VLBiMan: Vision-Language Anchored One-Shot Demonstration Enables Generalizable Bimanual Robotic Manipulation
Authors: Huayi Zhou
Kui Jia
Venue: ICLR 2026 Poster (2026)
Keywords: Bimanual Manipulation
Single Demonstration Learning
Vision-Language Grounding
Skill Generalization
Primary area: applications to robotics, autonomy, planning
TL;DR: We enable training-free generalization of bimanual manipulation from a single human demonstration via spatiotemporal decomposition and vision-language anchored adaptation, achieving robust skill transfer and composition in dynamic environments.

Abstract: Achieving generalizable bimanual manipulation requires systems that can learn efficiently from minimal human input while adapting to real-world uncertainties and diverse embodiments. Existing approaches face a dilemma: imitation policy learning demands extensive demonstrations to cover task variations, while modular methods often lack flexibility in dynamic scenes. We introduce VLBiMan, a framework that derives reusable skills from a single human example through task-aware decomposition, preserving invariant primitives as anchors while dynamically adapting adjustable components via vision-language grounding. This adaptation mechanism resolves scene ambiguities caused by background changes, object repositioning, or visual clutter without policy retraining, leveraging semantic parsing and geometric feasibility constraints. Moreover, the system inherits human-like hybrid control capabilities, enabling mixed synchronous and asynchronous use of both arms. Extensive experiments validate VLBiMan across tool-use and multi-object tasks, demonstrating: (1) a drastic reduction in demonstration requirements compared to imitation baselines, (2) compositional generalization through atomic skill splicing for long-horizon tasks, (3) robustness to novel but semantically similar objects and external disturbances, and (4) strong cross-embodiment transfer, showing that skills learned from human demonstrations can be instantiated on different robotic platforms without retraining. By bridging human priors with vision-language anchored adaptation, our work takes a step toward practical and versatile dual-arm manipulation in unstructured settings.

Published as a conference paper at ICLR 2026
VLBIMAN: VISION-LANGUAGE ANCHORED ONE- SHOT DEMONSTRATION ENABLES GENERALIZABLE BIMANUAL ROBOTIC MANIPULATION
Huayi Zhou' Kui J
The Chinese University of Hong Kong, Shenzhen Dexcee,, Shenzhen
zhouhuayi@cuhle edu. en, kui jia@cuhk..... edu. cn
https: wrtteahhhh.itttuuuu wttpro//cts/////oooooopppostsset /aa
ABSTRACT
Achieving generalizable bimanual manipulation requires systems that can learn efficiently from minimal human input while adapting to real-world uncertainties and diverse embodiments. Existing approaches face a dilemma: imitation policy learning demands extensive demonstrations to cover task variations, while mod- ular methods often lack flcxibility in dynamic scenes. We introduce VLBiMan, framework that derives reusable skills from a single human example through task-aware decomposition, preserving invariant primitives as anchors while dy- namically adapting adjustable components via rision-language grounding. This adaptation mechanism resolves scene ambiguities caused by background changes, object repositioning, visual clutter without policy retraining, leveraging seman- tic parsing and geometric feasibility constraints. Moreover, the system inherits human-like hybrid control capabilities, enabling mixed synchronous and asyn- chronous use of both arms. Extensive experiments validate VLBiMan across tool- use and multi-object tasks, demonstrating: (1) drastic reduction in demonstra- tion requirements compared to imitation baselines, (2) compositional generaliza- tion through atomic skill splicing for long-horizon tasks, (3) robustness to novel but semantically similar objects and external disturbances, and (4) strong cross- embodiment transfer, showing that skills learned from human demonstrations can be instantiated on different robotic platforms without retraining. By bridging hu- man priors with vision- laiiuale anchored adaptation, our work takes a step to- ward practical and versatile dual-arm manipulation in unstructured settings.
INTRODUCTION
Recent years have witnessed rapid progress in embodied robotic manipulation, particularly under the paradigm of visuomotor imitation learning through large-scale teleoperated demonstrations Fang et al. (2024a); Khazatsky et al.) (2024); O'Neill et al. (2024); Bu et al. (2025). By collecting thou- sands real-world samples for each task and object setting, Vision Languugg-naueecee
(VLA) models Team et al. (2024); ; Kim et al. a (2024); Lin et al. (2055) are trained to directly map raw sensory inputs to motor commands. This end-to-end approach avoids explicitly modeling task- or object-specific priors (even for challenging cases involving deformable or articulated objects), by embedding such complexities into high-dimensional latent representations. Such strategies are especially compati- ble with high-DoF collaborative scenarios like bimanual manipulation, enabling impressive perfor- mance on long-horizon tasks, as demonstrated by works such as ALOHA series Zhao et al. (2023a); Fu et al. 2024); Aldaco et al. (2024); /Zhao et al. (2024). RDT-1B Liu et al. (2025a), To Black al. (2024). and FAST Pertsch et al. (2025). However, this line of research is bottlenecked by its re- liance large-scale data collection and retraining cycles: adapting new objects or tasks typically demands a full demonstration pipeline and model retraining, hindering scalability in open-world settings with unbounded task-object combinations and robot types.
To alleviate this, recent efforts have embraced modularized VLA pipelines that leverage the gener- alization capabilities of pre-trained LLMs Achiam al. (202)) and VLMs Radford al. (2021):
Xiao et al.] (2024). These models are repurposed to handle perception and semantic grounding, while downstream motion execution delegated either optimization-
controllers or pre-
trained visuomotor modules such as atomic skills or diffusion policies Chi et al. (2023); Ze et al.
"The corresponding author.

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Published as a conference paper at ICLR 2026
One-Shot
VL Biai
Generalizable Bimanual Manipulation
Demonstration
Pour water from bottle into cup.
rimmnncid
Cortnlator Arms
Arms
Task
Decompostion
grasp
Invariant Primitives
align
place
lift up
Carry
Adjustable
Components
pour
inserting
pouriing
Adlppting
Uneven Illumination
Vision-Language Models
Anchoring
Reusing
spoon
funnel
Tool-based Manipulation
Task-Aware Adaptation
and Composition
Execuling
bimanual
trajectories
unscrew
reorient
BOuliny
unscrew
Dynamic Interference
Cross-Embodiment Transferring
Figure 1: Left: Taking pouring water as an example, we sketch the entire process of VLBiMan based on the one-shot demonstration. Right: VLBiMan can achieve generalizable bimanual manipulation on a variety of complex contact-rich tasks without retraining, robustly coping with diverse scenarios.
2024); Yang et al. (2024). Reinforcement learning in simulation also serves as strategy for learn- ing skill speeiffc controllers Xie et al. (2020); Chen et al. (2022); Yuan et al.] (2024b). This modular design allows robotic agents to inherit part of the generalization capability from foundation mod- els, while maintaining ficxibility and interpretability. A common practice in these pipelines is to define generalizable representations (e.g., keypoints, affordances and correspondences),
struc-
tured anchors between perception and control. For instance, ReKep Huang et al. (2024b) plans robot motion by anchoring multiple predicted relation points, MOKA |Fng al. (2024b)
tracts gre-ggrinedd functional regions via multi-modal visual question answering, and RobotPoint Yuan al. (2024a) identifies object-centric task-relevant point clusters. Such approaches demon- strate that irpoint-afffrdance abstractions are effective for transferring behavior across objects, viewpoints, or instances, and have become a cornersoone generalizable manipulation.
Building on this insight, we propose VLBiMan for one-shot bimanual manipulation that leverages vision- laniual anchoring without retraining. Our approach also relies on object-centric representa- tion points, but rather than predicting them via learned networks, we utilize VLMs to perform stable and robust object segmentation, followed by two heuristic strategies for anchor selection: geometric center of masks and plane-contact points. These anchors, though reminiscent of affordances, far more controllable and lightweight. Unlike prior zero-shot methods Huang et al.) (2024b) that require fragile prompt engineering and suffer from unreliable trajectory execution, framework is demonstrationncomdiiie conditioned: we structure the action plan based on a one-shot, fine-labeled demon- stration, then adapt it using sanguage-grounded grounded object anchors and motion optimization techniques. This enables robust execution on complex bimanual tasks while reusing invariant sub-skills.
Our methodology unfolds in three stages: (1) Twsr-Aware Bimanual Decomposition, which splits the one-shot demonstration into semantically meaningful left/rghtt arm primitives with inter-arm depen- dencies; (2) Vision-Language Anchored Adaptation, which grounds the invariant motion primitives onto new scenes by aligning demonstration anchors with newly segmented objects via VLMs; (3) Autonomous Trajectory Composition, which composes new robot trajectories through kinematics- aware blending of adapted sub-skills, ensuring smooth coordination under scene variations. The related illustrations can be glimpsed in Fig. T and Fig. 2 VI.iMMnn actually inspired by a key principle: what to achieve matters more than how to execute it. For instance, rather than mim- icking the exact poses or insignificant diversities involved pouring water, our approach focuses on capturing and re-instantiating the relative spatial relationship between the cup and bottle, empha- sizing coordination rather than absolute motion. We validate VLBiMan across ten diverse bimanual tasks (including six basic bimanual skills, two cong-horizon tasks consisting of skill combinations, and two multi-stage tool-use use tasks), demonstrating superior generalization and minimal engineering overhead compared to prior strong baseline methods.
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To summarize, contributions are follows: (i) We propose VLBiMan, novel framework that enables generalizable bimanual manipulation through one-shot demonstration and vision-language anchoring, without retraining. (ii) We introduce task-aware motion decomposition and adaptation mechanism, which reuses invariant sub-skills via object-centric anchors from VLMs and supports cross-embodiment transfer from human demonstrations to different robotic embodiments. (iii) We validate VLBiMan on ten diverse bimanual tasks, showing superior generalization, sample effi- ciency, and robustness compared to strong baselines.
2 RELATED WORKS
Generalizable Representations for Manipulation. Traditional robotic manipulation often relied
on structured representations built upon strong priors Kaclbling & Lozano- Pérezz 2013); Dantam ctt al. (2018); Migimatsu & Bohg 2020); Tyree et al. 20022). such as object geometry or rigid-body assumptions, typically via estimating 6D poses or manually specifying grasp configurations. They
are hard to scale in unstructured environments. With the rise of data-driven techniques, more flexible representations have emerged, including keypoints| Papagiannis
al. (2024); Gao al. (2024); Wen
let al.
(0024b); Grannen et al. (202T), affordances Ju et al. (2024); Nasiriany et al. (0024); Zhao
al. 2023b). dynamic flow fields Colomé & Torras (2018); Weng al. 2022) and invariant
object-centric correspondences Ko et al. (0224) Zhang & Boularias 2024) Zhang et al. (023))
Some works further leverage human demonstrations to retarget 3D hand trajectories robots Chen
et al. (2024a); Li et al. (2024); KKer et al. )2024); Chen et al. (2024b). However, these approaches often depend on private datasets, retraining. or complex retargeting pipelines, limiting scalability. In contrast, our method essentially anchors adaptation to object representive points without retraining, achieving greater efficiency and generality.
