizon task planning compared to baselines. Right: evaluation results on generalizable planning tasks. By co-training with synthetic vision-languee language data, TwoVA-L further enhances its generalization to novel tasks. In both figures, each method is evaluated over 20 trials per task.
from the lack of mutual awareness between the two systems' capabilities. System Two occasionally outputs atomic commands that are infeasible for System One to execute (e.g., instructing to add green onion in the Tomato-FFa task when none is present). Additionally, the significant inference latency of Gemini 2.5 Pro may prevent System Two from promptly updating its reasoning content, causing System One to encounter out-of-distribution states during execution.
Generalizable Planning. We investigate how co-training with uision-language (VL) data can improve OneTwoLL'' ability generalize in task planning. Specifically, we collect additional demonstration data for various atomic skills (e.g.. pick, place, open, etc.) across two robot plat- forms. We then co-train Two on these robot data together with the with 16,000 VL sam- ples synthesized by the pipeline described in Sec. 3.3 3.3 We denote this variant as OneTTwoVLA-VE while the model trained exclusively on robot data denoted as OneTwo VLA. During evaluation, the policy receives instructions that never appear in the robot data (c.g.. Fig. 5 last column). We test on four challenging tasks that demand deep commonsense reasoning (see Appendix
C.2 for details).
As shown in Fig. 6 right, OneTwoVLA-VL exhibits strong generalization, transferring knowledge from VL data to robot control. The model proactively searches for non-visible cola by opening the refrigerator in Get Icy Cola, handles complex spatial relationships and occlusion by first removing fruits before retrieving the plate in Empty Plate, recognizes the need for a tool and uses a nearby stick to sweep distant objects within reach in Tool Use. Furthermore, it exhibits sophisticated scene-aware human intent understanding "Help me stay awake", kale juice for "I want something healthy". .. In contrast, To executes random atomic skills when faced with such novel tasks, and OneTwoVLA without VL co-training produces entirely incorrect plans- exhibiting only minimal generalizable planning abilities.
Prepare Drinks, preparing coffee for
4.2 ERROR DETECTION AND RECOVERY
Recovering from mistakes is a critical ca- pability for general-purpose robots. OneT- woVLA can detect eттоrs in real-time, rapidly reason about recovery strategies, and subsequently generate corrective ac- tions learned from collected robot recovery data. Table [ presents quantitative com- parison of error recovery performance on two tasks: Hotpot and Toma to- In the Hotpot task, the robot occasionally fails to grasp the strainer due to misalignment. We there- fore collect 200 demonstrations containing recovery actions (600 demonstrations in total). After retraining, One reasons to retract, reposition to align with the strainer and try grasping again, subsequently succeeding in lifting it up. In contrast, To frequently ignores errors and contin- ues to lift the gripper despite not having grasped the strainer. In the T task, sometimes the oil bottle slips from the gripper while pouring; total). OneTwoVLA recognizes the error, reasons to adjust its grasp for increased firmness and retry the action. However, the dual-system approach fails to respond promptly due to latency issues. Sys- tem Two only alerts that the oil bottle is not grasped after the robot has already reached the pouring pose, by which time recovery is hard because the robot has entered out-of out-o--distributin state.
Hotpot Tomato-Eggl
Total
OneTwoVLA|
5/6
3/4
8 10 (80.0%)
3/7
5/7
8/ 14 (57.1%)
Dual-System
4/5
3/7
7/ (58.3%)
Table I: Error Detection and Recovery Results. Values are reported as successful recoveries/ / # error occurrences.
collect 100 recovery demonstrations (200 in
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4.3 NATURAL JUMAN-ROBOR INTERACTION
Deploying robots in human-centric scenarios requires natural interaction with peo- ple. We conduct 10 human-robot interactions for OneTwoVLA and the Dual-System baseline (To is omitted as it cannot generate language outputs) on the Hotpot and Cocktail tasks, with subtask-level interaction results shown in Table 2 Due to its adaptive nature and explicit rea- soning process, OneTwoVLA is able to en- gage with humans in natural way seam- lessly handling human interventions and proac- tively seek clarification when faced with am- biguities. For example, in the Hotpot task, when a human interrupts by requesting. "Could you also dip another vegetable for me?" One'T- WoVLA immediately responds by clarifying, "Sure! Would you like green bok choy, enoki mushrooms, or cabbage?" In the Cocktail task, when the robot is preparing a Vodka Sunrise and the human interrupts with, "I don't It want or- ange vodka, I want lemon-flavored one," OneTwoVLA immediately reasons that it needs to put down the orange vodka, retrieve the lemon vodka, and generate action sequences that align with the human's 's intent. In contrast, the dual-system approach frequently loses context during interaction and struggles maintain coherent reasoning process, merely picking up the lemon vodka without continuing to prepare the cocktail in the example above.
Hotpot Cocktail
Total
TwoVLA] 10/
10/ 10 20/ (100%)
Dual-System 8/10
5/ 10 13 / 20 (65%)
Table Human- Robot Interaction Results. Each en-
try is reported as # successes / # interactions.
Generalizable Human-Robot Interaction. We further evaluate generalization on the Hotpot and Cocktail 1 tasks by testing 20 novel interaction scenarios that never appear in the robot training data. With rision-languag data co-training, OneTwoVLA- VL achieves a 72.5% success rate. the Hotpot task, WLo--VV demonstrates proactive reasoning by asking "Which plate of meat would you like me cook?" when given the instruction "cook meat" with two meat plates present. It also effectively handles unseen dynamic interruptions - when picking up enoki mushrooms, if interrupted with, "I don't want enoki mushrooms, please cook some green bok choy instead," immediately switches to executing the new instruction. In contrast, TneoooVLA without VL. data co-training fail to interpret such unseen interaction commands, exhibiting poor generalization.
4.4 ENHANCED VISUAL GROUNDING
Single-Eav
Open- World
Grounding objects in language instructions to the visual world is a prerequisite for robots accomplish more complex tasks. We cat- egorize visual grounding into three key as- pects (Shridhar & Hsu 2018 Bhat et al. 2024] Kim et al., 2023) Shridhar et al.) 2020): spa- tial relationships. object attributes, and seman- tic features. To validate OneTwo ef- fectiveness in these aspects, we design ex- periments where instruction following requires non-trivial object grounding capabilities. Fur- thermore, to demonstrate the impact of our syn- thetic vision- lision-language data, we conduct exper- iments in open-world settings where diverse items and environments pose additional chal- lenges. The specific experimental settings are described below (shown in Fig. 77
Instruction:
Instruction:
"Get object for computer tasks."
"Hand the woven object
with wide brim."
Reasoning:
Reasoning:
Black mouse for computer tasks. need to pick up black mouse to the left of cube, which
Straw hat is the woven object
with wide brim. I need to pick up straw hat to the left
arching shape that fits the palm.
back of teddy bear, which provides sun protection.
Figure 7: Illustrations of visual grounding tasks. In the Single setting, we provide task instruc- tions that require understanding of spatial relation- ships, object attributes, or semantic features. the Open-Worle setting, we further evaluate the model's generalizable visual grounding capabilities.
1) Single-Enns Four objects randomly arranged a tabletop in single environment. We collect 50 picking-up demonstrations for each object using the UMI (Chi et al. 2024) device, total- ing 200 demonstrations. For testing, perform 40 trials per method in the same environment using the same four objects. 2) Open-World: We collect demonstrations in 16 diverse in-the-we wild en- vironments, totaling 933 valid demonstrations using the UMI device. Each demonstration involves moving the gripper to a randomly selected object within the scene, collectively including 180 dis- tinct household items. For testing, we evaluate each method across 8 unseen environments, testing
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5 times per environment, each time randomly selecting one from 20 objects: 5 objects seen in robot data, 10 objects unseen in robot data but present in synthetic vision-language data, and 5 objects unseen in either dataset. In both settings, training and test instructions refer to target objects using their names or through spatial relationships, attributes, or semantic features. Our annotated reason- ing explicitly identifies the target object's name and includes additional information about it. We compare three methods: To, OneTwoVLA, and OneTTwVVLA-V trained exclusively robot data, whereas One is additionally co-trained with 16,000 synthetic vision-language samples. Further details are provided in Appendix C.3
Both xo and OneTwoVLLA are
Explicit reasoning facilitates visual grounding, In the Single-Enr -nv setting, as shown in Table 3 TooVW achieves success rate of 78%, signif-
Single -nnv Openn World
No Tne
5%
3%
icantly outperforming To- which has a success rate of only 5%. In most cases, VLL accurately interprets spatial relationships, object attributes, and semantic features described in the instructions, rea- sons about the correct object, and then successfully picks up the target object. In stark contrast, Ty con- sistently fails to comprehend the instructions, even when the target object is explicitly named. To typ- ically extends the gripper forward aimlessly or randomly picks up the closest object. This clear performance gap demonstrates that explicitly learning to reason helps the model truly understand the visual world rather than attempting to find shortcuts to overfit actions. Moreover, we find that the reasoning content also aids the model in fitting actions, as evidenced by mo 's action mean squared error (MSE) on the validation set being 62% higher than One'wooLA'ss.
