MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation
Authors: Chengshu Li
Mengdi Xu
Arpit Bahety
Hang Yin
Yunfan Jiang
Huang Huang
Josiah Wong
Sujay Garlanka
Cem Gokmen
Ruohan Zhang
Weiyu Liu
Jiajun Wu
Roberto Martín-Martín
Li Fei-Fei
Venue: ICLR 2026 Poster (2026)
Keywords: Data Generation for Robot Learning
Bimanual Mobile Manipulation
Imitation Learning for Robotics
Primary area: applications to robotics, autonomy, planning
TL;DR: We propose MoMaGen, an automated data generation method for bimanual mobile manipulation that satisfies hard constraints (e.g., reachability) while balancing soft constraints (e.g., visibility).

Abstract: Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming. This challenge intensifies for multi-step bimanual mobile manipulation, where humans must teleoperate both the mobile base and two high-DoF arms. Prior X-Gen works have developed automated data generation frameworks for static (bimanual) manipulation tasks, augmenting a few human demos in simulation with novel scene configurations to synthesize large-scale datasets. However, prior works fall short for bimanual mobile manipulation tasks for two major reasons: 1) a mobile base introduces the problem of how to place the robot base to enable downstream manipulation (reachability) and 2) an active camera introduces the problem of how to position the camera to generate data for a visuomotor policy (visibility). To address these challenges, MoMaGen formulates data generation as a constrained optimization problem that satisfies hard constraints (e.g., reachability) while balancing soft constraints (e.g., visibility while navigation). This formulation generalizes across most existing automated data generation approaches and offers a principled foundation for developing future methods. We evaluate on four multi-step bimanual mobile manipulation tasks and find that MoMaGen enables the generation of much more diverse datasets than previous methods. As a result of the dataset diversity, we also show that the data generated by MoMaGen can be used to train successful imitation learning policies using a single source demo. Furthermore, the trained policy can be fine-tuned with a very small amount of real-world data (40 demos) to be succesfully deployed on real robotic hardware. More details are on our project page: momagen.github.io.

Published as a conference paper at ICLR 2026
MOMAGEN:
GENERATING DEMONSTRATIONS UNDER SOFT AND HARD CONSTRAINTS FOR MULTI-STEP BIMANUAL MOBILE MANIPULATION
Chengshu Li Mengdi Xu"", Arpit Bahety"", Hang Yin",, Yunfan Jiang', Huang Huang', Josiah Wong', Sujay Garlanka', , Cem Gokmen', Ruohan Zhang', Weiyu Liu', Jiajun Wu', Roberto Martin-Martín Li Fei-Fei!
'Stanford University, The University of Texas at Austin, 'Amazon. 'Equal Contribution
ABSTRACT
Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time- consuming. This challenge intensifies for multi-step bimanual mobile manipulation, where humans must teleoperate both the mobile base and two high-DoF arms. Prior X-Gen works have developed automated data generation frameworks for static (bimanual) manipulation tasks, augmenting few human demos in simulation with novel scene configurations to synthesize large-scale datasets. However, prior works fall short for bimanual mobile manipulation tasks for two major reasons: 1) a mobile base introduces the problem of how to place the robot base to enable downstream manipulation (reachability) and 2) an active camera introduces the problem of how to position the camera to generate data for visuomotor policy (visibility). To address these challenges, MOMAGEN formulates data generation as a constrained optimization problem that satisfies hard constraints (e.g., reachability) while balancing soft constraints (e.g., visibility while navigation). This formulation generalizes across most existing automated data generation approaches and offers a principled foundation for developing future methods. We evaluate on four multi- step bimanual mobile manipulation tasks and find that MOMAGEN enables the generation of much more diverse datasets than previous methods. As a result the dataset diversity, we also show that the data generated by MOMAGEN be used to train successful imitation learning policies using a single source demo. Furthermore, the trained policy can be fine-tuned with very small amount of real-world data (40 demos) to be succesfully deployed on real robotic hardware. More details are on our project page: momagen. github.io
INTRODUCTION
Learning from human demonstrations a powerful paradigm for teaching robots complex manipula- tion skills. A common approach for collecting such data is teleoperation, where a human directly controls the robot to demonstrate desired behaviors. When scaled up, teleoperated data has enabled training visuomotor policies with impressive generalization and success in challenging manipulation tasks (Brohan et al., 2022b; Neill et al., 2024; Khazatsky et al., 2024; Black et al., 2024; Intelligence al., 2025). However, this data collection process remains expensive and time-consuming, especially for tasks that require high-quality demonstrations to ensure effective policy learning.