Efficient Bimanual Robotic Manipulation. Recent advances in bimanual manipulation have show- cased the power of large Vision-Language,
(VLA) models Black et al. (2024); Liu et al. 2025aa); Pertsch et al. (2025) trained extensive telcoperated demonstrations Fang al. (0024a); (
Khazatsky et al. (2024);0 O''Neill et al. (022)) [Bu et al. (2025). However, these approaches are highly suspected of lacking efficiency,
scaling to unseen objects or tasks often requires re-collecting and retraining. Alternative efforts explore leveraging large-scale Internet [Ponimatkin et al. (2025) ; Ye
et al. (2025); y; [Bharadhwaj et al. (2024) or egocentric human-hand videos Zhan al. (2024) Liu al.
2 Grauman et al. (2024): Zhao et al. (2025) ; Kareer et al. (2024), yet the embodiment gap be- tween human and robot limits direct usability. Some methods improve sample efficiency by learning visuomotor policies Chi et al. (203);; Ze et al. (2024) from a small set of real-world robot data, but their generalization remains limited. While one-shot imitation learning Wen et al. (2022): y |Bahety
et al.
2024g; Zhou et al. (2025); y; Wang & Johns (2025); Mao et al. (2023); Liu et al. (2025b): Biza
jtt al. 2023) reduces data demands, the high-dimensional action space and coordination complexity
of bimanual control hinder learning efficiency. In contrast, VLBiMan achieves efficient adaptation
from a single bimanual demonstration by leveraging VLMs to handle novel variations, while reusing decomposed task- -ikviinrriant atomic skills. These lead to both data and computational efficiency.
Large Foundation Models for Robotics. Integrating LLMS and VLMs into robotics is a prominent trend to enable generalizable agents Ma et al. 2025); Huang et al. (2025); Fang et al. (2025) y Feng
et al. 2025). LLMs are utilized for high-level task understanding and planning, such as decompos ing instructions into executable subtasks or generating scripts Liang et al. (Singh et al. (2023);
Szot et al. 2 Huang et al. (2024)). Meanwhile, VLMs facilitate visually grounded perception through semantic prompts, enabling object-level detection and segmentation. For fine-grained tasks, Visual Foundation Models (VFMs) Oquab et al. 20 Ravi et al.] (2025) are further employed to find keypoints| Saciuntt/Paaiiinniiii et al. (2024); Gao et al. (2024): Wen al. (2024b) or dense correspon- dences Ko et al. (2024); Zhang & Boularias (2024). Recent efforts like ReKep Huang et al. (2024b), MOKA Mang et al. (2024b), RobotPoint Yuan et al. (0244)), and RAM Kuang et al.) (2024) combine LLMSs and VLMs into modular pipelines that follow the serreive-undersstad--
paradigm to
achieve zero-shot generalization. These approaches often rely on engineered prompts and ambigu- ous intermediate representations (e.g., region of interest or keypoint clusters) requiring additional prot-procsssing. In contrast, VLBiMan avoids LLM-based instruction parsing and task decompo- sition, which are brittle and labor-intensive. Instead, we build one-shot demonstrations with precise action labels, using VLMs to extract semantically grounded action structures that are adap- tively composed and reused, enabling efficient and scalable bimanual manipulation.
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1. Task-Aware Demonstration Decomposition
2. VL M-based Adaptation
3. Trajectory Composition
grasp
Iit up
For changed spatial location
Adaated
Reused
rel. [Ax, Ay]
For charged spotial location
Just adjust graep actions two ams! #8 For new Instances geometries
inttal masks of traget objects in the givan demonstration
% For new instances/ geometries [Ax, 4y,
Garry
Pirst adust grasp piace actions, then aignn dual-amm coordination to pour
Vision
Language
Model
Pour water from battle into
DOUr
place
place
alin
Brocudorr Observation (showing ng RGB)
Figure 2: Framework of Vision-Language Anchored Bimanual Manipulation (VLBiMan). Taking the pouring water as an example, the paradigm consists of three stages (e.g. decomposition, adap- tation, and composition) based on given demonstration. VLBiMan achieve generalization of unseen spatial placements and category-level new instances under the same task.
METHODOLOGY
This section introduces the full pipeline of VLBiMan (F which enables generalizable biman- ual manipulation via vision-language anchored one-shot demonstration. Firstly, we present prelim- inaries, where we formalize the problem and describe the input-output configuration. Then, explain three key components: (1) Task-Aware Bimanual Decomposition in Sec. Sec. which extracts reusable atomic skills through structured trajectory segmentation: (2) Vision-Language Anchored Adaptation in Sec. S.c. which adapts to new object instances or configurations with vision-language models; and (3) Autonomous Trajectory Composition in Sec. 3.3] which composes and optimizes executable dual-arm motion plans under physical and semantic constraints.
Preliminaries. Given concise textual description of a bimanual manipulation task, together with an one-shot demonstration in a canonical scene, we aim to synthesize executable dual-arm trajec- tories through modular decomposition and adaptation in new scenes, where objects may be re- located or replaced by category-level variants. Formally, let T denote the task description and D = (c)), represent the demonstration, where O, the multimodal observation (e.g., visual frame, 6-DoF end-effector poses of both arms, and gripper states) at time and Ar is the corresponding bimanual action. We seek to learn a mapping:
FYLBIKAn : (T, (T,D,S Saww) + (Ap Ji=1
TT
where Snew denotes a new scene containing instance-level object variations or rearrangements, and Apew denotes the synthesized bimanual trajectory adapted to Snew. To achieve this, we de- compose the overall policy synthesis into reusable invariant modules and scene-adaptive variants. This requires solving three core challenges: (1) Task-object semantic grounding: aligning T with semannically--elevant objects oę in the scene via visual-language grounding, i.e., learning a map- ping 9 T - {ox K (2) Executable module decomposition: partitioning D into temporally ordered motion primitives M, with discrete boundaries t; such that each M, is either lask-invariant or requires adaptation. (3) Trajectory composition with kinematic feasibility: synthesizing new trajectory Anew by composing primitives under scene-aware geometric and kinematic constraints.
3.1 TASK- TASK-AWARE AWARE BIMANUAL DECOMPOSITION
To enable reusable and adaptable dual-arm skills, we begin by parsing the one-shot demonstration D into semantically meaningful and structurally reusable modules, which involves two sub-procedures: spatiotemporal segmentation and atomic skill extraction.
Spatiotemporal Segmentation. We record the one-shot demonstration using a third-person stereo RGB camera 10 FPS, synchronously collecting dual-arm end-effector 6-DoF poses and gripper
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pouring
reorient unscrew
tool-use: spoon
mixed position and rotation
rel. [Ax, Ay]
pptlle
transformation
rel. Ar
Axx Ay]
bosil
Sottin
rel. [Ax, Ay]
only position transformation
position + rotation
transformation
Illustrations of representative points for manipulated objects in three tasks: pouring (left), zecrient + +unscrew (middle) and tool-use spoon (right), These points will be used calculate the change in object position and orientation (not always required).
Figure 3:
where each action A E 14 consisting of 6- 6-DoF for each arm with binary gripper states. We employ a keypose-driven segmentation scheme, which are inspired by those discrete motion prediction studies James et al. (2022); ; [Shidhhar et al. 2023) Ma et al. 2024]; Ke et al. (2024). Initial segmentation be scripted and automated via heuristies: trajectory waypoints are detected based either changes in motion dynamics (e.g., velocity discontinuities, acceleration spikes) or state switches (e.g., gripper open/close transitions). Each candidate waypoint w, divides the trajectory into time slots 7; = [t,, f.+1]. The inverse kinematics (IK) solver Chitta et al. (2012); SShhummnn et al. (2014) used to validate the feasibility of trajectory segments M; = [Aitter.
states. This forms a temporally aligned observation-action sequence: D
Then, human-in-thee-oone in-the-loooopp refinement ensures spatial continuity and execution robustness. Waypoints are inspected and manually adjusted in both temporal order and spatial distribution to guarantee smooth and robust control under the segmentation policy Taog 'D {M:
Atomic Skill Extraction. To determine relkvrel modularity, we assign semantic labels to seg- ments M, by assessing object-robot couplings. For each M,, if no object is rigidly grasped, i.e., ob- ject and end-effector are not in contact, the segment is classified as pre-contact adaptation dependent and potentially variable. Once the object is grasped and rigidly coupled with end-effector (veri- fied via gripper state and object pose consistency). subsequent motion is considered task lusk-invariant such lifting dual-arm alignment. Let bind(o, r, be binary indicator of whether object is physically attached to end-effector at time t. We define a skill M, as invariant if:
Vt Tú, ww..binnoo..... r, t) 1, and geometzy(os) & geometry(of (o
(2)
where the denotes geometrically equivalent dimensions within a tolerance threshold Cg. Other- wise, we mark M; as requiring adaptation. This yields decomposition into:
= {M, [M Niw U 1/
(3)
These atomic skill modules stored for reuse and recomposition in novel scenes tasks. Some illustrations on the pouring water task can be found in the left side of Fig.
3.2 VISION-LANGUAGE ANCHORED ADAPTATION
Adaptation of variable modules Maar is anchored by semantic perception and geometric reasoning. structured into components: VLMM-ased scene understanding and VFM-based geometric feasibility.
VLM-Based Scene Understanding. We extract task-relevant prompts Ps: from the text description T. mapping them to object categories. These are passed to the VLMs (e.g., Florence-22 Xiao et al. 2024) and SAM2 Ravi al. (2025) obtain high-quality 2D semantic masks M. from the current scene observation Wher Given the robustness of VLMs to lighting variations and distractors, we leverage their segmentation results to ground physical object identity without requiring explicit detection prior 3D models.
VFM-Based Geometric Feasibility. To adapt grasping or alignment poses, we introduce a three- step process. (1) Firstly, we compute relative position transformation AT between new object placement and reference demonstration via task-specific representative points (e.g. the 2D mask centroid task-specific contact point the table-facing boundary). Examples of two kinds of representative points can be found in Fig. 3 Let p dema na denote the representative 3D positions back-projected from 2D points via stereo and calibrated camera intrinsics. The relative position shift
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is Ax = p new -p demoo. (2) To account for prientaation-sensiiiiet objects (such as pen, spoon and lying down bottle), we compute the principal axis from second-order image moments Chaumette (2004); Kotoulas & Andreadis (007)) of the 2D mask and derive relative rotation A Z(v
via angular deviation. The final adapted grasp pose T obtained by applying (Ax, A0) to the original grasp pose in robot coordinates via calibrated hand-eye transformation. (3) For category- level variation, we measure shape-induced feasibility change through height and width differences.
For example, the z-extent of the object point cloud P yields Ahg max, (Pi)) ming (Pr"). This is used to adjust vertical placement motions or inter-arm distances for tools or containers.