78%
8%
OmeTwoVVLA-VI.
88%
73%
Table 3: Evaluation results on visual ground- ing tasks. OneA exhibits strong visual grounding capabilities, and co-training with VL data further enhances its generalization.
Reatoning-centric vision-language data enables generalizable visual grounding. In the World setting, OneTwoVLA-VL achieves a 73% success rate, significantly outperform- ing both One and ma. In most cases, Two can correctly handle objects unseen in the robot data but present in vision-language (VL) data, effectively transferring commonsense knowledge from VL data to the robot policy. Remarkably. OncTwoVLA-VV generalizes even to novel objects that appear in neither the robot nor VL training data (c.g., Sprite, GoPro). We at- tribute this exceptional generalization capability to VL data co-training, which better activates web knowledge already encoded in the pretrained vision-language model. In contrast, OneTWwoVLA and To frequently exhibit aimless reaching behaviors - even for objects present in the training data indicating that they merely overfit to action training data without developing genuine understanding of the visual environment in this complex and diverse setting.
CONCLUSION, LIMITATIONS AND FUTURE WORK
In this paper, we present OneTwwoLL a single unified model capable of both reasoning and acting, and adaptively switching between these two modes. This synergy is enabled by our meticulously designed framework and reasoning enrriched robot data curation. Moreover, we propose scal- able pipeline for synthesizing embodied casoning-centrie vision-language data to further enhance the model's 's reasoning and generalization capabilities. Extensive experiments demonstrate OneT- VLL'''s 's superior performance across four key abilities: long-horizon task planning, error detection and recovery, natural human-robot interaction, and generalizable visual grounding.
There are several limitations that future work can address. First, OneTwoVLA relies on hand- selected heuristics to determine which steps require reasoning. Analogous to supervised fine-tuning (SFT) in LLMs, this strategy aligns the policy with a set of candidate reasoning steps and content; however, achieving more optimal reasoning likely requires reinforcement learning (RL.). Future work could investigate RL-baaedd training to further strengthen the reasoning capabilities of VIA models. Second, although our adaptive framework allows the model to reason only at few critical steps during task execution, the robot still needs to pause for two to three seconds while reasoning occurs. Future research could explore the design of asynchronous architectures, enabling simulta- neous reasoning and action generation. Third, we have not optimized action inference efficiency. As our single unified model scales larger parameter sizes, the time cost of action inference may become a bottleneck. We anticipate that incorporating advanced inference techniques from the LLM literature could help accelerate action inference in future work. Finally, due to resource constraints, we only investigate the effect of high-quality synthetic vision-language data on VIA reasoning ca- pabilities. Future work could explore the impact of vasion-language data from various sources.
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ACKNOWLEDGMENTS
This research was conducted with the support of the Shanghai Qi Zhi Institute & Spirit AI Innovation Program and the Tsinghua University Dushi Program. Funding and support for this work were also provided by the Tsinghua University- Sivversit-KKyystonn Keystone Electrical (Zhejiang) Co.,Ltd Joint Research Center for Embodied Multimodal Artificial Intelligence (JCEMAI).
We thank Tong Zhang, Chuan Wen, Weirui Ye, Weijun Dong, Shengjie Wang. Chengbo Yuan, Boyuan Zheng, Haoxu Huang, Yihang Hu, and Yuyang Liu for valuable discussions. We also thank the ARX team for prompt assistance with robot hardware and the Xiongan Al Institute for their support.
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APPENDIX
Please visit our website to view robot rollout videos: https:/one-too-Vaagitttbbbi
A LARGE LANGUAGE MODELS USAGE STATEMENT
We used large language models only for light grammar checking and language polishing. All content was written by the authors.
B MORE RELATED WORK
Co-training for Robot Learning. Co-training with data from diverse sources has been shown to
benefit robot learning (Vuong et al.
2023 Drisss et al., 2023
Li et al.
2023b
Nasiriany et al,
2024
Hejna al. 2024 Yang al.
2024 Doshi et al.] 2024 Yuan et al.
2024
Maddukuri al.
2025
In particular, several prior works (Brohan et al.
2023 Mu et al.
2023 7hu et al. 2025
Zhou al. 2025 explore co-training robot policies with action-free vision- languge data alongside
robot data, demonstrating improvements in policy generalization. However, these methods (Brohan
al. 2023 Mu et al., 2023) Zhouu et al. 2025) typically either rely existing vision-language
datasets, which suffer from limited quality due to their significant domain gap from robot application
scenarios; or manually collect vision-language datasets (Zhu al., 2025), which are inherently limited in size and difficult to scale up. To address these limitations, we propose a scalable pipeline for synthesizing vision- laiguuge data rich in embodied reasoning. Our pipeline ensures both high quality and scalability, significantly enhancing policy's 's reasoning and generalization capabilities.
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C TASKS AND EVALUATIONS
In this section, we provide a detailed description of the tasks and evaluations.
C.I LONG-HORIZON TASKS
Figure 8: Execution processes of three long-horizon tasks: Tomato o Eggg, Hotpot, and Cocktail (ex-
emplified by Mountain. Fuji preparation).
Fig. [ shows the complete execution progress of the three long-horizon tasks. Detailed descriptions of these tasks are follows:
1) Tomato-Egen The robot first pours oil, then tomato and egg liquid into a cooking machine. Once cooking is finished, the robot picks up spoon hanging on rack, scoops out the tomato- egg scramble, transfers it onto a plate, and finally places the spoon into the cooking machine. We observe that sometimes the robot fails to grip the oil bottle firmly enough, causing it to slip from the gripper. We collect dedicated recovery data for re-grasping the oil bottle more securely after it has slipped. This enables the robot to automatically perform this recovery if encounters a bottle slip during testing. We collect 200 robot demonstrations for this task.
2) Hotpot: Four plates containing beef, green bok choy, enoki mushrooms, and cabbage are placed on table with randomized relative positions. A hotpot with a strainer is positioned to the right of the plates. For each test, the human instructs the robot to dip beef and one type of vegetable. The robot must accurately pick up the ingredients sequentially, place them in the strainer, wait for them cook. and then lift the strainer. Notably, for OneTwoVLA and the dual-system approach, in of the experiments, the initial instruction is only to dip the beef. While waiting for the beef to cook, the human interacts with the robot saying, "Could you also dip another vegetable for me?", requiring the robot to ask, "Sure! Would you like green bok choy, enoki mushrooms, or cabbage?" Following the human's specification, the robot then proceeds to dip the requested vegetable. This interaction step is omitted for To due to its lack of text output capabilities. Furthermore, we observe instances where the robot fails to grasp the strainer. To address this, we specifically collect recovery data for correcting misaligned grasps. This enables the robot to automatically perform this recovery if it fails to pick up the strainer during testing. We collect 600 robot demonstrations for this task.
3) Cocktail: The robot is instructed to prepare of three cocktails: Mojito, Mountain Fuji, or Vodka Sunrise. Each cocktail requires pouring 3-4 different ingredients. For OneT- woVLA and the dual-system approach, in 10 trials, the initial human instruction is general: "Make me a cocktail." The robot must clarify by asking: "Which cocktail would you like?", and then pro- ceed based the human's 's specific cocktail choice. This interaction step is again omitted for mp- Additionally, during separate Vodka Sunrise trials, the human interrupts with, "I don't 't want orange vodka, want lemon-flavored one," requiring the robot put down the orange vodka and pick up lemon vodka instead. We collect 100 robot demonstrations for each type of cocktail, totaling 300 demonstrations.