Recently, collecting a small amount of human teleoperation data and then synthesizing additional data in simulation has become a popular approach to scale up data collection (Mandlekar et al., 2023b; Garrett al., 2024; Jiang et al., 2025b; Xue et al., 2025; Yang et al., 2025). Compared to offline data augmentation techniques such as those based on image augmentation (Laskin et al., 2020; Yarats et al., 2021; Pitis et al., 2020; Yu et al., 2023; Chen et al., 2023), this approach can autonomously generate new behaviorally diverse data for the same task. This process enlarges the support and convergence region of the policies and reduces the teacher-student distribution mismatch (Ross et al., 2011) through new generated experiences validated in simulation to ensure quality. Notably, the X-Gen family of techniques (Mandlekar et al., 2023b; Garrett et al., 2024; Jiang et al., 2025b; Xue et al., 2025; Yang et al., 2025) leverages simulation and augmentation based on small number of human demonstrations that are used as seeds to generate multiple new variations automatically. While they have shown success in simple table-top manipulation tasks, significant standing challenge for

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Published as a conference paper at ICLR 2026
Clean Frying Pan
Single Source Demo
MoManGn
Data Generation Constrained Optimization
Human Collected Source Demo
Diverse Object Configurations
Generated Trajectories
Diverse Navigation Behaviors
Diverse Manipulation Behaviors
Figure 1: (left) MOMAGEN uses single human-collected demonstration generate large set of demonstrations, formulating data generation as a constrained optimization problem. (top-left) shows human-collected demo for cleaning a pan with scrub. (top-right) shows three novel object configurations with aggressive object pose randomization and additional distractors/obstacles. MOM AGEN can generate novel trajectories in these diverse scenarios. (bottom-left) shows three robot base poses and (bottom-right) shows two arm trajectories for picking up the pan.
X-Gen methods is to extend the benefits generalizable data generation to real-world tasks with more complex robot embodiments such mobile manipulators.
Solving real-world tasks, such as everyday household activities, often requires a mobile manipulator with whole-body control capabilities to coordinate stable and accurate navigation with end-effector manipulability, often with two arms (Jiang et al., 2025a; Li et al., 2020; Fu et al., 2024b). Teleoperation data collection becomes significantly challenging for gegb-degrees-oo-frredoo whole-body control since controlling the base and two arms is a severe overload on the human operators (Dass et al., 2024; Ishiguro et al., 2020; Fu al., 2024b) (see Fig. 1, left). Augmenting a few (expensive) demonstrations becomes thus critical, but previous methods of the X-Gen family fall short in this domain due to two major reasons: First, mobile manipulation introduces the problem of object reachability. For novel object arrangements, naive replay of the navigation segments of the human collected demonstrations easily leads to robot configurations that render subsequent manipulation infeasible. Second, having a mobile base, and thus movable camera, exacerbates the problems of partial observability. Concretely, when training visuomotor policies for mobile manipulation, a naive augmentation of demonstrations leads to severe problems in object visibility: the task-relevant objects may view, making it hard for the policy to make optimal decisions based on the images from onboard sensors. Naive motion planning (Garrett et al., 2024) replay (Mandlekar et al., 2023a; Jiang et al., 2025b) is insufficient to ensure either reachability or visibility of the task-relevant objects.
out of the field of
To address these challenges, we propose MOMAGEN, a general data generation method for bimanual mobile manipulation. It formulates data generation as constrained optimization problem with hard constraints (e.g., reachability, visibility during manipulation) and soft constraints (e.g., visibility while navigation). Significantly, we realized that previous methods of the X-Gen family can be interpreted with the same unified framework, except using different (and insufficient) hard and soft constraints for data generation. We evaluate MOMAGEN four multi-step bimanual mobile manipulation tasks and find that it generates substantially more diverse datasets than prior methods. Leveraging this diversity, show that the synthetic data can train effective imitation learning policies from just single demonstration. Moreover, these policies can be fine-tuned with a small amount of real-world data (40 demonstrations) achieve successful deployment physical robotic hardware.
RELATED WORKS
Data Acquisition for Robot Learning. Collecting large-scale human teleoperation data for robot learning incurs considerable costs. Scaling up data collection requires large number of human operators over extended periods time (Brohan et al., 2022b; O' O'Neill et al., 2024; Khazatsky et al., 2024). Offline data augmentation techniques can boost data quantity and diversity by perturbing
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Methods
Bimanual
Mobile
Obstacles
Base Random.
Active Perception
Hard Construints
Constraints
Soft
MinicCien (Mandiekar al., 2023b)
Suce Suvc. Kin. C-Free
N/A
SkillMimi-Gen (Gurrett et al., 2024)
V
X
X
N/A
DetMinicGenn (Jiang et al., 2025b)
X
K
K
Succe Temp
N/A
DemoGin (Kue al.,, 2025)
N/A
Physics (Yang et al., 2025)
X
X
X Kin, C- Free, Dyn
Tme
MoMaGio (Ours)
Succ, Kin, C-Frce, Temp, Vis
Vis, Ret
Table 1: Comparison of different automated data generation methods and the constraints they enforce. "Succ": task success; "Kin": kinematic feasibility; "C-Free": collision-free execution; "Temp": temporal constraints for bimanual coordination; "Dyn": system dynamics; "Trac": target trajectory
tracking; "Vis": visibility of task-relevant objects in the robot's camera view; "Ret": retraction of robot torso and arm to a compact configuration before navigation.