Notably, we avoid applying 6-DoF pose estimation Lin et al. (2024); Wen et al. (2024a) or grasp pose detection Fang et al. (2020) 2023) methods our adaptation, as they either depend on pre-defined CAD models produce ambiguous non-semantic proposals, which are fragile and unfriendly.
3.3 AUTONOMOUS TRAJECTORY COMPOSITION
After adaptation, we compose a new executable trajectory D by aligning Mi and Maar according to the original temporal structure. However, this naive assembly may suffer from infeasibility due to reachability or collision. We therefore apply two refinements:
Progressive IK Refinement: For initial grasping motions Myrasps we iteratively solve IK with interpolated splines approaching the target pose:
(n+1)= IK(T)"), T = SpiineInterp(T
T T))
(4)
where T Tatart is the continuously updated initial pose, Topal is the final goal that remains un- changed is recalculated after being disturbed by external factors (such human relocation or movement after being touched), and n represents the interpolation density (which is set to 6 in our experiments). This refinement brings closed-loop correction under object displacement.
Dynamic Collision Compensation: To reduce early contact risks, we add proximal and vertical compensation terms dasse and 82 the position item during grasp approach:
g
x0oal
+ Sease + 8,us,
(5)
where in and u, respectively represent 3D Cartesian coordinates. After full trajectory synthesis, perform one-time physical replay to observe unintended collisions and adjust motion plans cordingly. The adjusted plan remains reusable for repeated deployments the identical object.
Thanks to modularity, VLBiMan supports cross-task module assembly and long-hone long-horizon tool-based task compositions by reusing Minv across tasks. This enables not only generalization within a task, but also scalable extension new task compositions, as illustrated in Fig. ((.c).
EXPERIMENTS
We aim answer following research questions: (1) How well does our framework automatically
formulate and synthesize bimanual manipulation behaviors (Sec. 4 (2) Can our method general-
ize to novel scenarios and achieve effective combination of skills (Sec. 4.2 (3) How do individual
components contribute to the effectiveness and robustness of our system (Sec. 4.3 ? We validate VL-
4.3)
BiMan on a stationary dual-arm platform with two parallel grippers and binocular camera (Fig. Additional implementation details can be found in Supplementary Materials.
Tasks and Setups: We have designed up to 10 bimanual tasks (Fig, [). In each task, least two category-level objects with different geometric shapes are covered (Fig. 4. for comprehensively
testing the performance in the face of novel placements and instances. These tasks involve diverse skill operations, complex multiple stages, and contact-rich tool-using, which can help to test the generalization. The external dynamic interference might be involved to check robustness.
Baselines and Metric: For each task setting, we conduct 25 trials, where objects are randomly located or replaced, and the success rate will be reported. For baselines, we compare to Robot- ABC Ju al. (2024) based on keypoint affordance prediction with using AnyGrasp Fang et al. (2023) for initial grasping (After which, the remaining trajectory obtained by trivial modules combination), well as ReKep Huang al. (2024b) based VFMs (SAM Kirillov et al. (2023)

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Published as a conference paper at ICLR 2026
diippers
pressing
Inserting
plugpen
Camera
unscrew
pouring
100
reoniett
om
Julb
Audo IS
unscrew polningg
rooHion
Tunnel
InSErew
Figure 4: Manipulated object assets involved in each task, and the fixed-base dual-arm platform
Table 1: Quantitative comparison results of success rates six primary bimanual tasks/skills.
new placemenis + same objects
new placements + novel instances
Dynamic Inter-
Manipulation Method
plagpen
Lnaesting
pouring
Presging
Average Staceesss
plugpen
1039 rengriin
UDSCreW
pouri
presaing
reoriens
Average
Success
Rate
Rate
Mechanisms
11/25
09/25
US/25
05/25
01/25
03/25
26.7%
06/25
U5/25
02/25
01/25
04/25
01/25
12.7%
MAGIC
16/25
15/25
10/25
10/25
09/25
07/25
44.7%
11/25
10/25
05/25
05/25
06/25
04/25
27.3%
No
Rohot-ABC
14/25
10/25
09/25
07/25
08/25
06/25
36.0%
11/25
09/25
03/25
02/25
07/25
04/25
24.0%
ReKep
14/25
11/25
10/25
12/25
10/5
09/25
43.3%
12/25
08/25
05/25
06/25
07/25
06/25
29.3%
ReKept
19/25
13/25
17/25
11/25
63.3%
10/25
07/25
42.7%
VI.iMann
25/25
23/25
20/25
21/25
20/25
19/25
85.3%
24/25
21/25
18/25
17/25
20/25
17/25
78.0%
Mechamisms
05/25
05/25
03/25
02/25
04/25
01/25
13.3%
03/25
01/25
0025
00/25
02/25
D0/25
4.0%
MAGIC
24.7%
01/25
01/25
Robot-ABC
07/25
06/25
04/25
03/25
05/25
02/25
18.0%
05/25
03/25
0025
00/25
03/25
00/25
7.3%
Yes
ReKep
10/25
03/25
22.7%
01/25
02/25
15.3%
ReKep+
12/25
10/25
09/25
08/25
09/25
09/25
38.0%
10/25
08/25
05/25
04/25
06/25
05/25
25.3%
VLBiMan
19/25
16/25
19/25
18/25
17/25
15/25
69.3%
18/25
14/25
15/25
14/25
15/25
13/25
59.3
and DINOv2 Oquab et al. (2024) and GPT-4o Achiam et al. (023). Besides, for a convincing
comparison, enhanced ReKep+ is introduced, where we inject oracle-level initial grasp label to mitigate the impact of noisy perception. We also adapt two one-shot single-arm manipulation methods Mechanisms Mao et al. (2023) and MAGIC et al. (2025b) for our dual-arm tasks.
4.1 EFFECTIVE AND ROBUST BIMANUAL MANIPULATION WITH VLBIMAN
Firstly, we compare baselines six basic dual-arm tasks as summarized on Tab, [left. In general, our VLBiMan shows promising capabilities and advantages in various complex situations, regardless of whether the interference is applied. For example, it can timely adjust the end-effector 6-DoF pose and achieve task-related precise grasping for unseen position and orientation of objects (including pens in and inserting, or spoons in reorient), which reflects the high success rate of the initial grasping stage. For actions that require fine-grained dual-arm coordination (such as aligning the pen tip and pen cap in pl ugpen, or aligning bottle mouth and cup mouth in pourings it can always synthesize trustworthy trajectories to deal with these challenges. This ability benefits from decoupling and reusing invariant modules the greatest extent. Strong baselines ReKep/Huang al. (2024b) and Robot-ABC Ju et al. (2024) do not have such concept. For each new placement, they always need to re-plan the grasping and motion paths, which cannot fully explore and effectively utilize core components in given demonstraion. The adapted baselines Mechanisms Mao et al. (2023) and MAGIC Liu et al. (2025b) originally designed for single-arm tasks also cannot handle these bimanual tasks well, revealing the non-trivial nature of dual-arm coordination.
4.2 GENERALIZATION ON NOVEL SCENARIOS AND SKILLS COMBINATION
To prove that VLBiMan has stronger generalization, such as being able quickly transfer skills taught a single time to new category-Ievel objects, or further realize skills combination, complete complex multi-stage tool-use tasks, and transfer to other dual-arm robots. We conducted extensive experiments on six basic tasks (see Tab. right) and four long-horizon tasks (see Tab, 2 The final results again show that VLBiMan has outstanding performance and significant advantages.

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Published as a conference paper at ICLR 2026
Table 2: Quantitative comparison results of success rates on four long-horizon multi-stage tasks.
new placements + same objects
new placements + novel instances
Dynamic Inter-
Manipulation Method
F-BO ent.
+unsciow cow
unscyes eeee
spout ing
tcol tcol-use 1l--us
scoop
tool -use
funne]
Averago Success
vear ent
+unscren cew
unacras
ppur
tool -uee
scoop
tool -uge Zunnel
Average
Success
ference
Rate
Rate
Meciuamisms
05/25
01/25
12.0%
01/25
02/25
DXF25
00225
MAGIC
09/25
08/25
04/25
03/25
24.0%
05/25
04/25
01/25
01/25
11.0%
No
Robot-ABC
06/25
06/25
03/25
03/25
18.0%
04/25
02/25
00/25
01/25
ReKep
07/25
08/25
05/25
03/25
23.0%
05/25
04/25
01/25
00/25
10.0%
ReKept
11/25
10/25
07/25
06/25
34.0%
07/25
06/25
D4/25
02/25
19.0%
VLBiMau
15/25
15/25
12/25
10V25
52.0%
12/25
11/25
10/25
08/25
41.0%
Mechanisms
01/25
02/25
00/25
00/25
3.0%
00/25
00/25
00/25
00/25
0.0%
MAGIC
04/25
03/25
04/25
01/25
12.0%
02/25
02/25
01/25
00/25
5.0%
Yes
Robat-ABC
ReKep
02/25
06/25
02/25
05/25
02/25
03/25
02/25
02/25
16.0%
9.0%
01/25
03/25
00/25
03/25
01/25
00/25
00/25
00/25
6.0%
2.0%
ReKep+
08/25
08/25
05/25
03/25
24.0%
06/25
04/25
01/25
01/25
12.0%
VLBiMan
12/25
11/25
09/25
06/25
38.0%
DS/25
09/25
05/25
02/25
24.0%
grasp
grasp
grasp
grasp
blug
pour
press
plugpen
Inserting
unscrew
pouring
pressing
reorient
robale
SC000
grasp
reorient unscrew
handowar
tool-use: ScOOp
dump
gresp
1wist
unscrew + pouring
pour
handovar
tool funnel
pour
Figure 5: Visualization ten tasks executed on real robots. They are designed to validate different aspects, including (a) six dual-arm primary skills, (b) combination of basic skills for two long- horizon tasks, and (c) exploration of two multi-stage tool-use tasks.
For example, in six basic tasks, it can correctly handle unseen objects according to the VLMs an- chored adaptation, achieve stable combination of variable modules and readjusted invariant modules, and synthesize new executable trajectories. For long- long-horizon tasks, the first two are the sequential superposition of two basic tasks. The difficulty lies that new intermediate grasping stages during the task execution are introduced (e.g.. in reoriemt +unscrew, the right arm needs to pick up and straighten the lying bottle first, and then the right arm takes the bottle to perform the dual-arm collaborative unscrewing of the bottle cap). These difficulties challenge the adaptability and multi- stage compatibility. The latter two tool-use tasks naturally contain additional common sense related to affordances. well multi-object contact-intensive actions, which introduce troubles includ- ing the organic connection of sub-modules and mutual interference of multiple objects. VI.BiMan effectively alleviates these challenges with the help of powerful vision perception capabilities of VLMs and reasonable skill reuse design. While, baselines Mao et al. (2023); Liu et al. (2025b); al. (2024); Huang et al. (2024b) still perform poorly on these more complex dual-arm tasks. More importantly we can still impose external interference on these long-horizon tasks, indicating VLBi- Man more practical and feasible. Fig. s visualization results. Besides, we migrated VLBiMan to humanoid dual-arm robot to demonstrate its ability to generalize across different embodiment types. Qualitative results are shown in Fig. 6 Please refer to the Appendix for more details.