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C.2 GENERALIZABLE PLANNING TASKS
Figure 9: Execution processes of four generalizable planning tasks: Get Icy Cola, Empty Plate, Prepare Drinks (exemplified by kale juice preparation) and Tool Use.
We collect 2,000 robot demonstrations using the single-arm Franka system and dual-arm ARX sys- tem. Each demonstration belongs to one category of atomic skill, including pick, place, move, open, close, and pour. The task instructions and corresponding reasoning contents for these demonstra- tions focus on short-horizon atomic skills. Training solely on this data limits the model's generaliz- able long-horizon planning capabilities. OneTwoVLA LLA overcomes this limitation through co-training with our synthesized embodied reasoning-centric vision-language data, which equips it to general- ize previously unseen tasks. Fig- shows the complete execution progress of these unseen tasks. Detailed descriptions of these tasks are as follows:
of icy cola." The challenge is that cola can not directly visible in the scene. The robot must infer that "icy cola" implies the cola is stored in the fridge and therefore plan the necessary steps open the fridge, locate the cola, and retrieve it.
1) Get Icy Cola: The instruction is "Get
2) Empty Plate:
The instruction is "Pass me an empty plate". However, the plate in the scene not empty, it contains apples and grapes. The robot needs remove each fruit from the plate before finally picking up the empty plate.
3) Tool Use: The instruction is "Pick up the cocoa powder can, which is out of reach". The primary difficulty here is that the target object is not within the robot's direct reach. The robot must recognize the need for tool (a nearby stick), plan to first grasp the stick, use it sweep the distant cocoa powder can within reach, and only then proceed to pick up the can.
4) Prepare Drinks: The robot needs plan and prepare appropriate drinks based intent: such as coconut latte for "Help me stay awake," kale kale juice for "I want something healthy," and a blue mood cocktail for "I'm feeling down." This task requires scene-aware intent understanding capability.
C.3 VISUAL GROUNDING TASKS
Task descriptions be found in Sec. 4.4 In the Single-Env setting, each robot demonstra- tion is paired with 11 instruction-reasoning pairs. These instructions refer to target objects using their names (2 instances), spatial relationships (3 instances), attributes (3 instances), features (3 instances). In the Open-Worl setting, each demonstration includes a total of 17 instruction-reassoingg pairs, broken down 2 using direct names, 5 using spatial relationships, using attributes, and using semantic features. All instruction instruction-reasoningg pairs are first generated with Gemini 2.5 Pro and then verified by human annotators.
semantic
During testing, we evaluate each method 40 times in both settings. This consists of 10 tests for each reference type. Table presents the experimental results broken down by these four types.
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Here list the objects used in visual grounding tasks. The Single-Env task uses four objects: blue cube, eggplant toy, coconut water bottle, and black mouse. For the Open-Wor world task evalua- tion, we use the following objects (shown in Fig. [O):
1) objects seen in robot data: flower, mouse, cardholder, tissue, and glasses case.
2) 10 objects unseen in robot data but present in synthetic vision-language data: globe, teddy bear, straw hat, binoculars, trowel, croissant, map, magnifying glass, VR headset, lantern.
3) 5 objects unseen in either dataset: GoPro, Sprite, Starbucks Coffee, HDMI cable, Captain Amer- ica model.
Fig. I1 displays the 16 training environments for the Open-World task, while Fig. 12 evaluation environments.
shows the 8
Single Env
Open- Worlld
Name
Spatial
Attribute
Semantic
Total
Name
Spatial
Attribute
Semantic
Total
OneTwoVLA-VL
10/10
8/10
8/10
9/10
35/40
8/10
6/10
7/10
8/10
29/40
OneTwoVLA
10/10
5/10
31/40
2/40
0/10
1/10
3/40
30
2/10
0/10
0/10
0/10
2/40
1/10
0/10
0/10
0/10
1/40
Table Experimental results for the visual grounding tasks. Results are broken down by the four instruction reference types: direct names, spatial relationships. object attributes, semantic features.
Objects seen in robot data
PROSUN
Objects seen in vision-language data
Objects unseen in either dataset
10
Figure 10: Objects for task evaluation.
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Figure 11: Training environments for Open-World visual grounding task.
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Figure 12: Evaluation environments for Open visual grounding task.
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D MORE REASONING EXAMPLES
Detailed TweTTwoVL reasoning examples during task execution are presented in this section. These include examples for Long-horizon task planning (Table 55, generalizable planning (Table (6. error detection and recovery (Table 7. natural human-robot interaction (Table 8). Single- Env visual
grounding (Table D. and Open World visual grounding (Table T0
We provide prompt templates for annotating reasoning intervals (Fig. 13 and for generating reason- ing content (Fig. (4).
Our template can readily extend to error detection/recoovrry and human-robot interaction (HRI) via plan-augmentation step. Given the original high-level plan P (PL...-,Pk] ,K)) construct augmented plan P by inserting explicit substeps that represent anticipated recovery events and in- teraction turns (e.g.. "detect failure," "retract," "retry," "ask question," "receive answer"). We use P to prompt Gemini in both stages (i) interval annotation and (ii) reasoning-contee generation so that reasoning intervals and their fields align with these events.
Error detection and recovery. Using P encourages the annotator to place reasoning intervals around failure and recovery micro-steps (e.g., failed grasp followed by a retraction). During as- sembly, however, we map back to the original plan P. The reasoning tuple remains (Dj. P. Hj. Xj), where H, = render (PI, .......,.. (Pi..... :P)) over P. If failure occurs on Pk. we set the next step to a retry ac- tion conditioned on pt: (e.g., X3- = retry(pk)) )), optionally preceded by retraction, without altering P itself.
Human-robot interaction. In we insert explicit interaction substeps, such as the robot asking a question and the human providing an answer (e.g., "Which cocktail should make?" - "Mo- jito"). During assembly, extend the reasoning tuple to (Dj, Pj. H,, Xj, Qj, Aj): where Q, is robot's question and A, is the human's 's answer (empty if no interaction). The plan is updated around the dialogue: before the interaction
provisional plan pj and after observing A, we synthesize a conditioned plan Ppost that replaces the remaining substeps; thus P, = Pfre before the dialogue and P3 ppost after. The historical summary H2 records the newly acquired informa- tion (i.e., A,), and the next step X, is conditioned on A, (e.g., proceed to gather ingredients for Mojito). Interaction boundaries are marked as reasoning intervals during annotation.
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Instruction: Dip the beef and the green bok choy.
Reasoning:
The beef is located on the bottom-right plate. The green bok choy is on the top-right plate. The pot is in the center-right area of the table and contains hotpot strainer.
Scene description:
Plan: 1. Put the beef into the hotpot strainer. 2. Put the green bok choy into the hotpot strainer. 3. Wait for the ingredients to cook and then pick up the hotpot strainer.
What have done: Nothing.
Now need to: Put the beef into the hotpot strainer.
Instruction: Make me plate of tomato-egg scramble.
Reasoning:
The bottle is located at front of table. The cooking machine is on the left side of the table.
Scene description:
Plan: 1. Pour the oil into the cooking machine. 2. Pour the tomatoes and egg liquid into the cooking machine. 3. Wait for the cooking process to finish, then pick up the spoon. 4. Scoop out the tomato-egg scramble, transfer it onto the plate, and place the spoon into the cooking machine.
What have done: Nothing.
Now need to: Pour the oil into the cooking machine.
Instruction: Make a cup of Mountain Fuji cocktail.
Reasoning:
The grange-flavoree vodka is the second from the left on the bottom shelf. The Blue Curacao is the second from the right on the bottom shelf. The lemon juice is the second from the left the top shelf. The yogurt is located in the middle of the top shelf. Plan: 1. Pour the orange-flavored vodka into the glass. 2. Pour the Blue Curacao into the glass. Pour the lemon juice into the glass. 4, Pour the yogurt into the glass.
Scene description:
What have done: Nothing.
Now need to: Pour the orange-flavored vodka into the glass.
Table Reasoning examples for long-horizon task planning.
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Instruction: Give me icy cola.
Reasoning:
Scene description:
The cola is out of sight, maybe in the fridge.
Plan: 1. Open the fridge door. 2. Pick up the icy cola bottle and place it on the table. 3. Close the fridge door.
Instruction: Pass
empty plate.
