TwinVLA: Data-Efficient Bimanual Manipulation with Twin Single-Arm Vision-Language-Action Models
Authors: Hokyun Im
Euijin Jeong
Andrey Kolobov
Jianlong Fu
Youngwoon Lee
Venue: ICLR 2026 Poster (2026)
Keywords: VLA
Bimanual manipulation
Imitation Learning
Primary area: applications to robotics, autonomy, planning
TL;DR: We introduce TwinVLA, a vision-language-action (VLA) model for bimanual manipulation that fusing pretrained single-arm VLA models. This design reduces reliance on scarce bimanual data while achieving comparable performance.

Abstract: Vision-language-action models (VLAs) trained on large-scale robotic datasets have demonstrated strong performance on manipulation tasks, including bimanual tasks. However, because most public datasets focus on single-arm demonstrations, adapting VLAs for bimanual tasks typically requires substantial additional bimanual data and fine-tuning. To address this challenge, we introduce TwinVLA, a modular framework that composes two copies of a pretrained single-arm VLA into a coordinated bimanual VLA. Unlike monolithic cross-embodiment models trained on mixtures of single-arm and bimanual data, TwinVLA improves both data efficiency and performance by composing pretrained single-arm policies. Across diverse bimanual tasks in real-world and simulation settings, TwinVLA outperforms a comparably-sized monolithic RDT-1B model without requiring *any* bimanual pretraining. Furthermore, it narrows the gap to state-of-the-art model $\pi_0$, which relies on extensive proprietary bimanual data and compute cost. These results establish our modular composition approach as a data-efficient and scalable path toward high-performance bimanual manipulation, leveraging public single-arm data.

Published as a conference paper at ICLR 2026
TWINVLA: DATA-EFFICIENT BIMANUAL MANIPULA- TION WITH TWIN SINGLE-ARM VISION-LANGUAGE- ACTION MODELS
Hokyun Im Euijin Jeong' Andrey Kolobov²
Jianlong Fu²
Youngwoon Leet
Department of Artificial Intelligence, Yonsei University https://jellyho github. io/TwinVLA
Microsoft Research
ABSTRACT
Vision-language-action models (VLAs) trained on large-scale robotic datasets have demonstrated strong performance on manipulation tasks, including bimanual tasks. However, because most public datasets focus on single-arm demonstrations, adapt- ing VLAs for bimanual tasks typically requires substantial additional bimanual data and fine-tuning. To address this challenge, we introduce VWi a modular framework that composes copies of a pretrained single-arm VI.A into coor- dinated bimanual VLA. Unlike monolithic cross- cross-embodiment models trained on mixtures of single-arm and bimanual data, TLA improves both data efficiency and performance by composing pretrained single-arm policies. Across diverse bimanual tasks in comparably-sizer monolithic RDT-1B model without requiring any bimanual pre- training. Furthermore, it narrows the gap to state-of-the-art model тo. which relies extensive proprietary bimanual data and compute cost. These results establish our modular composition approach as a data-efficient and scalable path toward high- pighhreeformance bimanual manipulation, leveraging public single-arm data.
and simulation settings, TwinVLA outperforms a
INTRODUCTION
Thanks to publicly available large-scale robotic datasets, vision-language-action models (VLAs) have shown impressive performance single-arm robotic manipulation, effectively adapting to downstream tasks and generalizing across diverse tasks, objects, and environments (Zitkovich et al.,
2023, Open X-Embodiment Collaboration et al.) 2024) Kim et al. 2024 Black et al. 2024). However, extending these successes to bimanual manipulation aationaaaionnnoo remains challenging, as public bimanual datasets are scarce, and existing approaches often rely on large, proprietary datasets that require thousands of hours of data collection and curation (Black et al. 2024), limiting reproducibility and progress.
Can we build strong bimanual VLAs without collecting or fine-tuning on large bimanual datasets by leveraging existing single-arm data? In this work, we propose highly data-efficient adaptation paradigm for bimanual control that eliminates the need for prohibitive bimanual pretraining. By effectively repurposing a single-arm VLA, we demonstrate that complex bimanual skills can be mastered using only minimal target-domain demonstrations, establishing a practical and reproducible pathway to bimanual manipulation.
To effectively realize this transfer of single-arm priors to bimanual control, the choice of underlying architecture critical. Recent cross- cooos--mbodiment learning work typically trains monolithic models on multi-robot datasets (Open X-Embodiment Collaboration et al., 2024) employing embodiment- specific action decoders (Octo Model Team et al. 2024] NVIDIA et al. (025) or shared, zero-padee parddpddedd action spaces (Liu et al.) Black Black et al. 2024). Although promising, differences in observation
and action spaces introduce heterogeneity, forcing a single model to handle disparate action spaces, and monolithic training underutilizes the modular structure inherent to bimanual tasks.
modular perspective on bimanual manipulation supported by neuroscience: human bimanual manipulation is the coordination of arm-specific motor primitives rather than single monolithic controller. Dedicated neural circuits, such as the Supplementary Motor Area (SMA) and the corpus callosum, orchestrate and synchronize the arms (Sadato et al.) 1997 Swinnen 2002). Similar

---

Published as a conference paper at ICLR 2026
SingleVLA
TwinVLA-1.80
Pretrained on single arm data
Joint attention & MoE integration
A
Action head
Action head
SingleVLA
Selective duplication
Left-Arm
Right-Arm
tpp-comttra
Langurge
Left-wrist
Ego-centric
Right
vine
Anstruction
proprio
instruction
Figure I: Overview of VLA Inspired by humans' two-arm coordination for bimanual manipu- lation, TWi duplicates a VLM backbone pretrained on cross-embodiment single-arm data (Left) to form two arm-specific branches linked via Joint Attention (Right). Shared inputs (ego-centric views, language instructions) are routed via mixture-of ofixeere-f-exppetss (MoE) to improve computational efficiency. Only the VLM backbone is duplicated, keeping the increase in model size minimal.
principles have proven effective in Vision-language modeling, where interaction between modality- specific backhones improves its efficiency and effectiveness (Liang et al. 2024).
Inspired by these insights, we propose TWI,VLA, modular architecture that operationalizes this coordination-cent centrioonn-eeeiccccccc view. Instead of training from scratch, TwinVLA leverages pretrained single- arm VLA. Specifically, we first design lightweight, compact single-arm VLA, which we call SingleVLA (Appendix (A). We pre-train 0.8B-size Sin for single-arm manipulation on the OXE dataset (Open X- X-Embodiment Collaboration et al. 2024). We then duplicate this Single and integrate the two "twin" instances through lightweight coordination method. This design is highly data-efficient: it eliminates the need for a bimanual pretraining dataset and achieves strong performance with only small amount of bimanual demonstrations for fine-tuning.
To integrate two Single LAss into a bimanual policy, TWin utilizes a joint attention (Liang et al. 2024) across the twin models, illustrated in Figure [ This allows the twin SingleVLAs to exchange information and coordinate their actions, while preserving their pretrained capabilities. This approach is made feasible without significant overhead, and utilize a Mixture-of ofxterr-ff-Experts (MoE) to efficiently manage shared inputs. In contrast to monolithic cross-embodiment models (Liu et al., 2024, Octo Model Team al., 2024, Doshi et al. 2024). our approach yields better performance and data efficiency, significantly reducing the need for large-scale bimanual data collection and compute.
duplicate only the VLM backbone
We evaluate VLA across broad range of environments, including a complex, long-horizon real-world task and diverse suite of bimanual manipulation tasks in simulations. Despite leveraging only public single-arm data and limited bimanual fine-tuning data, TLi achieves performance comparable to state-of-the-un bimanual policies.
In summary, main contributions are threcfold:
We propose novel modular architecture for bimanual manipulation that integrates two copies of a pretrained Vin with lightweight coordination method based on joint attention with MoE, enabling synchronized two-arm control.
We present data-efficient paradigm that adapts our twin architecture into a capable bimanual policy for a target task by fine-tuning on only a small bimanual dataset, crucially without requiring additional pretraining, thereby eliminating the need for large-scale bimanual data.
Through extensive experiments across real and simulated bimanual tasks, TWinVLA matches or surpasses state-of-the -he-ttr-rt models trained on far larger bimanual data and compute.
Together, these findings identify our modular Single composition approach as a scalable, efficient path to high-performance bimanual manipulation.
2

---

Published as a conference paper at ICLR 2026
Bunamoel Dateset Size (lours))
10
Dataset size (approx.)
More Efficient
Avg. Success Rate
45%
Better Per Performance
TwinVLA
3.38
10,900h
1-38
10a
40%
2. .9th
RDT-1B
TwinVLA
RDT-1B
1.28
6co
Nodel size
1000
2000
10a
10
Single-arm Dataset Size (Hours)
Training Cost (GPU-days)
(a) Data efficiency comparison
(b) Compute efficiency comparison
Figure 2: (a) Data efficiency. TwinVLA requires only ~ 800h of single-arm and 50 episodes of target bimanual data, significantly less than RDT-IB (~ 2, 400h) and To (~ 10, 900h) in total. (b) Compute efficiency. RDT-IB and require high compute (exceeding 1, 000 H100 GPU-days), whereas Twin VLA achieves higher or comparable performance with only 25 H100 GPU-days.
2 RELATED WORK
Bimanual manipulation policies are essential to enable robots to perform complex tasks that require coordinated two-handed control, such as folding laundry (Bersch al. 2011; Avigal al. 2022) assembling parts (Stavridis & Doulgeri 2018), or wiping the plate (Black et al.) 2025 Chi et al.
2024b). Learning effective bimanual policies challenging due high-dimensional. tightly coupled
action spaces and the scarcity of high-quality bimanual demonstrations (Lee et al.
2020 Xie et al.
2020). Consequently, specialist methods, such as Diffusion policy (Chi et al.] 2024a) and ACT (Zhao
et al.
2023), trained only on target-task demonstrations, struggle on precise, long- long-horizon tasks.
Recent works have explored various architectures for bimanual control explicitly model the inter- dependencies between arms (Lee et al., 2024 Kobayashi & Buamanee (2255), or focus on high-level language planning via VLM (Gbagbe et al. 2024). Anybimanual (Lu et al.] 2025) introduces a
high-level skill manager to coordinate primitives and visual aligner to mask 3D voxels for decoupled policies, benefiting from both high-level managing and architectural inductive bias. While promising, it is difficult to generalize these methods, as they often limited to small-scale scenarios, handle
only low- dexterity tasks, or backbone constraints (Grotz et al. 2024 Shridhar et al. 2022).
Alternatively, another line of research extends successful unimanual Vision Lision-Langugge-Aiee Action (VLA) models (Liu et al. [203b;; Zitkovich et al. 2023 Li et al., (2024b) to bimanual tasks. This transition is challenging due the scarcity of bimanual data, as public datasets are predominantly unimanual. To overcome this, prior work trains 'monolithic' models, requiring large-scale bimanual data collection and intensive pretraining. For example. RDT-IB (Liu et al.) (024) required massive pretraining and fine-tuning (reportedly month on 48 H100 GPUs), and To (Black et al. 2024) relies on a 10, 000- hour proprietary dataset, both incurring high computational costs. Furthermore, the proprietary nature of these datasets limits reproducibility and broader adoption.
In contrast to both monolithic, compute-heas heavy pretraining and specialized architectural designs. our approach adopts modular, coordination-centric design. While Anybimanual (Lu et al., 2025) introduces novel inductive biases for coordination, these are often difficult to integrate into general- purpose VLAA frameworks due to specific backbone constraints. Our method, however, is designed to leverage and scale the existing generalist VLAs. We first train SingleVLA on large-scale public single-arm data, duplicate to couple them, and then fine-tune it on bimanual tasks aask-ialowwng each stage to benefit from the most suitable data (see Figure This composition- srppossttoon-bseed approach avoids bimanual pretraining, requires only a small amount of bimanual fine-tuning, better preserves the strong capabilities of single-arm policies, and significantly improves data and compute efficiency.
3 PRELIMINARIES
This paper aims to develop a data-efficient framework for learning bimanual manipulation policies by building upon pretrained single-arm Vision-L Lanuung-nnnatettiiion "SingleVLA models. This section
3