4.3 SYSTEM PERFORMANCE ABLATION AND ANALYSIS
Our modular solution has good process controllability and theoretical interpretability. We conducted the following two analyses VLBiMan: ablation studies module effectiveness and multi-factor statistics on system errors. First, we focused on four core designs (including VLMs type, initial grasp alignment, IK refinement, and collision avoidance), and checked the corresponding system performance. The results are shown in Tab. 3 can be found that choosing the more advanced VLMS has obvious adyantages, and our initial grasp adaptation scheme more robust than the non-
8

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Published as a conference paper at ICLR 2026
VLNS
SEARp
up
Garry
pace
inserting
VLMS
Dresp
Int 00
carry
pour
place
pouring
VLMe
pull
unscrew
VLMs
grasp
VLMS
grasp
nigt-
ketts
handed
handed
reorient
reorient
Figure 6: Visualization of four cross- crosseembodiment transferred tasks executed on new humanoid arms.
semantic AnyGrasp Fang et al. (2023) (where we find the one closest the demo grasp pose from many proposals for fair comparison). In addition, the kinematic optimization for trajectory synthesis much better than the trivial module stacking, which consistent with common sense.
Then, conducted statistical analysis of failed cases for results on Tab. rrihht (the interference part), and results are plotted in Fig. 7 The most prominent errors come from the initial grasp execut- ing, even though its computing is relatively more reliable (with lower error rate), which shows that performing task-related grasping in real-world is not easy, and there are a considerable proportion of singularity points or early collision problems. The second most error comes from the dual-arm coordination, which is the most core challenge of bimanual tasks. An optional mitigation solution is the closed-loop servo alignment. Finally, other items such as VLM-based perception and anchoring occupy smaller proportion, indicating that it is at least reliable for our tasks, and the lower propor- tion of trajectory optimization indicates that the overall feasibility of our solution well. Through these exhaustive analyses, we can understand the advantages and defects of VLBiMan.
Table 3: Ablation studies of VLBiMan. All trials were completed six basic tasks, under the placements novel instances evaluation, with interference.
10%
Initial Grasp Executing
6%
Dual-Am Coardination
6%
initial grasp IK collision
Avg.
45%
Iniiial Grasp Computing
VLMs type
aligmment ours AnyGrasp ours
refinement avoidance
SR
12%
VLM Perception
SAM+DINOv2 ours
35.8%
Trajectory Oplimization
31.7%
/
29.2%
Others
ours
ours
34.2%
ours
ours
59.2%
Figure 7: Error breakdown of VLBiMan.
CONCLUSION AND LIMITATION
In this work, present VIBiMMnn, novel framework that enables generalizable bimanual manip- ulation from a single human demonstration, guided by a natural language task description. Through task-aware decomposition strategy, vision-language grounded scene understanding, and geomet- ric adaptation anchored by visual representations, our approach efficiently composes executable bimanual trajectories under diverse scene variations. Without reliance on object-specific priors or pose annotations, VLBiMan achieves robust generalization across unseen object instances and an- other dual-arm robots. Extensive experiments demonstrate its effectiveness across wide range of real-world bimanual tasks, including tool use, and long-horizon compositions.
Despite promising results, VI.iMan still faces several limitations. First, it restricted to rigid objects and does not handle deformable items such cloth or rope, which require different representations and control. Second, lacks runtime anomaly detection and recovery mechanisms, making it sensitive to execution errors like slippage occlusion. Third, the capability of our ap- proach is inherently bounded by the hardware: the fixed-bar bree-base dual-arm platform limits the reachable workspace and lacks force tactile sensing. Future work could explore extending the system to mobile base to enhance spatial fiexibility, and equipping end-effectors with force or tactile sensors to enable fine manipulation of delicate or force-sensitive objects.
Limitations:

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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work was supported by the Guangdong Provincial Key Field R&D Program (Project No. 20240104), and was also funded by the Shenzhen Science and Technology Major Project (No. 202402002 and ZDCT202500011300000
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APPENDIX
This supplementary part provides detailed clarifications and additional insights to support the main paper. In Sec. A (Discussion on Bimanual Manipulation Tasks), we present the motivation be- hind the design of ten bimanual manipulation tasks, including an overview of the dual-arm robotic platform, a kask-by-task. breakdown, and the process of collecting one-shot demonstrations for each task. In Sec. (More Implementation Details of VLBiMan), we elaborate on the implementation details of the proposed VLBiMan framework, such as the object principal axis extraction algorithm based on image moments, and procedure for robust dual-arm execution under external dynamic dis- turbances. In Sec. C (More Exploration on VLBiMan Advantages and Limitations), we explore further strengths of VLn,Man, including its robustness to lighting variations and its modular struc- ture, which allows for synchronous dual-arm sub-skills to improve manipulation efficiency. We also provide additional experimental results and analyses, such as evaluating the impact of varying lev- els of external interference on task success rates, revealing the ease with which the system can be transferred across embodiments, as well as discussing some interesting empirical findings. This sec- tion also includes additional analyses introduced in four new subsections: (I) detailed discussion of the human--nn-tee -n-the-loop refinement process used during primitive segmentation, clarifying its role and negligible burden; (2) an investigation into the robustness of using simple object representing moints-sude as mask centroids or front-edge contacts for cross-ohject generalization; (3) evalua- tions of VLBiMan under cluttered scenes to assess stability in more realistic environments; and (4) ablation studies on pre-grasp interpolation density, highlighting its effect on collision avoidance and resilience to external disturbances. Finally, Sec. Sec.Diss ii the statement on the use of LLMs.
A DISCUSSION ON BIMANUAL MANIPULATION TASKS
A.1 FIXED-BASE DUAL-ARM PLATFORM
Our manipulation platform consists of a rectangular tabletop approximately 110 cm in length and 70 cm in width, equipped with two fixed-base robotic arms, parallel grippers, and a binocular vision system (see Fig.Ei of the table. This is an opposite-side configuration, which differs from the more common same-side or humanoid-style arrangements. This design significantly reduces workspace overlap between the arms, thereby expanding their combined reachable workspace. The trade-off, however, is a reduced resemblance to human-lii like coordination patterns. Each arm is mounted at the center of the table short edge, with its base extended slightly beyond the tabletop to save space.
4 in the main paper for layout). The dual arms are mounted on opposite short edges
Aubo-i5 collaborative robots]] (880mm reach) with six degrees of freedom and a maximum reach of approximately 880 mm. Note that these arms do not feature built-in force control at the joints. Each arm is equipped with DH-Roboties parallel gripper offering a max- imum width of 80 mm and an effective finger length of about 50 mm (total length approximately 160 mm, used to compensate for tool flange length). While the gripper can be controlled ar- bitrary open ratios, we restrict it to two discrete states (open and closed) across all experiments. For visual perception, we employ a binocular Kingfisher R-6000 stereo camera, capturing RGB images at 960x 540 resolution and supporting 3D scene reconstruction via calibrated stereo intrin- sics. This setup functions similarly to standard RGB-D cameras, but offers improved reconstruction quality and greater flexibility through algorithm-level customization. The camera is mounted in shrrd-person perspective, positioned approximately 20 cm horizontally and 100 em vertically from one of the long edges of the table, enabling full coverage of the workspace. Consequently, we do not employ eye-in-hand cameras at the robot end-effectors. To further demonstrate the convenient transferability of VLBiMan, as shown in Fig. & form configured in a popular humanoid style. This new platform consists of two Rokae x.Mate CR 6-DoF collaborative arms (reach: 988 mm), each equipped with a parallel gripper (Jodell Robotics RG75-300f] max opening: 75 mm). A binocular camera Kingfisher R-6000 is mounted centrally at the head position. We will present how to utilize this dual-arm platform in Sec. C.4
The manipulators
we have prepared another dual-arm robotic plat-
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Right Observatio
Obsanvation
Binccular
Inserting
22
pouring
Rakuss
Rakes
Paralel Grippers
75
reorient
100 cm
Figure 8: The another dual-arm manipulator platform (left) and corresponding manipulated object assets (right) used for the cross coos-embboiimnn evaluation.
A.2 INTRODUCTION TO BIMANUAL. TASKS
To comprehensively evaluate the dual-arm manipulation capabilities of system, we first de- sign a suite of six foundational bimanual tasks: plugpen, inserting, unscrew, pouring, pressing and reorient. Each task involves manipulating objects drawn from at least two category-level instances (see Fig. in the main paper), enabling systematic assessment the general- ization performance of VLBiMan under object instance variations. These tasks encompass broad range of atomic manipulation skills, such as single-arm actions like grasping, placing, inserting, transporting. pressing, and precise reorientation, as well as coordinated dual-arm behaviors includ- ing fix-and-unscrew, fix-and-skew-insert bilateral alignment, and handover. Together, they ensure sufficient complexity and coverage of real-world manipulation demands.
plugpen: Plug the marker body into its cap. The task begins with separated pen body and cap placed on the table. The left and right arms grasp the pen body and cap respectively, lift them, align the pen tip with the cap opening, and perform pen. The right gripper then releases, and the left arm places the assembled pen on the table. This task demands accurate segmentation of small objects, orientation-aware grasping, and near-zero- tolerance insertion. To avoid issues due to the lack of eye-in-hand cameras, configurations where the pen tip or cap opening faces downward are excluded.
nigh-precision plug-in motion to close the
insertings ing: Insert a closed marker pen into an inverted cup. The setup includes an upside- down, handleless cup and a fully assembled marker pen. The left and right arms grasp the cup and pen respectively, lift them, and the left arm rotates the cup to face upward. Simultancously. the right arm reorients the pen vertically for insertion. After aligning the two objects, the right arm inserts the pen, releases it, and the left arm places the cup back. The task requires precise rotation for object reorientation, orientation-awan grasping, and moderate-toleraace insertion.
unscrew: Open bottl by twisting the cap counterclockwise. A sealed plastic bottle containing water stands upright on the table. The left arm grasps and lifts the bottle, holding it steady in mid-air. The right arm approaches the cap vertically from above, grasps it, and performs multiple controlled counterclockwise rotations unscrew it. The cap is then placed on the table, and the bottle is set down. This task involves extremely tight grasping tolerance (for the cap), precise rota- tional control, and potentially force-sensitive unscrewing (though our gripper lacks force sensing, which may increase the failure risk).
pouring: Pour water from bottle into mug. A water-filled plastic bottle without cap and an empty mug with a handle are placed on the table. The left and right arms grasp the bottle and mug respectively, lift them, and coordinate to align the bottle and mug openings. The left arm rotates approximately 90º to pour water, then restores the bottle to an upright position. The right arm retracts the filled mug, and both objects are returned to the table. This task requires moderate- tolerance alignment, precise angular control for pouring, and careful handling of the deformable bottle body (deformation may affect the bottle's geometry and induce spill errors).