Reasoning:
The grapes and apple are on the plate. Plan: 1. Pick up the grapes and place them on table. 2. Pick up apple and place it on the table. 3. Pick up the empty plate.
Scene description:
Instruction: I'm exhausted
you make drink keep awake?
Reasoning:
need to make a coconut latte.
Plan: 1. Add thick coconut milk to the coffee cup. 2. Pour espresso into the
cup. 3. Drop in ice cubes.
Instruction: I've been eating clean and need nutritious drink.
Reasoning:
I need to make glass of kale juice with some kale leaves and ice cubes. Plan: 1. Pour kalejuice juice into the cup. 2. Put kale leaf in the cup. Add ice
cubes the cup.
Instruction: I'm feeling kind of down today. Can you fix me a drink?
Reasoning:
need to make Blue Mood cocktail.
Plan: 1. Pour in white rum. Add Blue Curacao, 3. Mix in lemon juice.
Drop in ice cubes.
Instruction: Pick up the cocoa powder bottle that is out of reach.
Reasoning:
Scene description: The cocoa power bottle of reach. There
wooden stick nearby.
Plan: 1. Grasp the stick. 2. Sweep the cocoa powder bottle closer with the stick and put down the stick. 3. Pick up the cocoa powder bottle.
Table 6: Reasoning examples for generalizable planning.
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Instruction: Dip the beef and the enoki mushrooms.
Reasoning:
beef the green bok choy are the hotpot strainer. But I fail to grusp the hotpot strainer due to misalignment. Plan: 1. Put the beef into the hotpot strainer. 2. Put the enoki mushrooms into the hotpot strainer. 3. Wait for the ingredients to cook and then pick up the hotpot strainer.
Scene description:
Some
What have done: Put the beef into the hotpot strainer. 2. Put enoki mushrooms into the hotpot strainer.
Now need to: Retract, reposition to align with the strainer, and try grasping again.
Instruction: Make me a plate of tomato-egg scramble. Reasoning:
The oil bottle is located at the front of the table. The cooking machine is on the left side of the table. But I discover that the oil bottle slips from the gripper.
Scene description:
Plan: 1. Pour the oil into the cooking machine. 2. Pour the tomatoes and egg liquid into the cooking machine. Wait for the cooking process to finish, then pick up the spoon. 4. Scoop out the tomato-ege scramble, transfer it onto the plate, and place the spoon into the cooking machine.
What have done: Nothing. Now need to: Retry to grasp the oil bottle with increased firmnessss
Table Reasoning examples for error detection and recovery.
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Instruction: Dip the beef for me.
Human: Could you also dip another vegetable for me?
Robot: Sure! Would you like green bok choy, enoki mushrooms, or cabbage?
Human: / want some green bok choy.
Reasoning:
Scene description: Some of the beef is in the hotpot strainer. The green bok choy the top-left plate.
Plan: 1. Put the beef into the holpet strainer. 2. Put the green bok choy into the hotpot strainer: 3. Wait for the ingredients to cook and then pick up the hotpot strainer.
What have done: 1. Put the beef into the hotpot strainer. Now need to: Put the green bok choy into the horpot strainer.
Instruction: Make me a cup Vodka Sunrise.
Human: don't want orange vodka, I want lemon-flavored one.
Reasoning:
Scene description: The grenadine syrup and orange juice have been poured into the glass. The remon-flavored vodka is the second from the right the top shelf.
Plan: 1. Pour the grenadine syrup into the glass. 2. Pour the orange juice into the glass. 3. Put down the orange vodka, and retrieve the lemon vodku. What have done: 1. Pour the grenadine syrup into the glass. 2. Pour the orange juice into the glass.
Now need to: Put down the orange vodka, and retrieve the lemon vodka.
Table 8: Reasoning examples natural human robot interaction.
Instruction: Pick up the black mouse. Reasoning:
I need pick up the black mouse, which the left back of the eggplant, has smooth, arching shape that fits palm, and key component typical office workstation.
Instruction:
Pick up the object which is to left back of the coconut water
bottle.
Reasoning:
I need to pick up the blue cube, which is identified by its blue shade, and is solid form useful for creating stable structures.
Instruction: Pick up the object which exhibits purple coloration. Reasoning:
need to pick up the eggplant, which is to the right of the coconut water bottle, and is common ingredient in ratatouille.
Instruction: Pick up the object which is packaged drink. Reasoning:
I need to pick up the coconut water bottle, which is to the left back of the blue cube, and has cylindrical form.
Table 9; Reasoning examples for Single-Env visual grounding.
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Instruction: Bring me the Sprite. Reasoning:
need to pick up green Sprite, which is a model of a drink, located on the right side of the table.
Instruction: Give me the object behind the seashell. Reasoning:
need to pick up the lantern the left the croissant, which is black with metal structure, and holds light and can be carried.
Instruction:
Hand me the object which black round.
Reasoning:
need to pick up the alarm clock sitting the left side of the table.
Instruction: need something get closer look at this fine print. Reasoning:
need to pick up the magnifying glass, which to the right of the red pepper, and has wooden handle and circular lens.
Table 10: Reasoning examples for Open-World visual grounding.
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You are given:
step-by-step plan for tabletop robot task, recorded by a wrist-mounted camera. set of sampled frames from the video, named by integer frame IDs. Each image is introduced by text line "Frame <id>" followed by the image itself.
Goal:
Annotate "boundary intervals" between consecutive steps in the plan. A boundary interval is contiguous range of frames (start id, end, id] such that:
1) From the visual evidence, the previous step has clearly been completed.
The next step has not yet started.
tabletop is substantially visible across interval (i.e,, relatively complete of the workspace surface; avoid frames where the table mostly occluded by the robot or out view).
Gripper-state criterion for all selected intervals: gripper open, gripper holds object (empty), and is positioned relatively from the tablstop (i.e., retracted clearly above the surface rather than contact). Prefer intervals where this is directly visible.
Additionally:
Typical step structure involves: pick tool/object, perform operation, then place/put the tool elsewhere. Favor boundary intervals after "place/release" the prior step and before the approach the next target, where the gripper is open, empty, and away from the tabletop.
the gripper temporarily occluded or out of view. accept frames where other cues strongly indicate oper/erppi eaae/a (e.g., contact, no object jaws across adjacent frames). If not confirmable, keep the interval minimal and lower the confidence, but still follow the schema.
Include a pre-task boundary interval that begins at frame (implicit) and ends just before Step visibly begins, while satisfying tabletop visibility and the gripper-state criterion.
Include a post-task boundary interval that starts right after the final step is complete and ends at the last frame (implicit). while satisfying tabletop visibility and the gripper-state criterion.
Intervals must be non-overlapping, ordered, and as long as possible while respecting the rules above. Prefer intervals with at least 2-3 frames where possible. only a single frame satisfies the conditions, use a single-frame interval [k, k).
both (a) show crrtoou-stee-compplee and next-step-not- started and (b) maintain tabletop visibility and (c) meet the gripper-state criterion, pick closest visually justifiable frame(s) and keep the interval minimal, but never violate chronological order.
some step transition has no frames
Assumptions and cues:
"Previous step completed" cues can include: target object placed in its goal pose, gripper released/open, robot no longer manipulating object, the scene stabilized. "Next step not started" object interaction has begun, The camera occluded by the manipulator.
can include: the gripper not yet approached/touched the next target,
evident motion initiating the next step.
the wrist; favor frames where the tabletop surface broadly visible and heavily
Input you receive:
Step plan (numbered 1..N).
A list of frames; each is introduced by the line "Frame <ids" followed its image. Frame IDs chronological and unique. The first frame ID 0; the last given.
Output format (retur ONLY this JSON; no extra text):
I'version":'vt" "fottl_framess" "tabletop_visible visible' :true, 'confidence" "<flottt 0..1))),
"steps":[" '["<sttpp 1 text>" "<step text>"."... "..."""""" "i'intereall"::
["label':'pre "pre_stpppr__tpp_____ 'endd" :*. "tabletop
"_2",,___"______ __ "start":"" "end":"" "", "tabletop_ visible" "ttrue, "confidence"""" <<foat 0..1>"),
""."e""""..."""ttrt""" "end"."* "tabletop ciciioppviiibbeee.. visible":true, "confidence": <float 0..1>)),
("label'
babbel:/between_step Beetween_step_N--e (N-1__N "start":" " "end":" "odd"""""taa "tabletop visible ttrue, "confidence": = <float 0..1>"),
"aabell""pott_stpp"" N", "ssart": "tabletop_ wwwivviiiiiiaaaa "confidance" "ooniidence"""<flla""" 0..1>']]
Figure 13: Prompt template for annotating reasoning interval.