---

Published as a conference paper at ICLR 2026
formalizes the single-arm and bimanual settings, briefly describes the VLA training objective, and introduces the core architectural concepts we leverage.
3.1 FORMULATING THE BIMANUAL VLA POLICY
Our goal is to extend pretrained SingleVLA single into bimanual policy Twinn applicable to target bimanual tasks. A VLA π (Ar 0z) predicts an action chunk A, = (at, 0.+1...... ,0.............1.... of length T from an observation includes a language prompt I, an ego-centric image Lego (shared input), and an arm-specific wrist
For single-arm manipulation, the observation or = ((1, Tegu)t; (Twri d)+)
single
image Twis with proprioception d (arm-specific input). We train Tsingle A | ogingle) to predict the action chunk for one arm. For bimanual manipulation, the observation aggregates both right (R) and left (L) arm-specific imput, o ((l, Logo)t; (J d' (1 d )e), and the policy Twiin (A, A/. of outputs joint action chunk for right and left arms.
3.2 TRAINING VLAS WITH CONDITIONAL FLOW MATCHING
We train our VLA models to predict continuous robot actions from observations. Each observation or is tokenized and fed into the VLM backbone to produce an output embedding h, (from learn- able readout token r₁). To enable continuous action prediction from h,, we attach an action head (A (AA.hhe he, de) and train it using conditional flow matching objective. The action head is trained
with the following loss function:
(T(O) (0) B/)AA/b)).0(A114))lloo h,, u(A7 A.)11
where he is the VLM output embedding and de is proprioception. This objective trains the action head Up to predict the reference flow u from noised action chunk A7 to the target action chunk A, conditioned on the VLM output and proprioception.
During inference, we sample actions using the forward Euler integration method. Starting from Au N(O, I). we iteratively update the action using the learned flow eg:
A +6 A7 + 5ve(A) he, de),
where we set the sampling step n 10 and use 5 = 1.
3.3 MIXTURE- BASED ARCHITECTURES
To adapt Transformers for multi-modal inputs, various mixture-based architectures have been ex- plored and shown to be effective. These approaches range from combining entire, modality-specifie backbones to ensembling or mixing individual layers within a single backbone. We briefly introduce two such paradigms that inform our design: a model-level Mixture-of-Tranne Transformers (MoT), which coordinates separate backbones, and a layer-level Mixture-of-Experte (MoE), which enables efficient, sparse computation.
The MoT architecture (Liang et al. 2024 enables efficient information sharing between separate, modality-speciff backbones (e.g., text and image). It introduces joint attention, shared self-attention layer performed over the union of multimodal inputs, allowing each modality to directly attend to the others. Meanwhile, modality-specific components such feed-forward networks remain separate, making fusion lightweight yet effective.
MoE (Shazeer et al. 2017) scales model capacity efficiently by routing each input through weighted combination of expert feed-forward networks using gating function, yielding MoE(z) = Σ. wi Ex)),, where w; denotes the routing weight.
TWINVLA
VLA is modular architecture that transforms a pretrained single-arm VLA into coordinated bimanual policy. The overall computation flow of our architecture is described in Algorithm I and Figure TwinVLA integrates single-arm policies through three core principles: (1) selective module
duplication (Section 4.1), (2) cross-arm fusion via joint attention (Section 4.2). and (3) efficient shared representation via Mixture-of-Experts (Section
4

---

Published as a conference paper at ICLR 2026
Algorithm TWA
Xp" encoded inputs from oi (Section 3.1 for each input m {shared, left, right).
FFN;: feed-forward network layer from each backbone E (left, right).
N: Number of transformer layers
for 0 to N 1 do
Iterate every transformer layer
Prepare Q, K, for each input m
for each input E {shared, left, right) do
Q Kr Vi + Norm(Proj
D Input-specific projections, Algorithm
end for
// Joint attention across inputs with attention re-weighting
{Am) <- JointAttention( (Q"), (KTr) (V,"
( J,
Algorithm with mask M Figure
// Residual & FFN / MoE
for each imput m {shared, left, right) do
H t n2 + Norm(Proj
Input-specific output projection, Algorithm
Fm < MoE H m shared else FFN, m (H)) MoE for shared input, Equation
X n+1 < H + Norm(F.)
b Residual connection with norm, Algorithm
end for end for return [XW]
> Return outputs, this will be used for action decoding
4.1 SINGLE-ARN POLICY DUPLICATION
We first pre-train VLA on a single-arm dataset, which we refer to SingleVLA. Note that existing pre- trained models can also be used for this purpose. To construct TwinVLA from Single VLA, we initialize the twin policies for the left and right arms by copying the pretrained SingleVI.A. However, instead of duplicating the full model, we share the vision encoder and DiT (Peebles & Xie 2023) action head while fully replicating the VLM. Each arm has its own lightweight proprioception encoder. This design yields a compact 1.3B parameter model, comparable to the L.2B-parameter RDT-IB, without significantly increasing computational cost.
Visual inputs are processed by the shared encoder, and each VLM produces readout tokens that are jointly decoded by the shared DiT. This design is motivated by the principle that general vi- largely
sual understanding (image encoding) and low-level motor control (action decoding) mmboodmennt-ggnsste skills that can be effectively shared for both arms. In contrast, the VLM, which decides output action given encoded observation, is fully replicated to allow for specialized control.
4.2 JOINT ATTENTION FOR CROSS-ARM FUSION
We integrate arm-specific inputs using a Joint Attention mechanism inspired by MoT (Liang et al.) 2024). As illustrated in Figure and Algorithm D this is achieved by sharing only the self-attention layers across the VLM backbones. Specifically, we concatenate the Q, K, from both backbones. perform self-attention, and subsequently split the outputs back to their respective streams, while other components such as projections use arm-specific networks from each arm's 's VLM backbone. Unlike To (Black et al., 2024), which links a VLM with an action head, we connect two VLMs directly. We elaborate joint attention mechanism detail on Algorithm
Causal joint attention mask. Effective joint attention requires appropriate attention masking. Standard L.MMs use a lower-triangular attention mask for causal prediction. To support joint attention among the shared and arm-specific inputs, we designed the attention mask for VL. as shown in Figure 3a Specifically, we embed lower-triangllar masks within each arm's 's region while treating the shared modality as fully accessible. Each arm also attends to half of the other's 's tokens, enabling symmetric cross-arm interaction without violating autoregressive constraints.
4.3 MIXTURE-OF-EXPER INTEGRATION
In TwinVLA, feeding shared inputs (l, lego): redundantly to both VLMs significantly increases VRAM usage. To address this, we process shared tokens single sequence by employing MoE
5

---

Published as a conference paper at ICLR 2026
Conson input Left arn Right arm
Left
Common inpuc
Right are
FFN
FFN
Ave
Joint Actencion
Projection Layer
Language Instruction
Vasking
(a) Causal joint attention mask
(b) Transformer block of TwinVLA
Figure 3: (a) Causal attention mask for joint attention. It preserves causality while processing shared, left, and right inputs parallel. (b) Twin joint attention mechanism. The two VLMs share information, and the shared modality (1, lego) is further processed by MoE to more efficiently leverage both VLMs.
mechanism that dynamically routes shared tokens between the two VLM experts:
MoB(z) =
When (2) + (1 When) FiN
(3)
For calculating When. add linear layer that takes the embedding input and outputs the weights via a softmax function. For other components like Projection, LayerNorm, we implement an output- averaging strategy inspired by task arithmetic (Tang al., 2024). By processing inputs through both backbones and averaging their outputs, we functionally simulate a shared layer without physically merging parameters (see Figure 3b center). This efficient design reduces VRAM usage by 21 1%, enabling training with batch size of 8 on a single 40 GB GPU.
Attention re-weighting. potential side effect of introducing new arm-specific tokens is that the model's 's learned attention patterns can be disrupted, shifting focus away from the pretrained shared modalities. To mitigate this and preserve the valuable pretrained knowledge, we reaseale the attention scores for the shared modality (Algorithm 4.. This maintains pretrained modality importance, allowing the model to bypass an initial adaptation phase and focus directly on the target task-a a benefit evidenced by lower initial loss and converged loss during fine-tuning.
5 EXPERIMENTS
In this paper, we propose TwinVLA to achieve strong bimanual manipulation performance with minimal bimanual data by fully leveraging a single-arm VLA pretrained on abundant single-arm data. Our empirical studies aim to answer the following questions:
How does TwinVLA compare to state-of-le offte-of-the-arr methods across diverse bimanual tasks, without any bimanual pretraining (Sections 5.2
5.2 and 5.3
How quickly can TLi adapt to new bimanual tasks (Section 5.4
Does Twin retain core VLA properties language-following and robustness to unseen scenes and instructions (Sections 5.3 5.6)
How much does each key design choice contribute to overall performance (Section 5.7h?
5.1 COMPARED METHODS
We evaluate VLA against three bimanual manipulation policies, each representing different point in the design space.
RDT-1B (Lu et al.
2024): This serves as our direct baseline. With a comparable size(1.2B VWin 1.3B parameters), it represents the standard monolithic approach that requires