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pressing: Press a pump bottle and catch the water in cup. A shampoo bottle with a pressable nozzle and a handleless upward-facing cup are provided. The left arm grasps the cup and lifts to a tilted receiving position near the nozzle. The right arm approaches vertically and presses the nozzle to dispense a small amount of water. Both arms then place the objects back. Key challenges include precise nozzle approach, accurate cup positioning for water collection, and robust force application (although without force feedback, we rely on a fixed press depth that balances functionality and bottle safety).
reorient: Flip a spoon or shovel so that its concave side faces upward. The object starts in an arbitrary pose with the concave side down. 'he right arm vertically grasps its center and lifts it, then repositions and reorients it into a graspable pose for the left arm. The left arm then grasps the handle, the right arm releases, and the left arm completes the flipping motion to place the spoon upright on the table. This task demands precise reorientation, spatially and temporally coordinated handover, and potentially strong grasping (the left arm's handle grasp is relatively unstable and may result in object slippage during motion).
In addition to the six hase tasks, we introduce four more challenging long-horizon tasks to evalu- ate VLBiMan's 's capacity for skill composition and multi-stage adaptive control. The tasks include reorient +unscrew, unscrewt www.wwgg.nnnnnnnn tool-use spoon, and tool-use funnel. The first two are about concatenations of previously defined skills, while the latter two require tool- use behaviors that test the system's s ability to generalize across distinct affordances.
reorient eeceenttuunte tunscrew: Struighten a fallen bottle and unscrew its cap. A sealed water bottle lies horizontally on the table. The right arm vertically grasps and reorients the bottle upright, ensuring the cap faces upward The system then proceeds with the unscrewing routine. The new challenge lies in accurate estimation of the lying down bottle's 's orientation, especially the cap direction.
uns unscrewt wnssrretpouringe pouring: Open the bottle cup and pour water into a mug. A sealed water-filled bottle and an empty mug are provided. The system first performs the full unscrewing sequence, followed by the water-pouring procedure. While the skills themselves are known, the compound task increases complexity through potential error accumulation across stages.
tool-use spoon: Use spoon to transfer water from a larger bowl to a smaller one. Three objects are involved: an upside-down spoon, a large bowl filled with water, and a smaller empty bowl. The system first performs reorientation on the spoon, then uses it to scoop water from the large bowl, transport it, and pour it into the smaller bowl before returning the spoon. The task requires multiple precise reorientation and motion sequences, handling mutual visual distractions among objects, and reliably distinguishing the bowls of different sizes.
tool-use funnel: Use funnel to pour water from a mug into an empty bottle. The setup includes an upside-down metal funnel, an empty plastic bottle without cap, and a water-filled mug. The system reorients the funnel, inserts its narrow end into the bottle. then the left arm relocates the bottle near the right arm. The right arm lifts the mug and pours water into the bottle througth the funnel. This task tests multi-object coordination, spatial reasoning under occlusion, and moderate-toleraace insertion.
A.3 ONE-SHOT HUMAN DEMONSTRATION
We collect one-shot seed demonstrations for each task using kinesthetic teaching, wherein the op- erator manually guides the dual-arm robot to designated waypoints. Specifically, the full trajectory is decomposed into sparse keypoints by physically dragging the robotic arms target poses. At each pause, we record the 6-DoF' end-effector poses of both arms (relative to their respective robot coordinate frames) using a teach pendant, along with the intended gripper open/close states. Subse- quently, with objects placed at approximately fixed initial positions, the robot autonomously replays the demonstration by executing the recorded sequence of waypoints. We first move the arms via in- verse kinematics (handled by the control API), followed by gripper actions. Throughout this replay, we record synchronized observations from the binocular camera and the corresponding end-effector states at 10Hz. After collecting the demonstration, we decompose it using the task-aware strategy described in the main paper, enabling downstream skill reuse. Notably, for the composed tasks reor iereeentuue tunstrunserrr and unscrew+pourime pouringg which are essentially combinations of existing base skills, we do not provide additional one-shot demonstrations, as their behavior can be suffi- ciently inferred from the constituent components.
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Algorithm Overall Procedure of Object Orientation Estimation from 2D Mask.
Require: Binary object mask M E {0, 1) 1
Ensure:
Orientation angle 8 E 360) (degrees)
1:
Extract object contour C = {I yi h=1 from M
2:
Compute centroid (z. y) using image moments:
N
N
Fis
y4
(6)
N
N
1=1
1=1
3: Calculate second-order central moments:
(A20
Σ J
))²
No22 =
Σ(v: -))
pn
N
Σ
(at
5)
(7)
4: Construct covariance matrix: Σ = U20 M11
pun 142
5: Compute eigenvalues À X2 and eigenvectors V1, V2 of
6: Obtain principal axis direction
(azz agy V1
7: Project contour points onto principal axis: p; = (r; 2 ag + (y) D)y Vi [1,N]
8: Identify endpoints: Cmaz arg max; Pi, Gmin arg min; Pi
9: Calculate perpendicular width Wy within radius around each endpoint Ej
Determine front endpoint: Crontt (Wmaz Wmin )mmin: Cmin
11: Adjust axis direction: < -a (Efront (T, 9)) < 0)? a a
12: Compute final orientation angle: (arctan 2(0y az.) x 180 return
mod 360
Table Statistical details regarding the ten bimanual manipulation tasks defined in this study. They contain the names of the target objects involved (including their placement states), the representation points used to indicate the positions of the target objects (where MC represents the object's 2D mask centroid, and CP represents the contact point between the object bottom and the table top. Please refer Fig. in the main text for visualization), and whether the object's orientation needs to be estimated during the manipulation process.
P1 uggeen
12502E Ang
UnSczoN
posei
precsing iin
zenzient
FPO roortens
sent M0nCcreH
unscrew seeuring
Cool tool-use use
scoop
too3 -000
Zunnel
Task Name
Object
Name
body
maakeennarkerr markee
cap
pen
Staddnng standing bottle
bottle
mugcup
pumpspoon
shovel
bottle
lying
standing
bottle
mugspoon bowll
big small
funnel
standing bottle mug
Representive
Point Type
MC
CP
CP
CP CP
Orientation
Estimation?
X
X
X
X
X
X
X
V
X
x
B
MORE IMPLEMENTATION DETAILS OF VLBIMAN
B.1 IMAGE MOMENTS BASED ORIENTATION ESTIMATION
In the Vision- Vision-Language Anchored Adaptation pipeline of VLBiMan, our method requires extracting the principal axis and determining the orientation of direction-sensitive objects. This includes the marker pen in the plugpen and insert ing tasks, the spoon in the reorient and tool-use spoon tasks, as well the horizontally placed bottle in the reorient+unsern in Algorithm we adopt an object principal axis extraction algorithm based on image moments theory (Chaumette (2004); Kotoulas & Andreadis (2007). Since this algorithm relies primarily on the 2D segmentation mask of object and does not require any deep networks, its computational overhead is minimal and can considered negligible practice.
task. As shown
In general, the proposed algorithm estimates the orientation angle of object from its 2D binary mask through hierarchical analysis of geometric properties. First, the object's 's contour is extracted, and its centroid is computed using image moments. A covariance matrix derived from recond-order central moments is then diagonalized to identify the principal axis direction via eigen decomposition. To resolve directional ambiguity inherent to eigenvectors, contour points are projected onto the
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principal axis to locate two extreme endpoints. The front endpoint is determined by comparing local perpendicular widths around these endpoints, leveraging the observation that structural asymmetry often manifests as width variation. Finally, the principal axis direction is reoriented to align with the front endpoint, and the orientation angle is calculated as the arctangent of the adjusted axis vector, ensuring a continuous -003 representation. This approach robustly handles directional ambiguity while maintaining computational efficiency through moment- feature extraction. For more details on which objects in which tasks require the orientation estimation algorithm, please refer to Tab. 4 (see the last row).
Why Not Use Off-the-Shelf 6D Pose Estimation? While one may consider leveraging off-the- shelf 6D pose estimators Lin et al. (2024); Wen et al. (2024a) for object orientation extraction, we found that such solutions are unnecessary, unstable across objects, and incompatible with VLBii- Man's cross-object generalization objective. Classical and learning-based 6D pose estimators gen- erally require either (1) object-specific CAD models, (2) textured templates, or (3) category-level canonicalization priors. These assumptions are difficult to satisfy in our setup, where VLBiMan must generalize to unseen and shape-diverse everyday objects without additional training or model registration. Moreover, 6D estimators often degrade when objects lack distinctive geometry tex- ture- pree-rrecislly the case for many household items used in our tasks (e.g.. plain spoons, cylindrical pens). In contrast, our moment-based orientation estimation (Algorithm|] I only depends on the 2D) segmentation mask produced by a VL.-powered perception module, eliminating the need for any object-specific shape information. This makes the approach far more robust to appearance varia- tions and naturally compatible with VLBiMan's 's object-centric anchoring framework. Additionally, we observed in our experiments that 6D pose estimators frequently output unstable yaw angles un- der partial occlusion or when only a single RGB camera view is available, while the moment- based method remains consistent, lightweight, and easy to deploy in real-world bimanual settings.
Finally, this simplified 2D-mask driven orientation strategy is fully aligned with VLBiMan's de- sign principle-to avoid heavy perception modules that compromise generalization proven sufficient for all direction-sensitivve tasks, including p ugpen, Inserting, reor reorient, tcol-use spoon, and georient +uns crew. Hence, the choice to avoid 6D pose estimation is both practical and necessary: our goal is not to recover full metric pose, but to obtain stable, VLM VLM-compaiible orientation anchor that enables one-shot bimanual manipulation without retraining or object modeling.
it has
B.2 DYNAMIC INTERFERENCE ROBUST VLBIMAN
Thanks to the modular design of our VLBiMan system, we enable dynamic interference to be ap- plied to an object before it is physically grasped by the robot arms (that is before the object formally becomes part of robot-object composite system). Such interference may include randomly per- turbing the position or orientation of the object multiple times, without any predefined limit, until the object is successfully captured. This capability introduces significant challenges for maintaining robustness during execution, requiring a carefully structured control process to ensure system reli- ability under disturbance. To address this, we summarize dynamic closed-loop control pipeline tailored for interference robustness pre-grasping below.
Specifically, for each object to be manipulated, VLBiMan first performs continuous 2D instance segmentation and tracks the object across frames using a lightweight vision pipeline. Let M, denote the segmentation mask at time step i, and let the corresponding object pose estimation function be Foosee + (Pr, where Pt is the 2D position and 0, is the principal axis orientation obtained via image moments (refer Algorithm D.. This estimation is continuously updated and serves as input to the grasp planning module. A grasping attempt is initiated only when the variance of (Pi-k.... } and (0.-k,... .0, over a short sliding window falls below pre-defined threshold c (e.g.. absolute moving distance less than 10mm), indicating that the object has stabilized. This implicitly filters out moments of dynamic perturbation. Once the object is deemed stable, the robot executes the corresponding grasp action G(p., 0z), where G(-) denotes grasp generation function conditioned on both position and orientation. If the grasp fails (e.g., the object slips or moves significantly post-action), the system returns to the observation loop and restarts the stabilization- checking process. This mechanism ensures that the object's 's interaction policy is dynamically robust, without requiring hard-coded assumptions when or how disturbances may occur.