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You are given:
central frame image (the current interval's keyframe).
The task's high-level plan as an ordered list of substeps P (01, .... pK).
The list of completed substeps up to the current interval.
The next substep (or "Task Finished" the task is finished).
Your goal:
1) Produce the "scene description'
current interval focusing strictly on task-relevant objects
the given plan and substeps.
2) Output must valid JSON only, matching the schemn below.
Position frame reference:
"front" nearest edge of table to camera/viewer.
"back" farthest edge the table from the camera/viewer. "left/right" viewer's left/night.
You may also "center/middle, "frant-left," "front-right," "back-left, "back-right," and proximity
phrases like "near the left edge." " "near center," "near the back edge."
Rules:
Write concise scene summary (2-4 sentences).
List only task-relevant objects the table (tools, containers, parts, targets, intermediates). One sentence object describing its absolute position on the table using the frame above. Do not hallucinate; if uncertain or partially occluded, lower confidence and note the uncertainty. If multiple similar objects exist, disarmbiguate with indices (e.g., "blue block #1", "blue block #2")
based on spatial layout.
an object is not on the table not visible, omit it from the objects list. Use brief, literal attributes (e.g., color, material, state like "apen/closed, * "full/empt""). Output must be valid JSON only. Do not include any extra text or formatting outside JSON.
Inputs:
TASK NAME: (TASK NAME)
HIGH HIGH_LEVE LEVEL PLAN (ordered): [HIGH_LEVER LELALLLLELLLLLNAAA LIST)
COMPLETED SUBSTEPS (up to current interval): (COMPLETED SUBSTEPS LS LIST)
NEXT SUBSTEP: (NEXT SUBSTEP OR_ DONE)
IMAGE: [central frame image provided as input]
Output JSON schema:
('scene ('scene_summart summary": "string, 2-4 sentences describing the visible scene and relevance to the task.',
"objects": (("amee" "name": "string, concise object name (e.g.. 'red mug', . 'blue block #1')', "attributes": ['string", ")..", "position sentence": "string, exactly one sentence stating the object's absolute position on the table ffrom//baokllettiignttteett optional proximity/edge terms).". ", "position prosition_tags": ['front] back(lattlightllenter" "optional tags like 'rontt-let', 'near-left-adge' 'near-back-edge" 'confidence": 1, "uncertain, observations": ["string notes about any ambiguities, occlusions, or visibility issues (optional; omit if n)
Figure 14: Prompt template for generating reasoning content (scene descriptions).
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E SYNTHETIC VISION-LANGUAGE DATA EXAMPLES
Our 16,000 synthetic images are entirely annotated by Gemini 2.5 Pro, without any human inter- vention. For 6,000 of these images, generate visual grounding tasks. Each of these images is annotated with 17 instruction-reasoning pairs, with the instructions referring to objects using their direct names (2 instances), spatial relationships (5 instances), attributes (5 instances), and semantic features (5 instances). For the remaining 10,000 images, we annotate a long-horizon planning task along with a corresponding high-level, step-by-step plan for task completion. We also attempt to use GPT-4o for annotating our synthetic images but find its spatial understanding to be weak. We therefore use Gemini 2.5 Pro, which demonstrates strong spatial reasoning capabilities.
We present illustrative examples synthesized by our embodied reasoning-centie visual- risual-language data synthesis pipeline. Table[[Zann Table show samples of synthesized data for visual grounding and aanghorizonn tasks, respectively, each including textual descriptions of tabletop layouts, synthe- sized images corresponding to these descriptions, and the accompanying (for visual grounding example, we only show one pair for each of the four reference types). Fig. [5] illustrates the effects of applying fisheye distortion or compositing robot gripper with adaptive brightness to the synthetic images.
enstructionrrasonnine pairs
and 18 detail the specific prompts used with Gemini 2.5 Pro throughout our pipeline: Fig. 16 shows the prompt for generating diverse tabletop descriptions, while Fig, 17 and [nd [8] illustrate the prompts for generating visual grounding and long-horizon task instructions and their associated reasoning, respectively.
Moreover, Fig. 16 17
Data Quality. We randomly sample 50 instances from the synthetic dataset and identify the follow- ing types of errors: 1) Wrong Image: the presence of hallucinated objects implausible physics; 2) Wrong Text: reasoning that contradicts the corresponding image or common sense. If the generated
image is incorrect, we do not evaluate its text. The results are summarized in Table 11
Wrong Image
Wrong Text
All Correct
8
40
Table 11: Frequency of errors and fully correct cases in 50 randomly sampled synthetic data instances.
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Tabletop layout deseription:
The entire scene sharp and focused. From 45-degree overhead view, observe a medium-sizedd rectangular driftwood table with a natural, uneven texture. On are displayed seashells, coral sculpture, a nautical map under a magnifying glass, a vintage lantern, and a model sailboat, backed by a beach house and ocean view.
Direct instruction:
Can you pass me the map?
Reasoning: need to pick up the map, located towards the center the table.
Spatial instruction: Get me the object sitting on the back right area of the
paper item.
Reasoning: need to pick up the miniature sailboat model, which is placed on the map near the top right corner of the map.
Attribute instruction: I need the item with the complex branching structure. Reasoning: need to pick up the large piece of coral positioned at the left side of the table.
Semantic instruction: Could you pass mc the device used for sceing small details much larger?
Reasoning: need to pick up the magnifying glass situated on the map near the bottom right corner of the map.
Tabletop layout description:
is sharp and focused. From 4e-dege overhead view, observe a large. oval, stone table with rough texture, set outdoors in a mountain retreat. On it, a hearty meal of grilled trout, wild rice, steamed vegetables, jug of water, and lantern. The rugged outdoor setting is enhanced by the surrounding pine trees and the fading twilight.
The entire
Direct instruction: Can you hand me the lantern? Reasoning: I need to pick up the black lantern situated at the back left of the rock table.
Spatial instruction: Pass me the item directly to the left of the grilled trouts. Reasoning: need to pick up the fork sitting to the immediate left of the grilled trouts.
Attribute instruction: Please give mc the plate with the long, silvery food item.
need to pick up the plate containing the two cooked fish positioned in front of the vegetable bowl.
Reasoning:
Semantic instruction:
Hand me that grain-based side dish, please. Reasoning: need to pick up the plate of wild rice located at the back left, the left of the vegetable bowl.
The entire scene sharp and focused. From a de-dege overhead view, observe a rectangular maple table with a honey finish, placed in a sunny conservatory. A watering can, a terracotta pot with blooming flowers, gardening book, gloves, and small birdhouse are charmingly scattered on it. Glass panels reveal lush garden outside.
Tabletop layout description:
Can you pass me the watering can? Reasoning: I need to pick up the green watering can positioned on the back left side of the table.
Direct instruction:
Spatial instruction: want the item located the front right side of the
table.
Reasoning: need to pick up the pair of gardening gloves, situated on the front right area of the table surface.
Attribute instruction: I need the object that's open and has pages with text. Reasoning: I need to pick up the book, which is open and located between the watering can and the brown gloves.
Semantic instruction: Please pass mc the item that could provide shelter for small birds.
Reasoning: need to pick up the wooden birdhouse, sitting on the back right side of the table.
Table 12: Examples of synthetic vision-language data for visual grounding tasks.
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Tabletop layout deseription: The entire scene sharp and focused. From 45-degree overhead view, directly facing the cluttered table, observe rustic wooden surface covered in vibrant fresh ingredients like tomatoes, peas, herbs, and spices. cutting board, knife, spoon, and book are laid out, bathed in bright light, against backdrop of softly visible shelves with plants.
Instruction: Prepare a Fresh Herb and Tomato Relish.
Reasoning:
Plan: 1. Chop the tomatoes using the knife and cutting board. 2. Chop the fresh basil and parsley. 3. Combine the chopped tomatoes, basil, and parsley in bowl. 4. Add the green peas and the dried herbs and spices from the piles on the board to the bowl. 5. Mix all ingredicnts thoroughly using the wooden spoon.