---

Published as a conference paper at ICLR 2026
Anubis robot
Put carrot into bag
Fold towel
Tabletop
towel
Brush dustpan
haxkey
tasks)
(a) Real-world tasks
(b) Simulation tasks
Figure 4: Experimental setups. (a) We evaluate TwinVLA on five real-world bimanual tasks using an Anubis robot. (b) We further analyze Twin on a large suite of simulation tasks: tasks in Tabletop-Sim and 50 tasks in RoboTwin 2.0.
substantially larger resources (~2,4000h data, ~1,440 H100 days vs. ~800h single-arm data, 25 H100 days).
TT (Black al. 2024): We include this as an upper-bound, as this is 3.aa-mrareetter VLA trained on over 1OK hours of proprietary robot data. Our goal is to assess how closely TWi can approach this performance ceiling with far greater efficiency.
Diffusion Policy (DP) (Chi al. 2024a): This is strong baseline method in low-data regime with 271M parameters, used to demonstrate the crucial benefits pretraining.
Diffusion Policy
RDT-1B
TwinvLA
Ri (SOTA Reference)
100%
(60
Success Rates
71.
60%
00.0
60.1 2
40%
30.0
20%
15.1
Fold towel
Extract hexkey
Carrat
Brush dustpan Take towel off Average
Figure 5: Success rates on real-world tasks. TwinVLA outperforms RDT-1B and DP on average.
Moreover, TwinVLA shows comparable performance with To while trained only on target data.
5.2 REAL-WORLD EXPERIMENTS
Environment. For world experiments, we use dual-arm robot, Anubis (Kang et al. 2025), as shown in Figure 4a Anubis has two 6 DoF arms with parallel-jaw grippers. The robot is equipped with two wrist-mounted cameras and single ego-centric view camera.
Tasks. We design five long-horizon tabletop manipulation tasks, which require careful coordination and accurate motions: Fold towel, Extract hexkey, Carrot to bag, Brush to dustpan, and Take towel off, and one task set, Put X into pot. We collect 50 episodes for each task using absolute EEF control. Each method is fine-tuned for each task and evaluated with 20 rollouts.
Results. As presented in Figure 5 our model, TwinVLA, significantly outperforms RDT-IB. This achievement remarkable considering the data disparity: VLA is pretrained on just ~-80h of single-arm data, in contrast RDT-1B's usage of ~2,400h dataset mixed with bimanual trajectories, which highlights the data efficiency of our approach. While DP's low performance confirms the necessity of pretraining, mo achieved the highest overall performance with significantly higher costs.
7

---

Published as a conference paper at ICLR 2026
Diffusion Policy
RDT-1B
TWinVLA
(SOTA Reference)
100%
RobeTwin
Tabletap- -Sim
Success Rates
80%
$1.5
60%
42.0
42.5
45.
4
45.2
40%
28.6
24.3
21.4
0%
Easy
Hard
Easy
Hard
Average
Figure 6: Average success rates for diverse bimanual tasks. Despite being pretrained solely on single-arm datasets, Twin VLA outperforms other methods except Tg.
5.3 SIMULATION EXPERIMENTS
RoboTwin 2.0. We use the RoboTwin 2.0 benchmark (Chen al. 2025a), consisting of 50 bimanual tasks. Adhering to the official evaluation protocol, we fine-tune a model per task with 50 generated demonstrations and perform 100 test rollouts under both "Easy" and "Hard" settings. For Easy tasks, test scenes match the training data, but the instructions are novel. The Hard tasks introduce variations in texture, object position, and height. For compared methods, we use results reported from RoboTwin 2.0 (Chen al. 2025a).
Tabletop-Sim. To assess dexterous scenarios beyond tasks in RoboTwin, we develop Tabletop- Sammettp-Simt tabletop simulation environment based on tm_crolool (Tunyasuvunakool al., 2020) and assets from ALOHA2 (Team et al. 2024) and GSO object dataset (Downs et al.J 2022). We design representative tasks that require precise bimanual coordination. Specifically, define four single- tasks and one multi-task: box into pot. In the "Hard" tasks, we vary background textures and objects. We collect 50 episodes on each task using absolute EEF control, and fine-tune a model per task, and perform 500 evaluation rollouts for both "Easy" and "Hard" settings.
handover box. choes-table
lift-box, and put
Results. The results in Figure&jshow the average success rates of TwinVLA and compared methods. DP, trained from scratch, shows the worst performance, highlighting the importance of pretraining. Once again, we observe that TWinVLA outperforms RDT-1B 1B in most scenarios, except for the RoboTwin Hard tasks, and achieves comparable performance with To by effectively leveraging single-arm data and modularity of bimanual manipulation. Notably, in Tabletop-Sin Easy tasks, VL. even outperforms To, which is trained on an extensive corpus of high-quality bimanual pretraining data. This demonstrates Twin VLA's advantages in scenarios demanding higher dexterity and significant bimanual coordination.
Palicy
5.4 DATA EFFICIENCY
80%
RDT-18
Success Rates
TWINVLA
60%
TwinVLA exhibits data efficiency in two key aspects: pretrain- ing and fine-tuning. For pretraining, efficient because does not require supplemental bimanual data. For fine-tuning, it learns new tasks rapidly because its structural inductive bias facilitates the efficient transfer and application of its pretrained single-arm knowledge. We validate this efficiency Tabletop- Sim Easy environment, comparing model's average success rates with varying amounts of demonstration data. As illus- trated in Figure 7 TwinVLA exhibits steep learning curve. Despite a modest start with 20 demonstrations, it quickly sur- passes the performance of RDT with just 50 demonstrations, highlighting its exceptional data efficiency.
40%
35 Number of Demos
Figure 7: Average success rates the Tabletop-Sim Easy tasks. Models are evaluated after fine- tuning with 20, 35, and 50 demon- strations.
Our simulation setup is similar to the concurrent work Aloha-Sim, released by Google DeepMind (Google
Mind 2025).
8

---

Published as a conference paper at ICLR 2026
5.5 POLICY ROBUSTNESS
One of the advantages of VLAs is their robustness to unseen situations and novel language instructions, thanks to pretraining. As shown in 6 TwinVLA outperforms RDT-1B by 3.3% even in the Hard setup of Tabletop-Sim, which involves different textures and objects.
The RwboTwin benchmark, both in the Easy and Hard setups, uses evaluation language instructions that are unseen during training. Here, TWA again shows 7.48% better perfor- mance than RDT-1B 1B in the Easy setup. Although TwinVLA's performance on the RoboTwin Hard tasks is 3. 3.72% lower than that of RDT- 1B, it still outperforms non-pretrained Diffusion policy by 9.38%. This result demonstrates that VLA pos- sesses sufficient robustness bimanual VLA. even without being pretrained on large-scale bimanual manipulation data.
Table Comparison of success rates for the Fold towel task in challenging scenes.
Model
Low light
With distractors
RDT
15.0%
15.0%
Na TwinVLA
40.0%
60.0%
In Table 1 we additionally compared success rates in unseen real-world settings (see Fig- specifically low-light and distractor distractor-heavy environments using the Fold towel task. Twin V LA is robust lighting changes but less effective with distractors. Meanwhile, To works robustly in both cases, and RDT-1B achieves the lowest success rates.
ure 13
5.6 LANGUAGE FOLLOWING EVALUATIONS
known challenge that fine-tuning VLMs robotic data can degrade their ability faithfully follow nuanced instructions. We therefore evaluate how effectively our model preserves this core capability in a multi-task setting. We evaluated the "Put into pot" task across both simulation and real-works settings. As observed in Figure 8a TwinVLA outperforms both RDT- IB and To- We believe this performance stems from effectively preserving the knowledge acquired during single-arm pretraining through careful fine-tuning.
100%
Swal Morld
76%
75.0
74.7
Tabletop-S00 Easy
85.6
73.6
73%
71.2
Success Rates
70%
Success
World
62.9
60%
35.0
RDT-18
TwinVLA
TwinVLA
w/o fe-weighting
w/o VoE
w/o Joint attn
Scratch
(a) Language task results
(b) Ablation results
Figure 8: Language following task and ablation results. (a) We evaluate average success rates on the language following tasks in the real world and Tabletop Sim. (b) Ablation studies in the real world and Tabletop-Shn Easy tasks.
5.7 ABLATIONS
In this section, we conduct a sequential ablation study to analyze the cumulative impact of our key design choices on performance. Starting from the full TWin model, we progressively remove each component specific order: first Attention Re-weighting, followed by MoE integration, and finally Joint Attention. This method reveals how performance degrades as each component of our architecture is stripped away. The results on our real-world and Tabletop-Sin Easy tasks are reported in Figure 8b
Attention re-weighting. Removing the attention re-weighting mechanism (w/o Re-weighting) increased the initial fine-tuning loss by 40% and decreased final performance by 1.1 % and 4.0% in simulation and real world, respectively. This demonstrates that our re- weightgg strategy successfully mitigates the input distribution shift between pretraining and fine-tuning.

---

Published as a conference paper at ICLR 2026
MoE integration. Building on the previous ablation, next remove the MoE integration (w/o MoE). This additional change increased the token sequence length by 28 % and increased VRAM usage by 21 %, making VLA training more burdensome. Surprisingly, it also further decreases the success rate by 1.1 % and 5.0 % suggesting that MoE. integration eliminates redundant processing of shared inputs while maintaining the performance.
Joint attention. Lastly, removing the joint attention mechanism (w/o Joint atm) causes the most significant additional performance drops of 4.0% and 27.0% in simulation and the real world, respectively. This impact is particularly pronounced in real-world tightly coupled bimanual tasks, confirming that joint attention is critical mechanism for bimanual coordination.
Effect of single-arm pretraining. As a separate, foundational experiment, we assess the role of pretraining by training model from scratch without OXE dataset (Scratch). This resulted in a 4.6 % performance drop in simulation and stark 46.0% % in real world. This result confirms that effective cross-arm coordination is essential for bimanual manipulation and validates joint attention as the critical mechanism for achieving it in our model.
Twin structure. While we have confirmed that joint attention effectively connects the two modules, a crucial question remains: how does this approach compare to monolithic model that is inherently unified from the start? To answer this, we revisit our comparison against RDT-1B, monolithic model of comparable 1.2B parameter size. The results telling: TwinVLA outperforms RDT- IB by 26.0% % in the real world, 5.0% in simulation, and 21.8% in language-follown following tasks on average. This provides strong evidence that the inductive bias from the Twin Structure itself is highly beneficial for bimanual manipulation, validating our design choice over a monolithic approach.
6 LIMITATIONS
Generalization remains limited due the visual disparity of two arms, which differs from the single- arm pretraining distribution. Future research into mechanisms that prevent this could address data scarcity by integrating diverse data, while also improving the model explainability and the better generalization ability to unseen tasks.
Moreover, we adopt absolute end-effector (EEF) pose control, its embodiment-agnosii nature facilitates single-arm transfer, unlike DOF-specific joint positions. Future exploration of relative absolute actions (Chi et al. 2024b) or shared representations could further enhance transfer efficiency.
7 CONCLUSION
In this paper, introduce TwinVLA, data-efficient VLA model for bimanual manipulation. VLA provides a new perspective on solving bimanual manipulation under scarce bimanual data by leveraging abundant single-arm datasets. From small amount of bimanual demonstration data, WWin learns to coordinate two copies of a SingleVLA pretrained on large-scale single- arm data via our proposed method. Through exhaustive experiments both in the real world and simulation, VLA demonstrates its data-efficient learning of bimanual tasks compared to prior monolithic approaches. Beyond the bimanual setting, we believe this work serves as a blueprint for addressing inherent dataset imbalances across modalities. By illustrating how modular relationships can be exploited to bridge these data gaps, TwinVLA opens promising ways for other complex domains-suce mobile manipulation-theree broadening the impact of large-scale robotic learning.
ACKNOWLEDGMENTS
This project was supported in part by Microsoft Research Asia and the Microsoft Accelerate Foun- dation Models Research (AFMR) grant program. This research was also supported by the Institute of Information & Communications Technology Planning & Evaluation (IIP)) grants funded by the 2020-11200300 Artificial Intelligence Graduate School Program (Yonsei University); RS-2024-00436688 Global Research Support Program in the Digital Field Program), the Alchemist Project (RS- 20244000422143 143) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), and the Electronics and Telecommunications Research Institute (ETRI)
Korean Government (MSIT)
10