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Table 5: Quantitative comparison results of success rates six primary bimanual skills/tasss.
Dynamic
new placements 4 same objects
new placements + novel instances
Interference
Plugpen
insertion Ing
Unscrew
poaring
pressing
1801 Bone
pLogpan
Ansert ing
UBacTew
Booring
gressing king
spor Lone
Manipulation Method
Averaage Success
Average
Uneven Lighting
Success
Rate
Rale
Mechanisms
01/20
01/20
00/20
01/20
01/20
00V20
3.3%
00720
01
0322
0120
0600
00/20
0.0%
MAGIC
03/20
04/20
02/20
02/20
02/20
01/20
11.7%
01/20
02/20
00/2
01/20
01/20
00/20
4.2%
Robot-ABC
02/20
02/20
01/20
01/20
01/20
01/20
6.7%
00/20
06/20
0020
00/0
00/20
00/20
0.0%
ReKep
05/20
03/20
02/20
01/20
02/20
02/20
12.5%
03/20
01/20
01/20
01/20
01/20
00/20
5.8%
ReKep+
08/20
02/20
VLBiMan
14/20
13/20
14/20
15/20
14/20
11/20
67.5%
13/20
11/20
11/20
10/20
12/20
10/20
55.8%
86
Figure 9: Examples of pluqpen (top) and bressing (botto)) show that under the uneven light- ing, the system is subjected to consecutive external interferences, and tasks can still be completed.
C
MORE EXPLORATION ON VLBIMAN ADVANTAGES AND LIMITATIONS
C.1 GOOD ROBUSTNESS TO LIGHTING CHANGES
In addition to the generalization capabilities of VLBiMan with respect to spatial object positions and category-level instance variations, as demonstrated in the main text, we further explore another crucial advantage-ing robustness to lighting changes, which also constitutes an important aspect of generalizable bimanual manipulation. Specifically, we investigate the impact of uneven illumination on task success rates. For this purpose, we evaluate six basic bimanual tasks under setting where dynamic object perturbations are applied during the initial grasping phase, while also introducing uneven lighting conditions. These lighting conditions cause non- uniform brightness across the scene and cast shadows on the manipulated objects, posing new challenges to both the visual perception module and the grasp pose alignment procedure for our VLBiMan.
Thanks to the strong generalization ability of the VLMs Xiao et al. (2024) and VFMs Ravi et al. (2025) employed in our system, the detection and segmentation of target objects remain highly reli- able even under such adverse lighting. Furthermore, our method for estimating object position and orientation relies solely binary masks, which are inherently invariant lighting variations. Quan- titative and qualitative results under this setting are summarized in Tab. 5 and Fig. respectively. As shown, the effect of uneven illumination VLBiMan's 's task success rate is minimal (70.0% 67.5% for ID testing, and 59.2% - 55.8% for OOD testing).
In contrast, two baselines Mechanisms Mao et al. (2023) and MAGIC Liu al. (025b) have had obvious negative effects (13.3% + 3.3% and 3.3% + 0.0% for Mechanisms, and 24.2% + 11.7% and 11.7% 4.2% for MAGIC). For another two stronger baseline methods (Robot-ABCI Ju et al.
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(024) and ReKep Huang al. (2024b)) also exhibit substantial performance degradation (18.3%
- 6.7% and 6.7% - 0.0% for Robot-ABC. 23.3% - 12.5% and 14.2% - 5.8% for ReKep. and 37.5% + 25.0% and 25.0% - 13.3% for ReKept). This is not unexpected, as both baselines rely on inferior vision pipelines that are sensitive to lighting. For instance, AnyGrasp Fang et al. (2023), which Robot-ABC depends for grasping, never trained point clouds data contain- ing uneven illumination, and ReKep employs fragile keypoints tracking mechanism that becomes prone to false positives and missed detections under such lighting variations. Additional dynamic execution records are available in our supplementary videos.
Figure 10: Examples of synchronized dual-arm movement. Segments from top to bottom are tasks plugpen, inserting, and pouring, which have relatively high dual-arm synchronizabilitt.
C.2 EFFICIENT SYNCHRONOUS DUAL-ARM MOVEMENT
Another notable advantage of the VLBiMan system lies in its ability to support more human- like dual-arm behaviors, specifically the hybrid execution of asynchronous and synchronous arm move- ments. This capability not only contributes to the overall manipulation efficiency of each task but also serves as an essential factor for achieving generalizable bimanual manipulation. For instance, certain tasks, such lifting large balls Grotz et al. (2024); Liu al. (2024a) or bimanual occluded grasping Yamada et al. (2025) (which we have not yet explored in this study), require strictly syn- chronized dual-arm motions.
In our system, after decomposing the given one-shot demonstration, we obtain temporally indexed motion sequences for both arms. These sequences are further processed with collision-avoidame strategics under a global trajectory perspective, enabling the possibility of triggering specific motion segments concurrently. For example, in the pl ugpen task, the left and right arms move simul-
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taneously to align and close the pen body and cap; similarly, in the pouring task, both arms coordinate to bring the bottle and cup closer and align them for fluid transfer. Such synchronized exccution clearly reduces overall task duration. However, this does not imply that the task execution time is halved, as certain motion segments inherently require strictly asynchronous behavior. For example, in the unscrew task, the left must serve stabilizer to hold the bottle stationary while the right arm unscrews the cap, making simultancous execution infeasible.
To evaluate this advantage quantitatively, conducted comparative experiments on all biman- ual tasks, testing strictly asynchronous execution versus a strategy that maximally leverages syn- chronous execution wherever feasible. We observed time savings of varying magnitudes across all ten tasks, yielding an average improvement in execution efficiency of approximately 22%. Fig. [T visualizes the synchronous motion segments for some tasks, and additional dynamic comparison footage can be found in our supplementary videos.
Figure 11: Example of dynamic interferences during task execution. From top to bottom, they are segments of the dynamic closed-loop grasping phase of tasks pouring, reorient funscrew, and tcol-use funnel, where each object is manually disturbed from one to three times. The red arrow indicates the direction of the manually moved object (interfering). The cyan arrow and yellow arrow indicate the movement direction of the left and right robotic arms (chasing) respectively.
C.3 INTERFERENCE FREQUENCY AND SUCCESS RATE
We have extensively examined VLBiMan's robustness to external disturbances in both the main paper and this supplementary material, which is an essential capability for achieving highly gener- alizable bimanual manipulation. As discussed in works like AnyGrasp [ang et al. (2023), dynamic grasping presents substantial practical value while remaining a formidable challenge, even for sys- tems already equipped with strong static 6-DoF grasp pose prediction and execution capabilities. Moreover, dynamic interference robustness also provides the embodied agent with foundation for error recovery and correction mechanisms during execution, whether through end-to-end learning pipelines Black al. (2024); Liu al. (2025a): Pertsch et al. 20255 external intervention mod-
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ules such human feedbacks Wang al. (2024) multimodal large models for intermediate state evaluation Duan et al. (202bb a).
To further quantify this robustness, investigate how the number of external interferences affects setting used our previ- conduct experiments all six basic bimanual
task success rates, by extending beyond the iew-innncreneeeeeeeeeeee ously reported quantitative results. Specifically, tasks, focusing on the ID evaluations without loss of generality. We define one interference as a scenario where each object involved in the task is disturbed once. Under this definition, tematically vary the number of interferences from 0 to 5 and record the average task success rates, which are: 85.0%, 70.0%, 61.7%, 56.7%, 53.3%, and 50.8%, respectively. These results reveal clear negative correlation between interference frequency and task success rate. However, the rate of decline diminishes as the number of interferences increases, suggesting a frend of diminishing marginal impact. One plausible explanation is that as the end-effector gradually approaches the ob- ject over time, the spatial freedom available for introducing effective perturbations decreases, thus leading to more stable system performance. Fig. I provides illustrative examples of continuous dy- namic interference scenarios, and additional results showcasing closed-loop dual-arm control under such conditions be found in supplementary videos.
sys-
Table 6: Quantitative results VLBiMan's 's success rates on four transferred bimanual tasks.
new placements + same objects
new placements + novel instances
Dynamic Interference
Dual-Arm Type
Inserting ing
unscrew
pouring
reaz Lent
Average Success
insert.i Lng
unscrew
pouring Lng
reoriant Lant.
Average Snccess
Rate
Contralateral
18/20
16/20
17/0
15/20
82.3%
17/20
14/20
14/20
14/20
73.8%
Humanoid
19/20
17/20
16/20
15/20
83.8%
18/20
16/20
13/20
14/20
76.3%
Contralateral
Humanoid
14/20
15/20
14/20
13/20
70.0%
12/20
13/20
12/20
10/20
58. 8%.8%
Figure 12: Examples of four transferred bimanual tasks with synchronized dual-arm movement. Segments from top bottom tasks Inserting unscrew, and pouring, which have rela- tively high dual-arm synchronizability. The last row are examples of single-arm task reorient, explicitly examining left- or right- right-handed execution strategies.
C.4
TRANSFERABILITY OF VLBIMAN
To further assess the generalization ability of VLBiMan, we investigate its cross- enoosmembodiment trans- ferability. Specifically, evaluate how one-shot demonstration collected from human demon-
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strator can be transferred to robotic embodiment with different kinematic and actuation constraints. We report both qualitative visualizations and quantitative results, focusing on four representative manual tasks: insert ing, unserew, pouring, and reorient, with the corresponding object assets shown on the right side of Fig.
Among them, the unscrew and pour ing tasks preserve the exact same step design and final goals as the original experiments, thereby serving as direct transfer cases. The insert ing task, however, introduces embodiment-induced modifications: due the reduced maximum gripper opening width (from 80 mm to 75 mm), the manipulated cup is no longer placed upside down on the table but instead stands upright. The gripper is required to grasp the cup vertically from the rim and move it to intercept the pen held by the other arm. For the reorient task, the manipulated object replaced with horizontally placed bottle, and the goal is changed to upright the bottle (with minimum theoretical rotation of 90 degrees instead of the 180 degrees required when flipping a spoon or spatula). While this can be accomplished by a single arm, we consider humanoid dual-arm embodiment, as illustrated in the last row of Fig. 6
As shown in Tab. we further evaluate VLBiMan on the new dual-arm humanoid robot with real- world executions of the four tasks. Following the comparison protocol of Tab. [ report success rates on both previously seen objects and novel unseen objects, and additionally record performance under external perturbations. The results demonstrate that, under a different embodiment, VL- BiMan consistently achieves competitive performance comparable to that on the original dual-arm platform with opposite-side installation. This provides convincing evidence of VLBiMan's an'' pability for gross-embodiment transfer and generalization. Fig. [2 presents qualitative visualizations of real-world executions. On this new humanoid platform, the two arms are maximally synchronized, leading to smoother, more numan-like, and more efficient motions. More intuitive dynamic real robot rollouts can be found in our supplementary videos.
adopt a system configuration where
pre-grasping
colliding with bottle changing pasition - robatic arm moving back - re-grasping
clamped
pre-grasping
moving top marker partial occlusion recalculating grasping position
clamped
pre-grasping
gripper aligned with bottle cap unscrewing bottle cap realigned
re- re-grasping
Figure 13: Examples of some interesting findings. Top row: this case comes from the pre-grasping phase of pouring, where the left arm approaches and grasps the bottle. Middle row: this case comes from the pre-grasping phase of inserting where the right arm approaches and grasps the marker from the top direction. Bottom row: this case comes from the untwisting bottle cap phase of unscrew. where the center of the bottle cap is not aligned with the center point of the end of the gripper. But after a counterclockwise rotation, the upper part of the bottle tilts to the right, and the bottle cap is aligned with the gripper.