Tabletop layout description: The entire scene is sharp and focused. From a 4e-degre overhead view, directly facing the cluttered table. observe low Japanese tea table with woven tatami mat beneath it, showcasing set, small bonsai tree, and scroll. The scene plays indoors, in serene, minimalist room, emphasizing peaceful tranquility.
Instruction: Prepare and serve tea.
Reasoning:
Plan: 1. Pick up the teacup and place next to the teapot. Fill the teapot with hot water. Pour tea from the teapot into the teacup. 4. Place the teapot on the table. 5. Serve the teacup to the guest.
Tabletop layout deseription:
The entire scene is sharp and focused. From 45-degree overhead view, directly facing the cluttered table, observe small potted plant beside an empty lerracotta pot. A soil bag, silver trowel, various tools are scattered across the worn wooden surface. The scene takes place outdoors, in garden full of flowers, bathed in warm afternoon light.
Instruction: Repot the small plant into the larger terracotta pot.
Reasoning:
Plan: 1. Remove the plant from its current small pot. 2. Add soil from the bag into the bottom of the larger terracotta pot using the trowel. 3. Place the plant in the center of the larger pot. 4. Fill the remaining space in the larger pot with soil from the hag using the trowel. 5. Use the trowel to gently firm the soil around the base the plant.
Tabletop layout description:
The entire scene is sharp and focused. From 45 45-degree overhead view, directly facing the cluttered table, observe white folding table at an outdoor market, Assorted fruits, vegetables, and price tags cover the table. The bright, natural light enhances the colors of the produce.
Instruction: Prioritize fresh produce to boost your daily vitamin intake.
Reasoning:
Plan: 1. Pick up some tomatoes and place them in the basket. 2. Pick up some lemons and place them the basket. Pick up some oranges and place them in the basket. 4. Carry the basket to checkout.
Table 13: Examples of synthetic laiinnlanguage data for long-horizon tasks.
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Figure 15: Augmentations for our synthetic images. From left to right: original synthetic images, synthetic images with fisheye distortion, synthetic images with a rohot gripper composited with adaptive brightness, and synthetic images with both fisheye distortion and compositing robot gripper with adaptive brightness.
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Create 30 detailed 50-word prompts that describe scenes from 45-degree top-down view of table. The table should have clear description of its shape, size, texture, and colar. On the table, place around five objects, describing each object in detail and their positions relative to each other (e.g.. object A placed above object B). The background and environment should be clearly defined, either indoor or outdoor, and the scene should be rich in detail. Ensure there is no blurriness or out-of-focus areas, and the lighting and atmosphere should enhance realism.
Please ensure each of following prompts is unique and creatively different, varying the table, objects, environment (like indoor or outdoor), lighting, and overall atmosphere.
Each prompt should start with "The entire scene sharp and focused. From a 45-degree overhead view, observe ...", followed by a description the table's "COLOR" (this could diverse across different prompts), shape, texture, size,
Use following format separate each prompt:
"START Prompt <Prompt ID>"
[Detailed description of the scene]
"END Prompt <Prompt ID>
Repeat this process for distinct prompts. Request generate all
Figure 16: Prompt used to generate tabletop descriptions.
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the provided image, you will notice several items placed on table. Your task to come up with 17 different instructions based these These tasks categorized into three types based object properties: spatial, semantic, and attribute.
Spatial pertains to the object's position in space (e.g.. on top of the plate, to the right of the book, the bottom right corner of the table).
Semantic refers to the object's general, high-level meaning (e.g., sushi a type Japanese food, kettle is for bolling water, book meant reading, etc.).
Attribute concemed with the object's specific features characteristics (e.g., ball is round, handle made of wood, etc.).
For the objects on the table in the image, your task to create 17 instructions, which can either directly ask for object or describe using its spatial, semantic, or attribute properties (e.g., "pass me the item on the plate, "give me something that helps with drying hair," "hand the yellow object").
Each task essentially "pick" task, but the instruction should sound natural and realistic.
After giving the instruction, provide more specific description that starts with "I need to pick up, * and then clearly name the object, possibly with some additional spatial details to help locate it.
When describing location, try to be as accurate as passible. Avold using vague descriptions such as "in middle/center table," "near," "beside," or to, these could apply many objects. Instead, precise relative positioning, such "to the left front of object." "on top object," "between object and object B." "to the right back of object," or "behind an object."
When giving instructions, avoid mentioning the specific name of the object and instead use pronouns like 'item," "object," "device."
When providing attribute instructions, only list properties object.
Your Tasks:
First, generating 2 tasks with direct references to the object name. Then, generate 5 tasks ""onl"" related to spatial properties (focusing on the location the objects). Next, generate tasks related to semantic properties (focusing on the general meaning or purpose the objects).
Finally, generate 5 tasks related attribute properties (focusing specific features of the objects).
For each task, follow this format:
"Start Task <task ids"*
Instruction:...
need to pick up
"End Task <task idx"
Separate these 4 types of tasks by ### Tasks Related to Spatial Properties
Tasks Related Semantic Properties
Tasks Related Attribute Properties
Figure 17: Prompt used to gemerate visual grounding task instructions and reasoning.
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"End Task" Published as a conference paper at ICLR 2026
the given image, there is a table with several items placed on messy manner.
Your task is first imagine lang-harizan task based items in image (such organizing the table, making sandwich, etc.). This task needs to be relatively long-term, meaning should require about several steps to complete.
The second step is to provide a plan, where each step is a brief action description (e.g., Pick up sth and place somewhere, Close sth, Open sth, Move to somewhere, etc.).
Output the following format:
"Start Task"*
Instruction:
1.
N.
you cannot think of an interesting task, simply output "Fail to think of a plan." instruction plan should brief and precise.
Figure 18: Prompt used to generate long-horizon task instructions and reasoning.
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F IMPLEMENTATION DETAILS
F.1 ROBOT DATA INTERVALS
As mentioned in Sec. 3.2 we segment robot demonstrations into two types of intervals: reasoning intervals and acting intervals. Below, we detail what TneTwoVL learns in each interval type.
1) Reasoning intervals, Tne learns to:
Predict [BOR] and the updated reasoning content ft based on the latest reasoning content R.
Predict [BOA] and actions based on the updated reasoning content R.
Predict actions based on the latest reasoning content R without supervising [BOA] I. This is to prevent incorrect action prediction if the model fails to update the reasoning promptly during deployment.
2) Acting intervals, OneTTwVVL learns to:
Predict [BOA] and actions based the latest reasoning content R.
(Optional) Predict [BOR] based on outdated reasoning without supervising the reasoning tent. This is included because we observe that during deployment, the model sometimes fails to enter the reasoning mode. Since predicting decision tokens is essentially binary classifica- tion problem, and acting intervals are typically significantly longer than reasoning intervals, the model predominantly learns to predict [BOA], leading to imbalanced classification problem. This optional training helps to increase the proportion of [BCR] predictions.
Additionally, it is important to note that reasoning interval during training is designed to encourage the model to learn the reasoning process more effectively. In real-world deployment, the robot only reasons at small number of steps (rather than continuous intervals), ensuring that the overall operational efficiency is almost unaffected.
F.2 POLICY TRAINING
As shown Sec. 3.1 we use To as our base model. For each task, we train the model for 30,000 steps on 8xH100 GPUs, requiring approximately 10 hours. We adopt training hyperparameters from To. We make two modifications to the original mo 's input. Firstly, we use the current image I and the reference image Ter image observations. We incorporate T because the textual scene descriptions in reasoning may become outdated as the task progresses (e.g., an object's position described relative to the gripper becomes invalid upon gripper movement). Including Ters which corresponds to the image observation for the current reasoning content, helps prevent model confu- sion that might arise from potentially outdated textual descriptions. Second, input not only the current robot proprioceptive states but also the proprioceptive states from 0.05 and 0.25 seconds ear- lier. This temporal context allows the model generate more consistent and smooth actions during execution.
F.3 DEPLOYMENT
In real-world deployment, we use the temporal ensemble (Zhao et al. 2023) technique to ensure smooth action execution. Specifically, in acting mode, the policy generates temporally overlapping action sequences every 0.2 seconds. At any given timestep, multiple predicted actions are averaged using exponential weighting to determine the actual executed actions.