---

Published as a conference paper at ICLR 2026
grant funded by the Korean government (26ZR1 100, Research Intelligent Industrial Convergence). LG Electronics provided the Anubis robot, which was used for the experiments. The authors would like to thank Byeongjin Kang for the assistance with the preliminary experiments.
REFERENCES
N. Ahmed, T. Natarajan, and K.R. Rao. Discrete cosine transform. IEEE Transactions Computers, C-23(1):90-93, 1974. doi: 10. 10.11/99...197.2223711
Yahav Avigal, Lars Berscheid, Tamim Asfour, Torsten Kröger, and Ken Goldberg. Speedfolding: Learning efficient bimanual folding of garments. In IEEE/RSJ International Conference Intelligent Robots and Systems, pp. 1-8. IEEE, 2022.
Shikhar Bahl, Russell Mendonca, Lili Chen, Unnat Jain, and Deepak Pathak. Affordances from human videos as versatile representation for robotics. In IEEE/CVF Conference Computer Vision and Pattern Recognition, 2023.
Suneel Belkhale, Yuchen Cui, and Dorsa Sadigh. Hydra: Hybrid robot actions for imitation learning. In Conference on Robot Learning, 2023.
Christian Bersch, Benjamin Pitzer, and Sören Kammel. Bimanual robotic cloth manipulation for laundry folding. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1413-1419. IEEE, 2011.
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. nØ: A vision-language-action flow model for general robot control. In Roboties: Science and Systems, 2024.
Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Robert Equi, Chelsea Finn, Niccolo Fusai, Manuel Y. Galliker, Dibya Ghosh, Lachy Groom, Karol Hausman, brian ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Lucy Xi- aoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowiez, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, and Ury Zhilinsky. $Sp_[0.555 vision- language-action model with open-world generalization. In Conference on Robot Learning, 2025. URL https::/ wew.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian lchter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong Tran, Vincent Van- houcke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, and Brianna Zitkovich. Rt-1: Roboties transformer for real-world control at scale. In Robotics: Science and Systems, 2022.
Remi Cadene, Simon Alibert, Alexander Soare, Quentin Gallouedec, Adil Zouitine, and Thomas Wolf. Lerobot: State-of-the-art machine learning for real-world robotics in pytorch. httpps
//github.comm htttshub..ooo/uuuggggggaa////ooo 2024.
Lawrence Yunliang Chen. Simeon Adebola, and Ken Goldberg, Berkeley URS demonstration dataset. 2023.
Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai. Yibin Liu, Qiwei Liang. Zixuan Li, Xianliang Lin, Yiheng Ge, Zhenyu Gu, et al. Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation. arXiv preprint arXiv:2506. 18088, 2025a.
11

---

Published as a conference paper at ICLR 2026
Zhe Chen, Weiyun Wang. Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, Lixin Gu. Xuchui Wang, Qingyun Li, Yimin Ren. Zixuan Chen, Jiapeng Luo, Jiahao Wang. Tan Jiang, Bo Wang, Conghui He, Botian Shi, Xingcheng Zhang, Han Lv, Yi Wang, Wenqi Shao, Pei Chu, Zhongying Tu, Tong He, Zhiyong Wu, Huipeng Deng, Jiaye Ge, Kai Chen, Kaipeng Zhang, Limin Wang, Min Dou, Lewei Lu, Xizhou Zhuu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. CoRR, 2025b.
Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research, 2024a.
Cheng Chi, Zhenjia Xu, Chuer Pan, Eric Cousineau, Benjamin Burchfiel, Siyuan Feng, Russ Tedrake, and Shuran Song. Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots. In Robotics: Science and Systems, 2024b.
Zichen Jeff Cui, Yibin Wang, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto. From play to policy: Conditional behavior generation from uncurated robot data. International Conference Learming Representations, 2023.
Shivin Dass, Jullian Yapeter, Jesse Zhang, Jiahui Zhang, Karl Pertsch, Stefanos Nikolaidis, and Joseph J. Lim. Clyr jaco play dataset, 2023. URL https::s:://gitt cww.ccoo/c////// /ltp///aaaaa//c.aaaooo
play_dataset dataset
Ria Doshi, Homer Rich Walke, Oier Mees, Sudeep Dasari, and Sergey Levine. Scaling cross- embodied learning: One policy for manipulation, navigation, locomotion and aviation. In Confer- ence on Robot Learning, 2024.
Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke. Google scanned objects: high-quality dataset of 3d scanned household items. In IEEE International Conference on Robotics and Automation, pp. 2553-2560. IEEE, 2022.
Zipeng Fu, Tony Z. Zhao, and Chelsea Finn. Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation. In Conference Robot Leurning, 2024.
Koffivi Fidèle Gbagbe, Miguel Altamirano Cabrera, Ali Alabbas, Oussama Alyunes, Artem Lykov, and Dzmitry Tsetserukou. Bi-vla: lssionllanguagc-action model-based system for bimanual robotic dexterous manipulations. In IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2864 2869, 2024. doi: 10. 10.10099MMM549922221111311133111
Google DeepMind, Aloha-sim. Accessed: 2025-10-24.
2025.
Markus Grotz, Mohit Shridhar, Tamim Asfour, and Dieter Fox. Peract2: Benchmarking and learning for robotic bimanual manipulation tasks. CoRR, abs/2407.00278, 2024. URL https//d/do org/ 10. 485404550///0 a0t.:/.0xxii........... 002788
Minho Heo, Youngwoon Lee, Doohyun Lee, and Joseph J. Lim. Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation. In Robotics: Science and Systems, 2023.
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn. BC-z: Zero-shot task generalization with robotic imitation learning. In Conference on Robot Learning, 2021.
Zhenyu Jiang, Yuqi Xie, Kevin Lin, Zhenjia Xu, Weikang Wan, Ajay Mandlekar, Linxi Jim Fan, and Yuke Zhu. Dexmimicgen: Automated data generation for bimanual dexterous manipulation via imitation learning. In IEEE International Conference on Robotics and Automation, pp. 16923- 16930, 2025. doi: 10. (0.109//CRA4555430020111111
12

---

Published as a conference paper at ICLR 2026
Taewoong Kang, Joonyoung Kim, Shady Nasrat, Dongwoon Song, Gijae Ahn, Minseong Jo, Seonil Lee, and Seung-Joon Yi. Anubis: A compact, low-cost, compliant humanoid mobile manipulation robot. In RASE-RAS//A International Conference on Humanoid Robots, 2025.
//ras ContentListWeb_ 2.html
papercept net/ wwonferenceeessrr.paaaaaaa http::/seeccctttsoooooonnnniiiis
Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti. Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, Peter David Fagan, Joey Hejna, Masha Itkina, Marion Lepert, Yecheng Jason Ma, Patrick Tree Miller, Jimmy Wu, Suneel Belkhale, Shivin Dass, Huy Ha, Arhan Jain, Abraham Lee, Youngwoon Lee, Marius Memmel, Sungjae Park, Ilija Radosavovic, Kaiyuan Wang, Albert Zhan, Kevin Black, Cheng Chi, Kyle Beltran Hatch, Shan Lin, Jingpei Lu, Jean Mercat, Abdul Rehman, Pannag R Sanketi, Archit Sharma, Cody Simpson, Quan Vuong, Homer Rich Walke, Blake Wulfe, Ted Xiao, Jonathan Heewon Yang. Arefeh Yavary, Tony Z. Zhao, Christopher Agia, Rohan Baijal, Mateo Gua- man Castro, Daphne Chen, Qiuyu Chen, Trinity Chung, Jaimyn Drake, Ethan Paul Foster, Jensen Gao, David Antonio Herrera, Minho Heo, Kyle Hsu, Jiaheng Hu, Donovon Jackson, Charlotte Le, Yunshuang Li, Kevin Lin, Roy Lin, Zehan Ma, Abhiram Maddukuri, Suvir Mirchandani, Daniel Morton, Tony Nguyen, Abigail O'Neill, Rosario Scalise, Derick Seale, Victor Son, Stephen Tian, Emi Tran, Andrew E. Wang. Yilin Wu, Annie Xie, Jingyun Yang, Patrick Yin, Yunchu Zhang, Osbert Bastani, Glen Berseth, Jeannette Bohg. Ken Goldberg, Abhinav Gupta, Abhishek Gupta, Dinesh Jayaraman, Joseph Lim, Jitendra Malik, Roberto Martin-Martin, Subramanian Ramamoorthy, Dorsa Sadigh, Shuran Song, Jiajun Wu, Michael C. Yip, Yuke Zhu, Thomas Kollar, Sergey Levine, and Chelsea Finn. Droid: A large-scate scale in-the-wild robot manipulation dataset. In Robolics:
Science and Systems, 2024.
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan P Foster, Pannag R Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn. Open An open-source vision-language-actioo model. In Conference on Robot Learning, 2024.
Moo Jin Kim, Chellea Finn, and Percy Liang. Fine-tuning vision-aagguage-cction models: Optimizing speed and success, In Robotics: Science and Systems, 2025.
Masato Kobayashi and Thanpimon Buamanee. Bi-vla: Bilateral control-based imitation learning via vision- vision-language fusion for action generation, 2025. URL htt.ttttss///xi..coto org/ h/agg//b///// abs/2599 2509
18865
Andrew Choong- Won Lee, Ian Chuang, Ling-Yuan Chen, and Iman Soltani. InterACT: Inter- dependency aware action chunking with hierarchical attention transformers for bimanual manipu- lation. In Conference on Robot Learning, 2024. htt.::tttts//petttt
wwt.eew.weeeeeeeee www.:eeefoooon net/forum?id=
IKCRPJFPCM
Youngwoon Lee, Jingyun Yang, and Joseph J. Lim. Learning to coordinate manipulation skills via skill behavior diversification. In International Conference on Learning Representations, 2020.
Qixiu Li. Yaobo Liang. Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, Xiaofan Wang, Bei Liu, Jianlong Fu, Jianmin Bao, Dong Chen, Yuanchun Shi, Jiaolong Yang, and Baining Guo. Cogact: A foundational vision- language-acttion model for synergizing cognition and action in robotic manipulation. CORR, 2024a.
Xinghang Li, Minghuan Liu, Hanbo Zhang, Cunjun Yu, Jie Xu, Hongtao Wu, Chilam Cheang, Ya Jing, Weinan Zhang, Huaping Liu, et al. Vision-language foundation models as effective robot imitators. In International Conference on Learning Representations, 2024b.
Zhiqi Li, Guo Chen, Shilong Liu, Shihao Wang, Vibashan VS. Yishen Ji, Shiyi Lan, Hao Zhang, Yilin Zhao, Subhashree Radhakrishnan, Nadine Chang, Karan Sapra, Amala Sanjay Deshmukh, Tuomas Rintamaki, Matthieu Le, Ilia Karmanov, Lukas Voegtle, Philipp Fischer, De-An Huang, Timo Roman, Tong Lu, Jose M. Alvarez, Bryan Catanzaro, Jan Kautz, Andrew Tao, Guilin Liu, and Zhiding Yu. Eagle 2: Building post-training data strategies from scratch for frontier vision-language models. CoRR, 2025.
Weixin Liang, Lili Yu, Liang Luo, Srinivasan Iyer, Ning Dong, Chunting Zhou, Gargi Ghosh, Mike Lewis, Wen tau Yih, Luke Zettlemoyer, and Xi Victoria Lin. ffituureof-transffrmmes A sparse
13