C.5 SOME INTERESTING FINDINGS OF VLBIMAN
During our dynamic interference experiments, we observed several interesting and insightful phe- nomena that further reflect the robustness and adaptability of the proposed VLBiMan framework.
(1) Dynamic adjustment during initial grasping: We found that the robot arms often exhibit the ability to dynamically refine their approach trajectories when objects are perturbed just before being
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grasped. This can be attributed to the fact that VLBiMan leverages VLMs with strong perception generalization, allowing real-time re-estimation object poses based on updated visual feedback. Please refer the case in the top row of Fig. 13
(2) Continuity despite partial perception failure: In scenarios where the manipulated object is partially occluded momentarily not detected (e.g., due to visual obstructions lighting shifts), the robot can still complete the task. This resilience stems from our modular trajectory composition scheme, which incorporates temporal anchoring of the demonstrationn-deiivee trajectory and does not rely on frame-by-frane perfect perception. Please refer the case in the middle row of Fig.
(3) Tolerance to object displacement during execution: We also noticed that slight spatial dis- placements of target objects during intermediate task stages often do not disrupt task execution. This behavior is supported by the task-aware decomposition and image-moment-based orientation extraction modules, which are both designed to operate on robust and low-frequency visual features (e.g., binary masks), making the entire system less sensitive to minor deviations and noise. These findings collectively highlight how VLBiMan benefits from the synergy between robust perception modules and structured motion control, leading to more fault-tolerant and adaptable bimanual ma- nipulation. Please refer the case in the bottom row of Fig. 13
Figure 14: Taking the inserting ing task as an example, we replaced the marker pen held by the left arm with other rectangular objects that were completely different (including spoon in cases al/a2, brush in cases a3/a4, spatula in case a5, syringe in case a6, and toothbrush in cases a7/a8). These newly added objects are circled in cyan color. We found that VLBiMan could still successfully locate the objects based on our designed method of using the centroid of the object's 2D mask as a representative point. Furthermore, accurately estimated the object's pose using the orientation estimation method in Algorithm [] thereby helping to stably grasp these objects and ultimately achieve the inserting task objective.
C.6 DISCUSSION OF HUMAN-IN-TIIE-LOO REFINEMENT
While VLBiMan is designed as a training- free and highly automated pipeline, the initial task-aware spatio-temporal decomposition may occasionally require minor human refinement during its first- time execution on new task. These refinements primarily concern safety and robustness adjust- ments that are difficult to infer from single demonstration alone. Typical examples include veri-
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Figure 15: Taking the inserting task as an example again, we changed the cup being grasped by the right arm to cups of completely different shapes (including mugs with handles in examples al/a2/a3/a4, and ordinary cups without handles in examples a5/a6/a7/a8). These newly added objects circled in yellow color. We found that VLBiMan using our designed method of using the foremost point of contact between the object and the table the orientation estimation
still successfully locate the objects
representative point (note that this time, is necessary method to estimate the object's 's pose again), thus helping to stably grasp these cups and ultimately complete the inserting task objective.
fying the tilt angle when grasping mug's handle or ensuring that the downward orientation of the right arm during unscrewing avoids exerting lateral pressure on deformable bottles.
Importantly, such refinements occur only once per task, decomposition time, and do not reappear during any subsequent executions. Once the primitive boundaries and key waypoints are validated, VLBiMan entirely relies on (1) VLM-based spatial adaptation and (2) trajectory composition to handle object pose variation, shape diversity, and long-horizon skill chaining. In practice, we find that only a small subset of tasks require any refinement all, and the operator does need to possess expert-level manipulation knowledge.
We acknowledge that fully automatic and reliable segmentation remains an open challenge few- shot imitation learning. Hardware-assisted demonstration capture (e.g.. instrumented gloves or hand-held trackers) could offer increased precision, though such solutions incur embodiment mis- match, reduced dexterity, and alignment overhead. Exploring more principled automatic segmenta- tion approaches while preserving usability remains an important future direction.
C.7 ROBUSTNESS OF OBJECT REPRESENTING POINTS
A core design choice in VLBiMan is the use of simple yet highly generalizable object representing points, which serve as anchors for both task-aware decomposition and cross-object adaptation. In practice, we adopt either the center of the object's 2D mask the foremost contact point between the object and the supporting surface. Despite their simplicity. these representations prove surpris- ingly robust across wide variety of object geometries. Because these points derived directly from VLM-assisted object segmentation, they require no object-specific modeling like the widely- used 6D object pose estimation Lin et al. 2004)): Wen al. (2024a), and naturally extend unseen
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pourtny
pouning
Irserting
Eack
Inserting
Visualization of test results for VLBiMan's robustness performance in cluttered sce- narios. We selected three tasks, reorient (corresponding to examples al/a2), pouring (corre- sponding to examples b1/b2), and inserting ing (corresponding to examples cl/c2/c3), for extensive evaluation. In these examples, there are not only various irrelevant objects that can easily lead to semantic ambiguity and execution obstacles, but we will also unexpectedly rearrange the target object during the pre-grasping stage. This requires the VLBiMan be able to quickly and nimbly find the target object again from the cluttered scene based on the task's linguistic instructions. This process faces many significant non-trivial challenges. The red arrow indicates direction of the manually moved or deliberately obscured object (interfering). The cyan arrow and yellow arrow indicate the movement direction of the left and right robotic arms (chasing) respectively.
Figure 16:
objects with distinet shapes, sizes, and surface profiles. This choice also provides robust abstrac- tion that generalizes across intra-class variations, imperfect geometry, and partial occlusions. And the system adapts by reattaching the invariant primitive to newly inferred representing points, main- taining functional consistency even in scenarios where popular 6D pose methods Lin et al.) (024); Wen et al. (2024)) tend fail due to symmetry or texture sparsity.
To further validate this robustness, conducted additional experiments in the inserting task, where geometric variations and small pose offsets are particularly challenging. And the used hard- and platform the dual-arm humanoid robot (refer Fig. 8 As shown in Fig. 14 and Fig.
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the results consistently show that these lightweight representations support reliable cross- cross-instance transfer without re- re-tuning, enabling accurate alignment even when objects differ sigmificantly from those used in the original demonstration (e.g.. varying mug/cup shapes or cuboid object's sizes). These findings highlight that VIBiMan'ss adaptation does not depend on high-fidelity 3D recon- struction or complex shape descriptors. Instead, object-centric points extracted from 21 perception are sufficient to drive effective and scalable bimanual manipulation. For all examples in Fig, [4] and Fig. [5, we have provided corresponding real-robot rollout videos in the supplementary ma- terials, and continue to support the application of perturbation to these entirely new categories of objects during the initial grasping phase, further demonstrating the strong generalization and wide versatility of VLBiMan.
C.8 TESTING VLBIMAN UNDER CLUTTERED SCENARIOS
To further examine VLBiMan's 's robustness complex perceptual conditions and more abstract nat- ural language descriptions, we conduct additional experiments in cluttered tabletop environments. These scenes contain least five distractor objects whose categories, shapes, colors resemble the target object, increasing semantic ambiguity and spatial interference. Using the same pipeline as in the main paper (without modifying any module). we evaluate reorient, pouring; and insert ing tasks under distractors, dynamic object relocation, and partial occlusion. We still uti- lized the dual-arm humanoid robot (refer Fig. as the hardware and platform. In these clutter tests, the VLM module must rely solely on the language instruction to identify the correct target and provide a stable grounding for subsequent geometric adaptation.
As shown in Fig. 16 across all cluttered configurations, VI.iiMan consistently identifies the ap- propriate object and completes the tasks with high reliability. Even when the object perturbed mid-execution or intentionally partially obscured, the system re-aligns the representing points and resumes the correct trajectory within a single perception-planning cycle (~ second). Correspond- ing visual results along with various indicating arrows are provided in Fig. 16 To our knowledge, many of the challenges in these examples lack systematic exploration in the current field of robotic manipulation. For instance, even when the target object is partially obscured during manipulation, VLBiMan can still locate the target and execute the grasping action accurately (see examples a al/c3). When there are multiple selectable target objects in the scene, VIBiMan can consistently eliminate ambiguous interference from very similar objects (in examples b1 and b2, where both require grasp- ing the blue mug, the system will not grasp the handleless yellow cup. And in examples el and c2, where the system needs to grasp the green and black marker pens respectively, it will not mistakenly grasp the other unwanted marker pen).
To sum up, these new experiments further validate that VLBiMan extends beyond template verb- conditioned tasks and remains robust under linguistic variation, distractor-rich scenes, and environ- mental disturbances. We highly recommend watching our recorded rollout videos provided in the supplementary materials to get a more intuitive feel for VI.BiMan's 's stunning performance.
C.9 ABLATION STUDIES OF THE INTERPOLATION DENSITY
During the pre-grasp phase in each task, VI.BMMan introduces a set of interpolated waypoints param- eterized by an interpolation density n. This design serves two purposes: (I) ensuring a smooth and safe approach trajectory that reduces the risk of prematurely colliding with the object, and (2) help- ing improve robustness against external disturbances. Without interpolation, the end-effector may directly execute long straight-line line motion from its initial configuration toward the demonstration- aligned grasp pose, which increases the chance of accidental contact, especially when the object has been shifted or rotated. Here we discuss how to find the optimal value of n.