Table 14 lists the computation time for To, along with the computation time for OneTwoVLA in acting mode for varying input token counts and in reasoning mode for varying output token counts, all of which are tested while processing two image inputs on an NVIDIA 4090 GPU. In acting mode, although OneTwo TwoVLA has additional reasoning content input and outputs extra [BCA] compared to Tp. this has minimal impact on computation time and remains well below 0.2 seconds, thus execution efficiency is not affected in this mode. In reasoning mode, when the reasoning token count is low (less than 20 tokens), execution efficiency unaffected; however, when reasoning content is lengthy (exceeding 100 tokens), the robot needs to pause for few seconds. Nevertheless,
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reasoning only occurs at a few critical moments, resulting in minimal impact overall execution efficiency. For example, in one trial of the Tomato-Egg task, the entire long-horizon task takes 183 seconds, with reasoning occurring 5 times, totaling 16 seconds of reasoning time, which accounts for 8.7% of the total duration. Similarly, entire long-horizon task takes 135 seconds, with reasoning occurring 5 times, totaling 14 seconds of reasoning time, which accounts for 10.4% of the total duration.
one trial of the preparing Mountain Fuji task, the
input tokens
# output tokens
computation time
20
0,082 $
OneTwoVLA-AActee
20
0.102 $
200
0.104 s
200
20
0.853 $
OneTwoVLA-Res 100
200
100
2.346 s
OneTVwoVLA-RResoon-222
200
200
4.361 8
Table 14: Computation times of and OncTwoVLA. no's input tokens consist solely of instruction é.
OLA'ss input tokens typically longer, including instruction and latest reasoning content and R)) acting mode (OneAwoVLA-AAt rows), OneTwoVLA's output token ing mode (OneTwoVLA LA-Rcco rows), OneTwoVLA outputs [BOR] and updated reasoning content, R. We showease computation times when its output token length is 20, 100, and 200.
single [BOA]. While reason-
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G
OTHER FINDINGS
Instruction:
"Grasp cube."
Instruction: "Grasp cube." "
Instruction: "Grasp bottle."
Instruction: "Grasp bottle." *
Reasoning:
Reasoning:
Reasoning:
Reasoning:
There are two cubes the table. choose the left one to grasp.
There are two cubes the table. choose the right one to grasp.
There are two bottles the table. choose the left one grasp.
There two bottles the Iable. I choose the right one to grasp.
Execution:
Execution:
Execution:
Execution:
Figure 19: Multi-modality task illustration. Two cubes and two bottles are symmetrically placed on the table. When the instruction doesn't 't specify grasping the left or right object, Two can reason to grasp either the left the right object, producing multi-modaa actions.
G.1 ONETWOVLA PRODUCES MULTI-MODAL ACTIONS
In this section, we design experiments show OneTwoVLA's capability produce multi-modal actions.
Tasks and Evaluations. Two identical cubes symmetrically placed on a table, each with iden- tical bottle positioned symmetrically behind it. Using the UMI device, we collect 50 demonstrations for each of these four objects (totaling 200 demonstrations). Each demonstration instruction is either "Grasp the cube" or "Grasp the bottle," without specifying left or right. During testing, the object positions and the robotic gripper's initial pose remain fixed. Each method is tested 20 times per instruction.
Comparative Methods. 1) OneTwoVLA: For each demonstration, we explicitly include disam- biguating reasoning content (e.g., specifying picking up the left or right object) to resolve the ambi- guity. 2) :o: The model receives the original instruction directly, without explicit disambiguation.
Experimental Results. As shown in Fig, 19 OneTwoVLA demonstrates multi-modal action capa- bility by alternating between reasoning to grasp objects from either side. Specifically, in the "grasp cube" experiment, OneTwwoVL grasps the left cube 9 times and the right cube 11 times. In the "grasp bottle" experiment, grasps the left bottle 8 times and the right bottle 12 times. OneT- WoVLA achieves this balanced left-right performance because its reasoning process is probabilistic, which means the model can sample different decisions (such as whether to grasp from the left or right) based on predicted token probabilities, much like language models generate varied responses from the same input.
contrast, although flow matching (Lipman et al. 2022 Liu 2022) or dif- fusion (Ho et al., 2020 Chi et al.) 2023) algorithms theoretically enable tently selects only the right-side objects, exhibiting only unimodal behavior, similar to observations in some other studies (Soare) 2024). Additionally, the disambiguating reasoning content helps the model fit actions more accurately. This evidenced by иo occasionally failing to grasp the block, while OneTwoVLA consistently achieves precise grasps. Moreover, no action mean squared error (MSE) on the validation dataset 56% higher than OneTwoVLA's, This interesting finding suggests that when training on large-scale, variable-qui -uraltlequullity robot datasets, detailed annotation of reasoning content may enhance action learning.
mo consis-
G.2 ONETWOVLA PRODUCES TRASONING-COMPLINE ACTIONS
Our experiments show that the actions generated by TweTWoVLA consistently align with its reason- ing, even when the reasoning itself is incorrect. This finding similar observations in previous
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work (Zawalski et al. 2024). For example, in the Hotpot task, if OneTwoVVL occasionally rea- sons incorrectly about food locations, proceeds to reach toward those incorrect positions. Simi- larly, in the Open-Wor experiment, OneTwo moves the object specified in its reasoning, even that object does not align with the instruction. This indicates that OneTwoVLA's cognition and behavior highly unified, showcasing synergistic reasoning and acting. Additionally, this interesting phenomenon may indicate that improving the model's ' reasoning ability (e.g., through additional vision-language data, using powerful VLM the base model, more precise rea- soning annotations) may contribute to generating more appropriate actions.
H HARDWARE SETUP
Fiess D42e
ARX Ar
Liting Table
WSG-60 Oripper
Mobil Buse
AAXX
Figure Robot platform overview. We employ two robot platforms: single-arm Franka system (left) and a dual-arm ARX system (right).
left) is a single 7-DoF Franka arm equipped with Weiss WSG-50 parallel-jaw gripper. moust-m GoPro camera with fisheye lens provides wide-angle observations. The arm is mounted on a custom height- beight-adjustable table that can pushed by a person person-while not autonomous, this mobility allows us evaluate the policy beyond traditional laboratory environments. The action space is 7-dimensional (6-DoF end-effector pose plus gripper width). Expert demonstrations for this platform are collected using UMI (Chi et al. 2024).
We utilize two robot platforms. The primary platform (Fig. 20
The second platform (Fig. 20 right) features two 6-DoF ARX arms with parallel-jaw grippers and three-cament system (two wrist-mounted and one mase-mounted). It also includes a holonomic wheeled base and I-DoF torso lift mechanism, though these components have not yet been utilized in our experiments. The resulting action space is 14-dimensional (2 x 7). Expert demonstrations are collected via teleoperation using Meta Quest headset.
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I
FAILURE CASES
Instruction: "Prepare plate of tomato-agg scramble."
Instruction: cup of Vodka Sunrise."
Instruction: "Pick small basketball toy."
the green bok choy."
Figure 21: Failure cases of OncTwoVLA.
Despite the promising performance of One it still makes mistakes. Fig. 21 illustrates the main failure cases of ThooA. In the Tomato-Egg task, One occasionally fails to grip the yellow plate containing tomato and egg liquid firmly enough, resulting in the plate being dropped (see the first column in Fig. 21). In the Hotpot task, One Two VLo sometimes misidenti- fies the location the target ingredient. For instance, as shown in the Fig, 21 second column, the robot is instructed to pick up green bok choy but instead it attempts to pick up enoki mushrooms. The third column of Fig. 21 shows& case in Cocktail task, where orange juice accurately while preparing the Vodka Sunrise, causing the juice to spill. In the Open-world experiments, TwLA shows vulnerability when encountering objects that are not present in either the robot data or our synthesized vision-language data. For instance, illus- trated the Fig. fourthh column. the robot consistently moves toward the chessboard despite being instructed to pick up the small basketball toy. We believe that training on larger robot datasets, as well as co-training with richer vision-language data, can further facilitate OneTwoVVLA learning fine-grained actions and improve generalization capabilities.