---

Published as a conference paper at ICLR 2026
and scalable architecture for multi-mode foundation models. In Conference Parsimony and Learning, 2024.
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. In Advances Neural Information Processing Systems, volume 36, pp. 44776-44791, 2023a.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In Advances in Neural Information Processing Systems, volume 36, pp. 34892-34916, 2023b.
Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, and Yuke Zhu. Robot learning on the job: autonomy and learning during deployment. Robotics: Science and Systems,
2023c.
Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang. Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation. In International Conference on Learning Representations, 2024.
Guanxing Lu, Tengbo Yu, Haoyuan Deng, Season Si Chen, Yansong Tang, and Ziwei Wang. Any- bimanual: Transferring unimanual policy for general bimanual manipulation. In IEEE/CVF International Conference on Computer Vision, 2025.
Jianlan Luo, Charles Xu, Fangchen Liu, Liam Tan, Zipeng Lin, Jeffreyy Wu, Picter Abbeel, and Sergey Levine. Fmb: A functional manipulation benchmark for generalizable robotic learning. The International Journal of Robotics Research, 44(4):592-600 606, 2025.
Russell Mendonca, Shikhar Bahl, and Deepak Pathak. Structured world models from human videos. Conference on Robot Learning, 2023.
Yao Mu, Tianxing Chen, Zanxin Chen, Shijia Peng, Zhiqian Lan, Zeyu Gao, Zhixuan Liang, Qiaojun Yu, Yude Zou, Mingkun Xu, Lunkai Lin, Zhiqiang Xie, Mingyu Ding, and Ping Luo. Robotwin: Dual-arm robot benchmark with generative digital twins. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 27649-27660, June 2025.
Soroush Nasiriany, Tian Gao, Ajay Mandlekar, and Yuke Zhu. Learning and retrieval from prior data for skill-based imitation learning. In Conference on Robot Learning, 2022.
NVIDIA, Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi "Jim" Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, Joel Jang, Zhenyu Jiang, Jan Kautz, Kaushil Kundalia, Lawrence Lao, Zhiqi Li, Zongyu Lin, Kevin Lin, Guilin Liu, Edith Llontop, Loic Magne, Ajay Mandlekar, Avnish Narayan, Soroush Nasiriany, Scott Reed, You Liang Tan, Guanzhi Wang, Zu Wang, Jing Wang, Qi Wang, Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhen- jia Xu. Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, and Yuke Zhu. Gr0Ot nl: An open foundation model for generalist humanoid robots, 2025. URL https://arxiv org/ abs/ www.ab//........e 2503 14734
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Charles Xu, Jianlan Luo, Tobias Kreiman, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Fino, and Sergey Levine. Octo: An open-source generalist robot policy. In Robotics: Science and Systems, Delft, Netherlands, 2024.
Open X-Embodiment Collaboration, Abby O'Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acom Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Andrey Kolobov, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov,
14

---

Published as a conference paper at ICLR 2026
Dorsa Sadigh, Edward Johns, Ethan Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Felipe Vieira Frujeri, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guangwen Yang. Guanzhi Wang, Hao Su, Hao-Shu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik Christensen, Hiroki Furuta, Homanga Bharadhwaj. Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang. Jad Abou-Chakra, Jachyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jay Vakil, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go. Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang. Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi "Jim" Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip. Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J Joshi. Niko Suenderhauf, Ning Liu, Norman Di Palo, Nur Muhammad Mahi Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag Sanketi, Patrick "Tree" Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Mart" Mn-Mart in, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham Sonawani, Shubham Tulsiani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung. Vidhi Jain, Vikash Kumar, Vincent Vanhoucke, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiangyu Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Xu Liangwei, Xuanlin Li, Yansong Pang. Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yongqiang Dou, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang. Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Fu, and Zipeng Lin. Open X- X-Embodiment: Robotic learning datasets and RT-X models. In IEEE International Conference on Robotics and Automation, 2024.
William S. Peebles and Saining Xie. Scalable diffusion models with transformers. In JEEE/CVF International Conference on Computer Vision, pp. 11772418,,100 2023.
Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-actiin models. In Robotics: Science and Systems, 2025.
Gabriel Quere, Annette Hagengruber, Maged Iskandar, Samuel Bustamante, Daniel Leidner, Freek Stulp, and Joern Vogel. Shared Control Templates for Assistive Robotics. In IEEE International Conference on Robolics and Automation, Paris, France, 2020.
Erick Rosete-Bean Oier Mees, Gabriel Kalweit, Joschka Boedecker, and Wolfram Burgard. Latent plans for task agnostic offline reinforcement learning. In Conference on Robot Learning, 2022.
Norihiro Sadato, Yoshiharu Yonekura, Atsuo Waki, Hiroki Yamada, and Yasushi Ishii. Role of the supplementary motor area and the right premotor cortex in the coordination of bimanual finger movements. Journal of Neuroscience. 17(24):667-96777 1997. ISSN 0270-6474. doi: 11.522/NEEUROSC11-22--666666 24/9667
URL https:/ /ww. http::wwwwwww...ooooee jneurosci. org/ content/17/
15

---

Published as a conference paper at ICLR 2026
Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru, Yiqian Liu, Ishan Misra, Soumith Chintala, and Lerrel Pinto. On bringing robots home, 2023.
Rutav Shah, Roberto Martín-Martin, and Yuke Zhu. Mutex: Learning unified policies from multi- modal task specifications. In Conference on Robot Learning, 2023. URL httpst////pppeeeeeeeee
nett/forun?id=Pwqiqaaezz
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoe Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In International Conference on Learning Representations, 2017. URL https://openreviee net/ Fooum?id=BickMDo)
Mohit Shridhar, Lucas Manuelli, and Dieter Fox. Perceiver-actor: A multi-task transformer for robotic manipulation. In Conference on Robot Learning. 2022.
Sotiris Stavridis and Zoe Doulgeri. Bimanual assembly of two parts with relative motion generation and task related optimization. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp- 7131-7136, IEEE, 2018.
Stephan P. Swinnen. Intermanual coordination: From behavioural principles to neural-network interactions, Nature Reviews Neuroscience, 3(5):348-359 2002. doi: 10. 10.1038/nm8877.
Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang, and Dacheng Tao. Merging multi-task models via weight-ensembling mixture of experts. In International Conference on Machine Learning. 2024.
ALOHA 2 Team, Jorge Aldaco, Travis Armstrong, Robert Baruch, Jeff Bingham, Sanky Chan, Kenneth Draper, Debidatta Dwibedi, Chelsea Finn, Pete Florence, Spencer Goodrich, Wayne Gramlich, Torr Hage, Alexander Herzog, Jonathan Hoech, Thinh Nguyen, Ian Storz, Baruch Tabanpour, Leila Takayama, Jonathan Tompson, Ayzaan Wahid, Ted Wahrburg, Sichun Xu, Sergey Yaroshenko, Kevin Zakka, and Tony Z. Zhao. Aloha 2: An enhanced low-cost hardware for bimanual teleoperation, 2024. httpss/t::///ett //arxiv.aii.i///aa org/ /a//ab///44/0 2405.02292
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa. dm_control: Software and tasks for continuous control. Software Impacts. 6: 6:100022, 2020. ISSN 2665-9638. doi: https://dii.org 10. m0.1016/.ssmpa.2222 200. p11/$266963320300099
URL BtLLhttps://// http://ccceeeeeeeeccccccc//coooe
Jöm Vogel, Annette Hagengruber, Maged Iskandar, Gabriel Quere, Ulrike Leipscher, Samuel Busta- mante, Alexander Dietrich, Hannes Hoeppner. Daniel Leidner, and Alin Albu-Schäffer. Edan an emg-controlled daily assistant help people with physical disabilities. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020.
Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Max Du, Chongyi Zheng, Tony Zhao, Philippe Hansen-Esttructs Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, and Sergey Levine. Bridgedata v2: A dataset for robot learning at scale. In Conference on Robot Learning, 2023.
Peng Wang. Shuai Bai, Sinan Tan, Shijie Wang. Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. CoRR, 2024.
Philipp Wu, Yide Shentu, Zhongke Yi, Xingyu Lin, and Pieter Abbeel. Gello: A general, low- cost, and intuitive teleoperation framework for robot manipulators. In JEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 12156-12163. IEEE, 2024.
Fan Xie, Alexander Chowdhury, M De Paolis Kaluza, Linfeng Zhao, Lawson Wong, and Rose Yu. Deep imitation learning for bimanual robotic manipulation. In Advances in Neural Information Processing Systems, volume 33, pp. 2327-2337, 2020.
Sudhir Pratap Yadav, Rajendra Nagar, and Suril V. Shah. Learning vision- robotic manipulation tasks sequentially in offline reinforcement leamnigg settings. Robotica, 42(6): 2024.
16

---

Published as a conference paper at ICLR 2026
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine fine-grained bimanual manipulation with low-cost hardware. In Robotics: Science and Systems, 2023.
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. On the continuity of rotation representations in neural networks. IEEE/CVF Conference Computer Vision and Puttern Recognition, pp. 5738-5746, 2019. doi: 1..1000.2..
Xinghao Zhu, Ran Tian, Chenfeng Xu, Mingxiao Huo, Wei Zhan, Masayoshi Tomizuka, and Mingyu Ding. Fanuc manipulation: A dataset for learning- based manipulation with fanue mate 200id robot. https://siten gww.le...ooo.....ooo
com www.coom//eeeeeeee ber.klerkeeeey...
htt.:/aanummaipllatiiiooo 2023.
Yifeng Zhu, Abhishek Joshi, Peter Stone, and Yuke Zhu. Viola: Imitation learning for vision-based manipulation with object proposal priors. Conference on Robot Learning, 2022a.
Yifeng Zhu, Peter Stone, and Yuke Zhu. Bottom-up skill discovery from unsegmented demonstrations for long-horizon robot manipulation. IEEE Robotics and Automation Letters, 7(2):4126-4133 2022b. doi: 10. 200.0/....02...1444444..
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et Rt-2: asioonlanguagg-action models transfer web knowledge to robotic control. In Conference Robot Learning, pp. 2165-2183, PMLR, 2023.
17