As shown in Tab. 7 our ablation on the choice of reveals clear benefits: higher interpolation den- sity leads to improved stability under perturbations, including cases where the object is intentionally repositioned by a human or slightly displaced by the robot's own motion during execution. The grad- ual, multi-step approach allows the controller to continually re-evaluate object-relative anchors and correct small deviations on the fly. Notably, we find diminishing returns beyond a moderate range of n (e.g., relatively small values), indicating that the pre-grasp interpolation strategy does not rely on excessive tuning. Overall, these studies demonstrate that a carefully selected number of interpo- lated points contributes to both safety and disturbance resilience, enhancing VI.iiMan's reliability
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Table 7: Ablation experiments of the interpolation density n. We utilized the dual-arm humanoid robot platform to conduct four bimanual manipulation tasks. Similar to Tab. we still divided them into objects that appeared in the single demonstration and objects that did not appear in the demonstration. Each task under each setting was executed with 20 trails, and the average success rate was calculated. To ensure reliable searching of the optimal n1, we did not add any additional dynamic interference in each trail, and stopped the task immediately after the initial grasping stage finished failed of the (indicating the pre-grasping only performance).
new placements + same objects
new placements + ROvel instances
Interpolation Densiyy
Unserting
uOSCIeN
pouring
recriens
Average Success Rate
insert Ling
GASCzeN
pouring
ceoxiens ient
Average Success
Rate
15720
14/20
12/20
13/20
67.5%
13/20
12720
11720
11/20
58.8%
T
18/20
15/20
15/20
16/20
80.0%
17/20
14/20
14/20
14/20
73.8%
19/20
17/20
18/20
16/20
87.5%
18/20
16/20
17/20
15/20
82.5%
T1
19/20
19/20
18/20
17/20
91.3%
19/20
18/20
17/20
16/20
87.5%
92.5%
16/20
19/20
19/20
17/20
18/20
92.5%
19/20
18/20
16/20
17/20
87.5%
in real-world deployments. Practical deployments can adopt medium density (n=6) that balances pre pre-grasping accuracy and computational efficiency, used in our main experiments.
D STATEMENT ON THE USE OF LARGE LANGUAGE MODELS
During the preparation of this manuscript, we used the ChatGPT language model exclusively for linguistic refinement, including grammar correction and stylistic improvement. The model did not contribute to research design, methodology, experiments, analysis. All scientific content and intellectual contributions are solely the work of the authors.
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Reviews:
[{"review_id": "taUL8lJpYD", "reviewer": "Reviewer_nQo1", "summary": "This paper presents VLBiMan, a framework for bimanual robotic manipulation that learns from a single human demonstration and adapts to new objects and scenes using VLMs for semantic and geometric anchoring. The system decomposes demonstrations into reusable motion primitives, enabling generalization across tasks, object categories, and robot embodiments. The method is demonstrated on ten real-world tasks.", "questions": "- How much manual intervention is needed during the human-in-the-loop segmentation refinement, and could this step be automated?\n- How does the system handle ambiguous or incorrect segmentation results from the vision-language models, especially in cluttered scenes?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The approach is demonstrated on ten real-world tasks.\n- The method shows improvements over recent baselines, like ReKep.\n- The supplementary shows the cross-embodiment transferability of the proposed approach.", "weaknesses": "- The algorithm requires human-in-the-loop refinement for the spatio-temporal segmentation. This aspect is mentioned only briefly (Line 237) but should be stated more explicitly, as it directly affects the level of automation of the pipeline. It is also unclear whether this refinement requires expert intervention.\n- The reliance on mask centroids and contact points is a simplistic heuristic that may fail under occlusion or complex object geometries. This limitation requires a more detailed discussion of failure cases.\n- The dynamic collision compensation introduces several design choices that appear brittle and may not generalize well.", "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": "K2gtVd3zt3", "reviewer": "Reviewer_uWbN", "summary": "This paper introduces VLBiMan, a framework for generalizable bimanual manipulation that learns reusable skills from a single human demonstration by decomposing tasks into invariant primitives and dynamically adapting variable components via vision-language grounding. This approach eliminates the need for extensive demonstration datasets and policy retraining, enabling robust adaptation to scene ambiguities, novel objects, and disturbances. The system achieves compositional generalization by splicing atomic skills for long-horizon tasks and demonstrates strong cross-embodiment transfer, allowing skills learned from human demo to be directly deployed on different robotic platforms, thereby advancing versatile dual-arm manipulation in unstructured environments.", "questions": "1. How much human efforts are required in the \"human-in-the-loop refinement\" in the Spatiotemporal Segmentation? \n2. I'm wondering how your pose estimation method (proposed in 3.2) compares to off-the-shelf 6D pose estimation methods, especially for some symmetrical objects.\n3. How do you deal with potential collision during each motion besides grasp approach?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper achieves remarkable data efficiency and generalization by learning reusable skills from a single demonstration through task-aware decomposition and vision-language adaptation.\n2. The experiments conduct detailed analysis on the modular system design and system robustness, including ablations on different modules and breakdowns of failure cases. \n3. The paper is well-written, presenting a complex technical system with conceptual clarity and a logical narrative that is easy to follow.", "weaknesses": "1. The scenes involved in the paper are clean. The paper does not show the cases where some distracted objects are placed in cluttered scenarios, which may pose some challenges to the capabilities of the visual-language anchored adaptation modules.", "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": "43ov6jXChs", "reviewer": "Reviewer_ueUa", "summary": "VLBiMan can be directly deployed in a new environment without the need for further training, simply by using RGB images from a single human dual-arm demonstration, 6Dof poses of the robotic arm, gripper switch states, and natural language task descriptions. The method performed approximately 4,200 real robot rollouts on 10 self-built dual-arm tasks, with an average success rate of 78% (ID) / 59% (OOD), significantly outperforming 5 strong baselines, and demonstrated robustness across embodiment migration, lighting, and continuous disturbances. However, the paper has some missing details in the implementation of the method. At the same time, it does not conduct further discussion and analysis on failed cases, nor does it carry out statistical analysis such as confidence level on the main experimental results. There is room for further improvement.", "questions": "1. With regard to the sensitivity of key hyperparameters, could you please provide sensitivity analysis curves or ablation study tables for critical parameters such as ε_g, ε, and n? Additionally, could you clarify whether these hyperparameters are consistently set across different tasks?\n2. Concerning the potential limitations in estimating orientations for symmetric objects, particularly rotationally symmetric ones such as bottle caps and cup rims, could orientation ambiguity lead to task failure? If so, what proportion of failures can be attributed to this issue, and are there any proposed solutions to mitigate such failures?\n3. Regarding the lack of clarity in explaining the strategy for selecting representative points, could you offer further elaboration on the criteria used to determine when to employ the center of gravity versus planar contact points?\n4. With respect to the insufficient analysis of failure cases, in scenarios involving spatio-temporal conflicts during bimanual operations, how is collision detected in real time? Were any statistical analyses performed on the observed failure cases? If available, could you provide quantitative data on the frequency and types of failures encountered in the experiments?\n5. Regarding the design of ablation studies, is there a plan to conduct ablation experiments concerning language inputs in future work? Specifically, if attribute descriptors (e.g., color, shape) in the task instructions are removed or altered, how would this affect the VLM segmentation recall rate and the overall task success rate?\n6. Concerning the absence of statistical significance testing, could you please include 95% confidence intervals or bootstrap confidence intervals in the main results table? Furthermore, could a paired two-sample statistical test be applied to assess whether the performance differences between VLBiMan and other baseline methods are statistically significant (p < 0.05)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. There is no need to re-collect data or fine-tune the model for new objects or new robots, and sufficient experimental verification has been conducted at the same time.\n2. Through the modular design concept, the \"unchanging skills\" and the \"variables to be adapted\" are explicitly separated. Combined with VLM for semantic segmentation and geometric offset calculation, a lightweight and interpretable process has been achieved.\n3. A progressive inverse kinematics solution and proximal - vertical collision compensation method were proposed, effectively alleviating conflicts in time and space, supporting synchronous/asynchronous hybrid execution, and achieving coordination of dual-arm operations.\n4. The same demonstration can be directly deployed to the opposed dual-arm platform and the humanoid dual-arm platform. The success rate of the experimental test still remains at around 70%, proving the universality of the method on different robot platforms.\n5. Under conditions such as illumination changes and continuous external dynamic disturbances, it still maintained a high success rate. The experimental evaluation was comprehensive, demonstrating the robustness of VLBiMan.", "weaknesses": "1. The experimental analysis lacks a thorough examination of key hyperparameters, including the geometric equivalence threshold ε_g, the stability window ε, and the interpolation density n=6. These parameters directly influence the quality of decomposition and trajectory generation; however, the manuscript does not provide justification for their selected values or the rationale behind the search ranges explored.\n2. The criteria for selecting representative points remain ambiguous. Specifically, the paper fails to clarify under what conditions the mask centroid is chosen versus planar contact points, and it does not detail the procedure for identifying planar contact points. This lack of transparency may hinder the method’s reproducibility and limit its practical utility in engineering applications.\n3. The proposed method may face challenges in accurately estimating the orientation of symmetric objects. For example, Algorithm 1 relies primarily on the principal axis derived from the second moment, which introduces a 0°–180° rotational ambiguity for rotationally symmetric or near-circular objects. The paper does not address whether this ambiguity could lead to functional failure, nor does it incorporate strategies—such as leveraging vision-language model (VLM) semantics—to resolve such ambiguities.\n4.Language is used only to generate object prompts, without incorporating a language-action alignment loss or conducting ablation studies on the influence of different linguistic formulations on segmentation performance. As a result, the integration of the language modality appears superficial, and the potential benefits of multimodal reasoning within the framework are not fully realized or demonstrated.\n5. All reported results are presented solely as average success rates, without accompanying measures of statistical variability such as standard deviations, confidence intervals, or significance testing. Given the limited number of trials (e.g., 20), differences such as a 78% versus 70% success rate may not be statistically significant. Furthermore, the absence of detailed failure case analyses prevents a comprehensive understanding of critical issues such as spatio-temporal conflicts.", "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": "lPEGHSnQdv", "reviewer": "Reviewer_DSMU", "summary": "This paper introduces VLBiMan, a framework for generalizable bimanual robotic manipulation using only a single human demonstration. The method decomposes the demonstration into reusable motion primitives and adapts them to novel objects or environments through vision-language models (VLMs) and geometric reasoning. Unlike imitation learning approaches that rely on large datasets, VLBiMan leverages semantic grounding and geometric anchors to perform new tasks without retraining. The authors demonstrate strong results on 10 real-world bimanual manipulation tasks including tool use and long-horizon skill compositions, achieving significantly higher success rates than baselines.", "questions": "- How sensitive is VLBiMan to segmentation quality in the one-shot demonstration?\n- Can the approach scale to more abstract task descriptions beyond manipulation verbs?\n- Could the authors provide computational efficiency details?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Modular design: The decomposition–adaptation–composition pipeline is intuitive and well-grounded, allowing for task-aware reuse of motion primitives.\n- One-shot generalization: Demonstrating adaptation to new objects, poses, and even robotic embodiments is impressive.\n- Comprehensive empirical evaluation: Ten tasks, multiple baselines, both static and dynamic settings, and thorough ablations (Table 3) provide convincing evidence.\n- Clarity and presentation: Figures clearly illustrate both conceptual flow and empirical outcomes. The writing is clear and professional.", "weaknesses": "- Limited novelty in underlying modules: While the integration is well-engineered, the components are largely standard.\n- Dependence on heuristics and manual refinement: The segmentation step requires human-in-the-loop refinement, which undermines full automation and scalability.\n- Lack of comparison with recent closed-loop VLA policies: The baselines focus on modular imitation systems (ReKep, Robot-ABC), but not large VLA models.", "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)