fails pour the
41


Reviews:
[{"review_id": "9mDjjHyPRy", "reviewer": "Reviewer_EYxJ", "summary": "- This paper introduces OneTwoVLA, a vision-language-action model that can perform both acting and reasoning.\n\n- OneTwoVLA adaptively reasons at critical moments during execution (e.g., upon completing subtasks, detecting errors, or requiring human inputs).\n\n- This paper also designs a pipeline for synthesizing embodied reasoning-centric vision-language data, used for co-training with\nrobot data.", "questions": "Please see the Weaknesses section.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Improving the reasoning capabilities of current embodied systems is crucial, and enabling adaptive reasoning is particularly insightful.\n\n- OneTwoVLA is highly practical, but it would be better if the authors could validate its generalization ability on public benchmarks.", "weaknesses": "- OneTwoVLA inherits the model architecture of pi0 (VLM for reasoning and flow-matching policy for acting). Why is it described as a single unified vision-language-action model?\n\n- Regarding the issue raised by the authors that \"System Two may produce intermediate contents that System One cannot execute,\" how does OneTwoVLA address this problem? Why does the standard two-system VLA suffer from limited mutual understanding, while OneTwoVLA does not?\n\n- In my view, compared to previous works, OneTwoVLA does not introduce architectural innovations; essentially, it still relies on a VLM for reasoning and subtask generation, with an action expert responsible for execution. The main difference lies in the fact that the authors heuristically designed certain scenarios requiring re-planning and annotated corresponding data to fine-tune pi0.\n\n- Currently, there are many works focused on embodied reasoning. Could the authors evaluate OneTwoVLA on some public benchmarks to demonstrate its multimodal reasoning and planning capabilities? For example, the following works:\n\n[1] RoboBrain 2.0 Technical Report\n\n[2] Embodied Arena: A Comprehensive, Unified, and Evolving Evaluation Platform for Embodied AI\n\n- Could the authors report the reasoning time overhead introduced by adaptive reasoning? For the baseline, is it possible to improve performance by increasing the frequency of System 2 execution?", "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": "glubDDETL4", "reviewer": "Reviewer_uSyR", "summary": "This paper presents OneTwoVLA, a unified vision-language-action model capable of both reasoning (System Two) and acting (System One) within a single framework. Unlike prior dual-system approaches that separate high-level reasoning from low-level control, OneTwoVLA adaptively switches between reasoning and action—engaging explicit language reasoning at critical moments while efficiently generating actions otherwise. The authors further propose a scalable pipeline for synthesizing embodied, reasoning-centric vision-language data to co-train with real robot data. Extensive experiments demonstrate that OneTwoVLA significantly outperforms existing methods across four key dimensions: long-horizon task planning, error detection and recovery, natural human-robot interaction, and generalizable visual grounding, highlighting the promise of unified models for embodied intelligence.", "questions": "1. Is the sequential dual-phase design (reasoning first, then acting) fundamentally necessary? Have the authors considered a mechanism that allows simultaneous reasoning and acting, similar to how humans can reason while performing actions, rather than pausing execution each time reasoning is invoked? It would be valuable for the authors to discuss how such an asynchronous or parallel architecture might be designed and what challenges it would introduce.\n\n2. Regarding the synthetic data filtering process, line 1536 mentions that incorrect images are excluded from evaluation. Could the authors clarify how this filtering was conducted — was it done manually, automatically, or through a hybrid approach? More detail on the evaluation pipeline and its reproducibility would improve transparency.\n\nIf the authors can address these concerns or provide more evidence supporting their design choices, I would consider raising my score.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "OneTwoVLA’s main strengths lie in its elegant unification of reasoning and action within a single adaptive model, which effectively eliminates the latency and coordination issues common in dual-system designs. It also demonstrates impressive generalization by leveraging a scalable pipeline that synthesizes embodied reasoning-centric vision-language data, significantly enhancing performance on unseen tasks. Moreover, the model enables natural and context-aware human-robot interaction, showing a level of interpretability and adaptability in current robotic systems.\n\nOne more thing:  The authors provide highly detailed supplementary content, including rich qualitative examples, visualizations, and additional experiments, which greatly improve the clarity and reproducibility of the work.", "weaknesses": "1. The reasoning-centric synthetic dataset appears essential to the model’s overall performance, but it remains unclear how much it directly contributes to robotic manipulation capabilities. An explicit ablation study comparing models trained with and without this reasoning data — or analyses showing how it benefits manipulation compared to standard vision-language data — would make the claim far more convincing.\n\n2. The model’s generalization mainly lies in reasoning, while action-level generalization remains limited. Given that large language models already generalize well in reasoning for simple robotic tasks, it would be more meaningful to explore whether OneTwoVLA can generalize in low-level manipulation, such as grasping unseen objects.", "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": "6ezhfQsVZv", "reviewer": "Reviewer_yjpV", "summary": "OneTwoVLA introduces a VLA framework that allows a single model to adaptively decide when to reason and when to act, bridging high-level reasoning and low-level control. It employs special decision tokens ([BOR]/[BOA]) to autonomously switch between reasoning and acting modes. Experiments on long-horizon real-world manipulation tasks demonstrate that OneTwoVLA achieves superior success rates.", "questions": "Q1. In the out-of-lab experiments, under such complex backgrounds, especially when using a GoPro with a wide field of view, is it truly possible to achieve stable manipulation? Is this manipulation capability mainly derived from the pretrained knowledge of the base model?\n\nQ2. The appendix layout could be improved, it contains excessive blank space and is overly long, making it difficult for reviewers to find key information efficiently.\n\nQ3. It would be helpful if the authors could add a discussion comparing with Pi_0.5 and ThinkAct. Of course, a quantitative comparison is not strictly necessary.\n\nQ4. This paper presents a very good idea, but it lacks detailed exploration and validation. I will adjust my score based on the authors’ response.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. It simultaneously equips the VLA model with reasoning capability and action prediction ability, which is important for robotics.\n\nS2. The authors conducted a large number of experiments, including \"out-of-lab\" real-world tests. It is amazing.\n\nS3. The writing is very thorough and careful, with a large appendix experiments and visualizations included.", "weaknesses": "W1. Lack of key ablations. The paper lacks experiments that use different reasoning components as conditions, e.g., using only “plan” or “Now I need to …”. Not every lab has the ability to collect all key-step annotations, and I also do not believe every reasoning element contributes positively to all tasks. For instance, in dynamic environments, is the “Historical Summary” really helpful? And in complex scenes, could detailed “scene descriptions” actually introduce unnecessary redundancy?\n\nW2. Impact on generalization. Would using different subsets of reasoning outputs as conditions affect the model’s generalization ability?\n\nW3. Table 14 only compares inference time across reasoning lengths. Did the authors investigate how different reasoning lengths affect manipulation accuracy? Also, what reasoning token length was used in the main experiments?\n\nW4. What is the additional time cost introduced to the execution head by different reasoning token lengths?\n\nW5. If the number of condition tokens increases, it may significantly slow down the DDIM output efficiency. In addition, could the authors provide a real-robot control frequency comparison experiment to support this?\n\nW6. Since the authors already built an automated data labeling pipeline, could it be extended to modify pretraining datasets such as OXE? This could enable the model to develop reasoning-and-action collaboration abilities across broader domains, not just in the downstream tasks.", "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": "sKrEqGeWcS", "reviewer": "Reviewer_2sCT", "summary": "This paper proposes a single unified VLA model capable of both reasoning and acting, and adaptively switching between these two modes. It proposes a pipeline for synthesizing embodied reasoning-centric vision-language data to further enhance the model’s reasoning and generalization capabilities.", "questions": "1. In Section 3.1, how is the adaptive inference capability learned? Is it acquired implicitly through training, or explicitly supervised with ground-truth signals? If it is the latter, how are such ground-truth labels defined and provided?\n\n2. In Figure 2, the blue and green blocks are difficult to interpret. For instance, I understand the green part as representing System 1 and the blue as System 2. However, it is unclear why System 1 is further divided into instruction and reasoning inputs. How are these two types of inputs related to the two systems? The figure needs to be clarified.\n\n3. The error detection capability is emphasized throughout the paper ( from the instruction to the conclusion) yet it is only qualitatively analyzed in Section 4.2, which is insufficient. If this ability is to be highlighted as a key contribution, it should be supported by quantitative evidence; otherwise, it would be better not to overemphasize it.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written. The experimental design is appropriate and effectively demonstrates the proposed model’s capabilities. The model shows promising performance, particularly on long-horizon tasks, owing to the integration of reasoning and action within a unified framework.", "weaknesses": "Please refer to questions section.", "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)