---

Published as a conference paper at ICLR 2026
APPENDIX
A SINGLEVLA: EFFICIENT SINGLE-ARM POLICY DESIGN AND PRETRAINING
VLM pre-training
Web-scale data
Action head
Robot demonstrations
Eagle2-1B
Abuslate EST control
SipLIP
Language encoder
S
NLF
0:5M single arn deros
Frequency matching
Largagge
Common
modalities
Arm- crn-specific modalities
Figure 9: Overview of Single architecture design and pretraining method.
This section presents the design of the SingleVLA siiglle- While SingleVLA follows established VLA conventions, our key novelty a duplication strategy that enables the construction of Twin VLA. Prior 7B-scale models (Kim et al. 2024 2025 Li et al. 2024a) are prohibitively large for such duplication, motivating more efficient, lightweight Eagle2- TB (Li al. (025) based SingleVL.A (Fig. ( Since do not language head, the overall model size became O.8B. To acquire generalizable knowledge, we pretrain Single VLA on ~800h subset the OXE mix, enabling transfer across diverse environments and embodiments. Pretraining ran for 120k steps and took about 5 days cluster with 5x H100 GPUs
To ensure effective transfer to bimanual manipulation, it is crucial choose appropriate action space. Heterogeneous joint configurations across robots induce incompatible action spaces and complicate joint training. Prior work mitigates this with robot-specific decoders or high-dimensional
zero-padded spaces (NVIDIA et al.) 2025 Doshi et al.) 2024; Octo Model Team et al. 2024 Black al. 2024 Liu et al. 2024). Instead, we convert all actions into absolute end-efiector (EEF) poses, providing a consistent, semantically meaningful representation across robots that naturally extends to bimanual control. For rotation, we adopt 6D representation (Zhou al. 2019), which is well suited for neural network learning.
A.1 PRETRAINING
Si is pretrained on an OXE subset (~~800h); dataset composition and sampling rates appear in Table 2 We adopt the dataset loader from the Open (Kim et al. 2024) codebase and apply sampling according to the designated weights. Because some datasets (e.g., Kuka and BC-Z) include failed trajectories, we pre-process to retain only successful ones. Regarding the action space, we convert all actions to absolute EEF control with 6D rotations. We deliberately selected an absolute representation to mitigate the error accumulation and drift issues often amplified in high-freqq frequeryy bimanual control. Unlike absolute joint positions. however, absolute EEF poses preserve the embodimennt-ggnoste property required for heterogeneous pretraining. We define these poses relative to the robot's base frame, resulting in 10-Dimensional action space. We further apply frequency matching as described below.
Frequency matching. Robotic datasets differ in control frequency, making fixed-length action- chunk prediction misaligned in real time. For example, 20-step chunk spans ~ 7 seconds in RT-1 (Brohan et al. 2022) (3 Hz) but only ~ 1.3 seconds in DROID (Khazatsky et al. 2024) (15 Hz). Mixing low-frequency data like OXE (Open X-Embodiment Collaboration et al. 2024 with high- frequency datasets can degrade pretraining quality. Inspired by FAST (Pertsch et al. 2025), ), which uses DCT (Ahmed et al. 1974) map 1-second actions into consistent space, perform frequency matching via interpolation: all datasets are resampled to 20 Hz, improving temporal alignment and transfer high-frequency bimanual tasks.
18

---

Published as a conference paper at ICLR 2026
Table Single VLA pretraining datasets and sampling percentages.
Dataset
Sample Percentage
RT-1 (Brohan ct al. 2022
24.49%
Kuka (Ilttred) Yaday ct al.
2024)
12.40%
BridgeV2 (Walke el al. 2023
13.74%
Taco Play Klostee-Beas et al.
2022
3.10%
Jaco Play (Dass et al. 2023
0.50%
Viola (Zhu al. 2022a
1.00%
Berkeley Autolab URS Chen et al.
2023
1.28%
Stanford Hydra (Belkhale al. 2023
4.73%
Austin Buds (Zhu et al.
[022bb
0.22%
NYU Franka Play (Cui et 2023
0.88%
FurnitureBench (Heo et al. 2023)
2.40%
Austin Sailor (Nasıriny al. (022)
2.33%
Austin Sirius (Luu al. 2023c)
1.84%
DLR EDAN (shared control) (Vogel et al. 2020
Quere et al.
2020
0.05%
UT Austin Mutex (Shah al.
(023)
2.38%
Berkeley FANUC manipulation
(Zhu et al.
2023
0.82%
CMU Stretch (Bahl al.
2023
Mendonca et al.
2023
0.16%
BC-Z (filtered) (Jang et al.
2021
7.90%
FMB (Luo et al. 2025
7.40%
Dobb-E (Shahullab et al.
2023
1.50%
DROID Khazatsky al. 2024
10.70%
A.2 HYPERPARAMETERS AND COMPUTE
Table 3: Key hyperparameters for TWINVLA training.
Hyperparameter
SingleVLA
TwinVLA
Global batch size
256
8
Precision
BF16
BF16
Gradient clipping (L2)
1.0
1.0
Learning rate
1 x 10
x 10
LR scheduler
cosine
cosine
Warm-up ratio
0.01
0.05
Total steps
120k
100k
Optimizer
Adam W
AdamW
Weight decay
1 10-5 5
10 55
Adam
1 1 10
1 x 10
Vision backbone frozen
true
true
Image augmentation
true
false
Action chunk size
20
Sampling step
10
10
Table 3 summarizes training hyperparameters for SingleVLA and TwinVLA. SingleVLA pretraining used 5x H100 GPUs for about 5 days. TwinVLA fine-tuning used 1x L40S GPU for about 2 days.
A.3 SINGLEVLA VLM ABLATION
We validate SingleVLA's VLM choice in the LIBERO (Liu et al. 2023a) environment using several VLMs. The LIBERO actions are converted to absolute EEF 6D control. Due to computational limits, directly fine-tune the pretrained VLM checkpoints LIBERO (i.e., without additional pretraining on LIBERO), Each model evaluated with 500 rollouts per task suite under identical random seeds. Results shown in Table
19

---

Published as a conference paper at ICLR 2026
Table 4: Performance of different VLMs on LIBERO.
VLM
Spatial
Object
Goal
Long
Average
Qwen2VL-22 (Wang ct al. InternVL2.5- CChnn el al. Eagle2-1D IB (Li et al. 2025)
2024)
80.4%
88.6%
83.8%
43.0%
73.9%
2025b
64.6%
84.8%
78.4%
46.2%
68.5%
73.4%
85.4%
90.8%
46.6%
74.0%
Although Qwen2VL widely regarded as robust, Eagle2- achieves comparable or slightly better results while using roughly half the parameters and providing significantly faster inference. We therefore select Eagle2-1B as the VLM backbone for SingleVLA.
Table 5: Performance of pretrained SingleVLA on LIBERO.
Method
Spatial
Object
Goal
Long
Average
Vin (Fagle2-1B, 1B, no pretraining)
73.4%
85.4%
90.8%
46.6%
74.0%
Single VLA (pretrained)
92.4%
94.5%
93.5%
63. 63.7%
86.0%
Open (Kim et al. 2024)
84.7%
88.4%
79.2%
53.7%
76.5%
Octo (Octo Model Ieam et al. 2024
78.9%
85.7%
84.6%
51.1%
75.1%
After pretraining SingleVLA with Eagle2-1B, we fine-tune it on LIBERO to assess single-arm capability. As shown in Table the pretrained SingleVLA substantially improves performance and even surpasses the 7B model Open indicating that the learned single-arm policy is both effective and sufficiently strong to benefit the bimanual policy.
B TRAINING DETAILS
Table 6: Training hyperparameters for baseline models.
Method
# of params
Learning rate
Lr scheduler
Batch size
Training steps
Twin VI.A
1.3B
le-4
cosine
100k
RDT-1B
1.2B
le-4
constant
100k
DP
271M
2c-5
cosine
100k
T
3.3B
2.5e-5
cosine
100k
We the official implementation of RDT-1B IB. Diffusion Policy and mo evaluated via the public LEROBOT release (Cadene et al. (024),, with two modifications for a fair comparison. First, the LEROBOT evaluation script normalized images differently from training; corrected this match the training pipeline.
All models fine-tuned with the same number of steps and batch size that the total number of training samples is consistent across methods. For learning rates, we began with each model's default and tuned within similar compute budget. In practice, defaults worked well for DP and RDT- B. For To. we observed better final returns by slowing the cosine decay; we therefore extended the LR schedule from 30k 100k steps.
C TWINVLA DETAILS
C.I JOINT ATTENTION
The joint attention TwinVLA fundamentally almost identical to the implementation in the of
(MoT) (Liang al., (024), but applied attention reeeiginnreweighting (Ap- pendix C.3). While MoT has transformers for text, image, and speech inputs, TLi the inputs for the left and right arms correspond these.
20

---

Published as a conference paper at ICLR 2026
Algorithm 2 Joint Attention
1: function JOINTATTENTION (1 @m {Km}. {. MM)
2:
Q. K, < Concatenate [Qm h: Km (vm))
D Concatenate moddllitysspecife Q. K,
3:
$ + Softmax (QK /vd,) + M)
> Apply causal joint mask M (Figure 3a
4: 8 f ApplyReweiihting(S)
> Apply re-weighting (Algorithm 4
5:
A S.V
Calculate output A
6:
return {Am} <- Split(A)
Split output A into modality-specifff A.
7: end function
Furthermore, MoT requires operation to group mixed inputs by modality and then restore their original order. However, this process is unnecessary in TwinVLA because the inputs are fed in fixed sequence: left arm, then right arm. The detailed computation process is shown in Algorithm
C.2 MoE INTEGRATION
To enable sharing the shared inputs between the two-arm models, we duplicated the entire VLM transformer. This necessitates different strategies for sharing the FFNs and the other components. This section details the strategy used for each component of the transformer.
Feed-Forward Networks. To share FFNs, adopt the common approach of using gating-based MoE. In standard MoE, multiple FFNs are included within a transformer, and a gating mechanism activates subset for each input. In VLA, the two VLMs act distinct FFN experts.
Because shared inputs (c.g., ego-centric views or language prompts) may have asymmetric relevance for each arm, the gating mechanism learns how much each FFN should contribute to processing the shared input. This approach widely used and has been shown to improve training stability and preserve information more effectively than simple averaging (Shazeer et 2017). We computed Wiet by applying simple linear layer and softmax to the token embeddings.
Other Components. Beyond FFNs, elements such layer normalization and projection layers also require integration. For these, we apply task arithmetic (Tang et al.) 2024). merging the two VLMs via simple parameter averaging with weight 0.5, elaborated Algorithm This extends MoE-style computation to the full transformer architecture.
Algorithm 3 Integration of other components
1: Let Projection, be projection layer from each backbone b right).
2: Let LayerNorm, be layernorm from each backbone {left, right).
3: function PROI(X'*)
4: if m shared then
5: Fm <- 0.5 (Projection Projection (X
Task arithmetic
6: else
7: Fm < Projection,.. (X')
8: end if
9: return pm
end function
11:
function NORM(X"")
13: if m shared then
pm <- 0.5 (LayerNorm)an + LayerNorm (X*))
Task arithmetic
else
16:
Fm + LayerNorm, (X")
end if
18:
return Fm
19:
end function
21

---

Published as a conference paper at ICLR 2026
Learned attention weight distributions (SingleVLA)
Increased length, decreased attention weights token (TwinVLA)
centric view
Language instruction
Wrist proprio
centric vlew
Language Instruction
wrist proprio
propric
Aggregated attention weights (Each backbone of TwinVLA)
Attention weight distribution changes from pretraining
Attention weights: for arm specific, decreased for shared inputs
Ego-centric
Language
view
instruction
proprin
Figure 10: Due the increased token length and softmax normalization, each VLM of VLi refers to arm-specific inputs more than during pretraining, requiring the model to adapt.
Algorithm Attention Re-weighting
function APPEIHHREIIH a 2)
2: Create mask M, (m + shared)
Create mask for arm-specific inputs Aroweighted <- A O (M, + a (M, Apply scaling to attention weights using the mask Normalize the new weights D Return weights as a residual update for gradient flow
4: Areweighted Normalize Arewiighted
return A + (A reweighted A)
6: end function
C.3 ATTENTION RE- WEIGHHTHTI
Attention reieetghting is technique employ improve the efficiency of adapting pretrained SingleVLA into bimanual TLin Constructing TWinVLA involves adding a second set of arm-specific modality tokens. During operation, input tokens are processed by their corresponding arm's VLM backbone, pass through a joint attention layer, and then flow back to the individual VLMs. However, the softmax normalization within this joint attention layer presents challenge. Although the total sequence length doubles, the number of tokens for shared inputs remains unchanged. Consequently, the proportion of attention allocated these shared inputs is significantly diluted compared to the pretraining phase, creating a distribution shift for each VLM backbone's 's inputs, as illustrated in Figure TO
This discrepancy requires greater adaptation effort for TwinVLA during fine-tuning on bimanual tasks. To address this, we introduce a simple re-weighting trick immediately after the attention scores are calculated. Specifically, we double the attention weights corresponding the shared modality tokens and then re- re-normalize attention each VLM backbone assigns to the shared inputs, aligning it with the pretraining conditions (see Figure IT). Applying this method reduced the initial fine-tune fine-tuning loss by approximately 40%. While Twin could learn bimanual manipulation without this technique, the required adaptation
weights to sum to one. This adjustment effectively restores the proportional
22

---

Published as a conference paper at ICLR 2026
Re-weighting attention weights
Restored attention weights (Each backbone of TwinVLA)
Left-wrist proprio
centric
Language Instruction
Right
Ego-centric
Language instruction
view
proprio
view
proprio
By re- re-weighting the attention weights, we can make each VLM refer to each modality identically to its pretraining stage, resulting no adaptation and a lower initial loss.
Figure 11:
period would be substantially longer. This simple trick makes the process significantly more efficient and faster. We illustrate our implementation with simple pseudocode in Algorithm
D REAL-WORLD ROBOT EXPERIMENT DETAILS
D.1 TASK DETAILS
Carrot bag
Brush dustpan
Take towel off
Fold towel
Extract hexkey
Put X into pot
Figure 12: Initial distribution of each tasks in real-world.
To illustrate the diversity of initial configurations in our dataset, Figure shows an overlay of the first frames from all 50 demonstrations. For each demonstration, the position and orientation of the objects were randomized, resulting unique starting setup.
Low light condition
With distractors
Figure 13: Challenging scene of Fold towel task.
23

---

Published as a conference paper at ICLR 2026
Furthermore, evaluate policy robustness in the real world, tested the Fold towel task under more challenging conditions, such as reduced lighting and the presence distractors. These scenarios visualized in Figure 13
D.2 QUANTITATIVE RESULTS
Table 7: Success rates for each model across all subtasks. The best overall performance is highlighted in bold. As mo included upper-bound, this is excluded from this direct comparison.
Task
Subtask
DP
VLA
RDT-1B
First fold
0.00
1.00
0.90
1.00
Fold towel
Rotate
0.00
1.00
0.85
1.00
Second fold
0.00
0.90
0.45
0.90
Pick up Extract
0.60
0.90
0.90
1.00
Extract hexkey
0.35
0.80
0.55
0.90
Put into bowl
0.30
0.80
0.45
0.80
Pick up carrot
0.50
1.00
0.75
0.85
Carrot to bag Brush to dustpan
Put carrot Close bag
0.20
0.70
0.40
0.65
0.15
0.60
0.35
0.65
Move the brush
0.70
1.00
1.00
1.00
Pick up the brush
0.65
1.00
1.00
1.00
Put onto dustpan
0.35
0.80
0.40
0.80
Dragging
0.40
0.90
0.80
0.95
Take towel off
Half off Entirely off
0.35
0.70
0.70
0.85
0.20
0.45
0.60
0.65
We provide the quantitative results real-world experiments in subtask-leve detail in Table The results reveal the main bottleneck in each long-horizon task. First, for the two tasks, Fold towel and Extract hexkey, requiring tightly coupled bimanual coordination, the phase where both arms meet to execute the action appears to be critical. The Carrot to bag task is challenging when inserting the carrot, which requires precisely opening the bag. The Brush dustpan task's bottleneck the high-pree gigh-precision insertion of the brush into the dustpan. Lastly, in Take towel off, the final unfolding difficult cicieuulluulkkkee the simple initial steps as it requires successful switch between the arms. In the next subsection, we show qualitative results from these specific bottleneck phases.
D.3 QUALITATIVE RESULTS
Figure 14 presents qualitative results highlighting challenging situations for each task. A check mark was used when the model succeeded with a probability above 0.5, an X mark for probabilities below 0.3, and an exclamation mark icon for intermediate cases.
Carrot to bag. showed the highest success rate, followed by TLA RDT, and DP. DP failed interact meaningfully with the bag, especially struggling to grasp the cover properly. RDT failed to complete the task successfully, primarily due to its inability to accurately localize and grasp the bag's opening.
Brush to dustpan. DP struggled either to grasp the brush itself or to successfully insert it. Interestingly, the RDT managed to grasp the brush well lacked precision during insertion. In this task, VLA and demonstrated the same success rate.
Take towel off. DP mostly failed to pull the doll from distant position toward the center, while the other models succeeded in pulling it to the center but showed differences in towel removal. Both RDT and To tended to successfully remove one side of the towel and then easily remove the other side well. In contrast, VLA struggled with removing the remaining part and repeated the same action. This likely because the longer action chunk length of RDT and ma helped them overcome the multimodality challenge.
24

---

Entirely off Published as a conference paper at ICLR 2026
Task Subtask
Diffusion Policy
RDT-1B
TwinVLA
TT
Fold towel Second fold
Extract hexkey
Grab and Extract
bag
inside
Brush to dustpan
Put onto dustpan
Take off towel
Figure 14: Qualitative visualization of real world experiments.
Fold towel. To and VLA successfully completed the task. RDT also generally performed well, but occasionally failed to fully rotate the towel by 90 degrees, which caused downstream failures. DP experienced substantial difficulty with the fold-towel task and ultimately failed to solve it.
Extract hexkey. To and Twin WLA generally solved the task reliably. RDT performed the subtask of lifting the hexkey precision in grasping the hexkey once the case was lifted. DP failed both to reliably pick up the hexkey and to extract the hexkey itself.
well but often failed during extraction due insufficient
D.4 ROBOT HARDWARE SPEC
We conduct our real-world experiments using a custom-built robot named Anubis. The platform features a teleoperation system inspired by the Mobile ALOHA setup (Fu al. 2024). Each arm has 6 DoF and equipped with a parallel gripper and a wrist-mounter camera. At the center the robot, an Intel RealSense camera is mounted on height-adjustable mechanism. serving as the ego-centric view camera. Details are described Table Anubis is equipped with a 3-wheel omni-directional base that supports planar locomotion; however, in this work, the mobility feature is not utilized.
25

---

Published as a conference paper at ICLR 2026
Table 8: Anubis Robot Hardware Specifications.
Component
Specification
Base Type
3-wheel omni-directional chassis
Mobility DOF
3 (X, Y. Yaw)
Arm DOF
2 (6 DOF + gripper)
Total Action Space
17 DOF
Wrist Cameras
Intel RealSense D405
Gripper
Parallel transparent gripper (hole design, ALOHA-
style)
Power System
Greenworks 40V 5.0Ah batteries (PC, wheels
& leader/follower)
Frame
3D-printed custom components
Figure 15: The Anubis robot.
E SIMULATION EXPERIMENT DETAILS
Tabletop-Sim
EASY
HARD
Language Following
Dish Drainer
Handover Box
Dhsh Drainer
Handover Box
pot
Figure 16: Task list of Tabletop-Sim.
E. TABLETOP-SIM
To test bimanual policies in simulation, developed Tabletop-Sime new benchmark specifically engineered to evaluate dexterous manipulation skills, in contrast to other benchmarks (Mu et al. 2025) that primarily focus task diversity. The code are publicly available at htthtt::/ggitte thub com Son//jllyhoTTabletoo..oo Sim The benchmark comprises four single-task environments and one multi-takk setup. Our task selection was guided by the laxonomy DexMimicGen (Jiang al. 2025). which categorizes bimanual tasks into: (I) parallel (two arms are doing separate tasks simultaneously). (2) coordinated (two arms are closely working together), and (3) sequential (one arm completes the task, and the other arm takes over) interactions. Using a custom controller similar to GELLO (Wu et al.) 2024), we collected 50 demonstrations for each single-task and 60 for multi-task environment.
The multi-task setup a tinguge--folowwin task requiring the policy to place a specific box (out of three) into designated pot (out of two) based a language instruction. This task is designed to rigorously assess a model's instruction-following capabilities, as Assion-Language-Attiinn (VLA) models often disregard instructions after fine-tuning.
Furthermore, to evaluate policy robustness, we established two difficulty settings for the four single- tasks. The original tasks are designated the Easy setting, while Hard variant for each task incorporates challenging variations such as different textures, object models, and the presence of distractor objects. Figure [6 presenttt snapshots of each task.
26

---

Published as a conference paper at ICLR 2026
E.2 QUANTITATIVE RESULTS
This section describes the detailed results for the simulation tasks. The results for Tabletop-Sim are listed in Table while the results for the RoboTwin 2.0 benchmark in Table I0 For RoboTwin, the results for other baselines were referenced from the official benchmark results.
Although To achieves the highest overall performance, this result is unsurprising considering its larger model size and pretraining dataset. Meanwhile, TwinVLA demonstrates consistently superior performance compared RDT-1B, a model of similar scale.
Table Performance comparison on the Tabletop-Sim benchmark.
Tabletop-Sim
Dish drainer
Handover
box
Lift box
Shoes
table
Put X cube in
pot
Model
Easy
Hard
Easy
Hard
Easy
Hard
Easy
Hard
DP
0.686
0.590
0.180
0.086
0.100
0.006
0.028
0.260
RDT-IB
0.810
0.780
0.694
0.508
0.300
0.076
0.660
0.192
0.555
TwinVLA
0.954
0.836
0.780
0.530
0.452
0.044
0.848
0.306
0.806
PI-0
0.774
0.520
0.788
0.444
0.512
0.136
0.824
0.660
0.792
Table 10: Success rates of Twin VLA for 50 bimanual tasks in RoboTwin 2.0.
Task Name
Easy
Hard
Task Name
Easy
Hard
adjust bottle
0.97
0.35
place can basket
0.40
0.00
beat block hammer
0.77
0.10
place cans plasticbox
0.47
0.08
blocks ranking rgb
0.58
0.00
place container plate
0.77
0.04
blocks ranking size
0.03
0.00
place dual shoes
0.18
0.03
click alarmclock
0.33
0.01
place empty cup
0.50
0.01
click bell
0.58
0.13
place fan
0.34
0.00
dump bin bigbin
0.80
0.34
place mouse pad
0.50
0.00
grab roller
0.96
0.22
place object basket
0.48
0.03
handover block
0.17
0.00
place object scale
0.06
0.00
handover mic
0.84
0.02
place object stand
0.20
0.02
hanging mug lift pot
0.10
0.05
place phone stand
0.34
0.02
0.87
0.07
place shoe
0.48
0.04
move can pot move pillbottle pad
0.45
0.05
press stapler
0.62
0.26
0.32
0.02
put bottles dustbin
0.08
0.04
move playingcard away
0.61
0.35
put object cabinet
0.39
0.16
move stapler pad
0.11
0.00
rotate grcode
0.54
0.03
open laptop
0.80
0.17
scan object
0.11
0.04
open microwave
0.03
0.01
shake bottle horizontally
0.96
0.55
pick diverse bottles
0.16
0.08
shake bottle
0.93
0.58
pick dual bottles
0.18
0.12
stack blocks three
0.00
0.00
place a2b left
0.27
0.05
stack blocks two
0.26
0.00
place a2b right
0.15
0.01
stack bowls three
0.77
0.15
place bread basket
0.11
0.03
stack bowls two
0.84
0.11
place bread skillet
0.20
0.01
stamp seal
0.16
0.01
place burger fries
0.67
0.13
turn switch
0.25
0.15
Average
Diffusion Policy
0.280
0.006
RDT-1B
0.345
0.137
TwinVLA
0.420
0.089
Tp
0.464
0.163
27


Reviews:
[{"review_id": "UNfJAbSy9j", "reviewer": "Reviewer_yzn2", "summary": "This paper improves bimanual manipulation by combining single-arm pretraining with a Mixture-of-Experts (MoE) mechanism.\nThis approach enables the model to achieve performance close to state-of-the-art (SOTA) results while using significantly less pretraining data.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The motivation is sound, and the results demonstrate that it is a more efficient training approach.\n\nThe experimental results and supplementary materials provide additional details, and the experiments on weight changes in particular highlight the advantages of the proposed method.", "weaknesses": "1. The implementation details are not clearly described. The approach to handling bimanual manipulation appears somewhat similar to previous works, or at least it’s not evident how it fundamentally differs from other methods. More implementation details are needed from the authors to ensure an accurate evaluation.\n\n[1] Anybimanual: Transferring Single-Arm Policy for General Bimanual Manipulation\n[2] InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation\n\n2. It is unclear whether this fine-tuning paradigm, which is specifically designed for bimanual tasks, can be applied to robots with different degrees of freedom. There might be concerns regarding the generalizability of the method.\n\n3. Will the authors conduct evaluations on more challenging tasks, such as tests under varying lighting conditions or complex backgrounds? I would like to understand the current limitations or upper bound of this fine-tuning approach.", "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": "avL1ZjaKfw", "reviewer": "Reviewer_zQCq", "summary": "TwinVLA proposes a modular approach for bimanual robotic manipulation by composing two pretrained single-arm vision-language-action models into a coordinated dual-arm system, avoiding the need for dedicated bimanual datasets. Instead of training a monolithic model on mixed data, it reuses single-arm policies and fuses them for coordinated control, improving data efficiency and generalization across both simulated and real-world settings. Experiments show that this compositional strategy can match or surpass prior large-scale bimanual methods without additional bimanual pretraining, suggesting a scalable and practical path to bimanual manipulation using existing public single-arm data.", "questions": "- L469 the absolute end-effector representation is inherently not frame-agnostic. Given that relative end-effector actions are typically more suitable for embodiment transfer, could the authors clarify why the relative formulation was not adopted here, and elaborate on the rationale behind choosing the absolute representation?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The evaluation is comprehensive and convincing, covering both simulation and real-world settings. The experiments span two simulation benchmarks and physical robot trials, with tasks designed to test both coordinated bimanual manipulation and asymmetric master-assistant roles.\n\n- The method draws inspiration from neuroscience principles, providing an interesting conceptual link between biological motor coordination and modular robotic control.\n\n- The approach significantly reduces VRAM and computational overhead compared to monolithic bimanual models, enabling training and deployment on more affordable hardware and lowering practical adoption barriers.\n\n- The modular policy design offers strong scalability and flexibility, allowing pretrained single-arm expertise to be reused efficiently without requiring specialized or costly bimanual datasets.", "weaknesses": "- The core architecture and data-flow formulation of TwinVLA—arguably the central contribution—would benefit from a more formal and detailed presentation (*i.e.*, with equation and notations) in the main paper. Key notations and explanations are currently placed in the appendix, while the preliminaries and single-arm VLA review receive proportionally more emphasis. Streamlining those background sections could make room for a clearer, more rigorous exposition of the proposed twin-policy mechanism and coordination strategy.\n\n- The real-world evaluation focuses largely on master–assistant settings, where one arm primarily aids the other. Including tasks that require tighter spatial and temporal coordination—such as hand-overs, bilateral grasping, or deformable-object manipulation (e.g., folding cloth by holding both corners)—would strengthen claims about general bimanual capability. These scenarios inherently require richer information exchange between the representations of the two arms, which may challenge the current decoupled design of TwinVLA and provide further insight into its scalability to fully interdependent control regimes.\n\n- While the empirical results demonstrate benefits over monolithic bimanual models, the underlying mechanism for this advantage remains insufficiently explored. In my evaluation setting, this point is particularly relevant: as embodied datasets continue to scale, the performance gap between compositional and monolithic approaches may shift (Table 9, twinVLA vs pi0, btw, it's not a big deal). Consistent with the “bitter lesson,” it would be valuable to articulate whether the observed gains arise from modular inductive biases, improved optimization stability, or data-efficiency effects, rather than relying primarily on current empirical trends that may be influenced by the present limits of bimanual data availability. A deeper discussion here would strengthen confidence in the method’s long-term relevance and theoretical grounding.\n\nI would be inclined to raise my score if the paper organization  are further improved.", "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": "0ndpn7AER5", "reviewer": "Reviewer_Enff", "summary": "This paper addresses the challenge of data scarcity in bimanual manipulation by proposing TwinVLA, a modular and data-efficient VLA framework. The core idea is to duplicate a VLA pre-trained on abundant single-arm data into two single-arm VLAs coupled via joint attention, with MoE routing, connecting them with a lightweight module for coordination. Experimental results demonstrate that this approach achieves strong performance by fine-tuning on only a small amount of bimanual data.", "questions": "On the Language Following Evaluation (Sec 5.4): The paper claims TwinVLA's strong performance (80.6% vs. RDT-1B's 55.5%) is due to its architecture better preserving pre-trained language knowledge. How is a simpler confounding variable ruled out: that the baselines' (RDT-1B, Pi0) pre-training data mixtures were simply less effective at teaching these specific color-based instructions in the first place? Was any single-arm evaluation performed on the pre-trained checkpoints to confirm all models started with this capability before bimanual fine-tuning?\n\nAloha-Sim Easy may be too weakly bimanual. The w/o Joint attn variant (no cross-arm coordination) achieves 69.6%, only 6.2% below full TwinVLA (75.8%), suggesting these tasks may not require strong concurrent, mutually-constraining coordination. We request this 'w/o Joint attn' ablation be performed on the real-world and RoboTwin tasks to see if the coordination module proves more valuable there.\n\nOn 'Scratch' TwinVLA (71.2%) outperforming pre-trained RDT-1B (61.6%) on Aloha-Sim Easy: This is a counter-intuitive result, as one would expect the pre-trained RDT-1B to have an advantage. Could the authors analyze whether this finding points to: a) A superior inductive bias of the 'twin structure' itself for low-data bimanual learning, which is strong enough to overcome the lack of pre-training? b) A potential flaw, simplicity in the Aloha-Sim Easy benchmark?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Modular twin design. Proposes TwinVLA, a clear, practical architecture that couples two single-arm pretrained VLAs via causal joint attention, enabling coordinated bimanual control while preserving per-arm specialization.\n\nEfficiency mechanisms. Integrates MoE routing (and attention re-weighting on shared tokens) to avoid duplicate computation on shared inputs and to stabilize adaptation, yielding a favorable compute/memory profile.\n\nData efficiency. Achieves strong performance with only a small number of bimanual demonstrations for fine-tuning, offering a realistic path to leverage abundant single-arm corpora without large-scale bimanual pretraining.", "weaknesses": "Architectural advantage not causally established: The paper argues that the \"twin structure\" is superior to a \"monolithic\" one by comparing TwinVLA (1.3B) to RDT-1B (1.2B) . However, this comparison is confounded by major differences in their pre-training data and recipes. Without a size-matched monolithic VLA trained on exactly the same 0.5M single-arm pretraining data and the same 50 bimanual demos (with equal token/compute), the observed gains cannot be causally attributed to the twin.\n\nBimanuality insufficiently demonstrated in real-world tasks: The three real-world tasks appear predominantly sequential—two single-arm subtasks executed in series—rather than requiring concurrent, mutually constraining coordination. In the absence of tasks that necessitate simultaneous bilateral coupling (e.g., coordinated lifting/transport of a single large object), the evidence for learned genuine bimanual synergy remains weak.", "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": "d4iIUVAlYD", "reviewer": "Reviewer_5ANG", "summary": "This paper proposes TwinVLA, a data-efficient framework that finetunes a pre-trained single arm VLA into a bimanual VLA without requiring any bimanual pretraining. The proposed method utilizes a joint attention mechanism of two identical pre-trained single-arm policies to adapt the bimanual action space. The experiment results show that TwinVLA achieves competible performance on downstream tasks compared to several bimanual VLAs that were pre-trained on large-scale bimanual manipulation data.", "questions": "The paper indicates that TwinVLA performs poorly on the RoboTwin-Hard tasks. Is there a more in-depth analysis of this critical limitation? Furthermore, the authors also mention that TwinVLA exhibits weak generalization to unseen tasks. Do the aforementioned results reveal inherent weaknesses in this coordination mechanism's out-of-distribution generalization capability? Given that VLAs are expected to demonstrate strong generalization on unseen tasks, does this mean we still cannot avoid relying on large-scale bimanual pre-training data to achieve this goal?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The single-arm policy can be trained to execute bimanual manipulation tasks without requiring any bimanual pre-training;\n2. The proposed model can achieve competible performance compared to SOTA bimanual VLAs such as $\\pi_0$ and RDT with much less training cost.", "weaknesses": "1. The paper has limited experimentation in real-world scenarios (only 3 tasks) and lacks comparisons with more bimanual VLA baselines;\n2. There is a noticeable performance gap between TwinVLA and both $\\pi_0$ and RDT in Hard tasks of the RoboTwin2.0 benchmark. More importantly, yet the study omits a detailed analysis of it.", "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)