existing trajectories (Mandlekar et al., 2023b; Garrett et al., 2024), or leveraging image augmentation techniques and generative models (Laskin et al., 2020; Yarats et al., 2021; Pitis et al., 2020; Yu et al., 2023; Chen et al., 2023). However, the augmented data may not always be executable by real robots. A promising alternative is to leverage automated data generation and validation in simulation. Fully automated approaches include trial-and-error (Levine et al., 2018 8; Pinto & Gupta, 2016; Yu al., 2016; Dasari et al., 2019) and pre-programmed (e.g., scripted) experts (James al., 2020; Gu et al., 2023; Jiang et al., 2022; Wang et al., 2023), which are yet to be proven effective for multi-step complex tasks with contact-rich interaction. X-Gen (Mandlekar et al., 2023b; Garrett al., 2024; Jiang et al., 2025b; Xue et al., 2025; Yang et al., 2025) represents a hybrid approach that uses handful of human demonstrations as seeds to generate many new variations, augmenting the data by factor of 25x to 350x (Mandlekar et al., 2023b; Jiang et al., 2025b), while ensuring synthesized comparison between prior X-Gen works and our work can be found in
data valid in simulation. Table 1. Our work signifies an important step toward more generalizable data generation framework for challenging mobile manipulation tasks, which have never been tackled before.
Imitation Learning for Mobile Manipulation. Early successes in robot imitation learning mostly focused on fixed-bases arms, but many real-world tasks require a mobile manipulator that can both navigate and manipulate. Such robots need to effectively navigate in environment position themselves for downstream manipulation. Collecting teleoperation data for mobile manipulation is significantly more costly: operators must simultaneously control the robot base and arms (Wong et al., 2022; Fu et al., 2024b; Shaw et al., 2024; Dass et al., 2024; Jiang et al., 2025a), calling for automated data generation methods for better scaling. On the algorithmic side, imitation learning methods started handle the complexities of mobile manipulation tasks, employing behavior cloning (Brohan et al., 2022a; Fu et al., 2024b;a; Cheng et al., 2024; Wu al., 2024a; Yang et al., 2024; Li et al., 2024b; Ze et al., 2024; He et al., 2024) and large pretrained models (Ichter et al., 2022; Wu et al., 2023; Xu et al., 2023; Stone et al., 2023; Jiang et al., 2024b; Shah et al., 2024; Wu et al., 2024b).
3 PROBLEM FORMULATION: AUTOMATED DATA GENERATION AS CONSTRAINED OPTIMIZATION
We formulate automated demonstration data generation as a constrained optimization problem, and provide unified framework that incorporates existing approaches (see Table 1). This optimization problem includes both hard and soft constraints. The former must be strictly satisfied (e.g., task success, convergence target end-effector poses key frames, and collision avoidance). The latter capture desirable properties (e.g., shorter trajectory and reduced jerkiness). Generating valid data requires strictly satisfying hard constraints while minimizing the costs associated with soft constraints.
Each task is modeled as a Markov Decision Process (MDP) with state space S and action space
A. Given a set of source demonstrations Dare = {d = (s/- ag........... sy... Noye where Nore is the number of source demonstrations, T is the trajectory length, and Sp D is the initial state from distribution D. We aim to generate new set of successful demonstrations D = {d]NNa given the source demonstrations Da and set of constraints (9.). With the generated demonstrations, we can train Behavioral Cloning (Pomerleau, 1988) policies vy using arg ming (s. (- log (a]ss))
Following prior work (Mandlekar al., 2023b; Garrett et al., 2024; Jiang et al., 2025b), each source demonstration d can be decomposed to N subtasks. Each subtask contains an object trajectory S,(o,), E [N], where o, is the object of interest (i.e. target object), and an end-effector trajectory T, = & w j E [N], k E [K,]. where T is the pose of the end-effector frame E with respect to the world reference frame W at time k and K2 is the number of steps for the subtask Each
W
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subtask further be labeled either free-space subtask contact-rich subtask. In a free- space subtask, the goal is to move the robot base or arms in free space (e.g. to a pregrasp pose), where feasible trajectories can be sampled using motion planning subject to kinematic and collision constraints. a contact-rich subtask, the goal is to manipulate the relevant objects through contacts (e.g- grasping. wiping). The relative poses between the end-effector frame and the target object frame for contact-rich subtasks are preserved from the source to the generated demonstrations.
Generating a demonstration can be viewed as solving the following constrained optimization problem:
st+1 = f(s at), 9kin ar) 1 0,
vt E [T]
4A E [T]
(cal ot) 2 0, (vis ac. Oi(t)) S 0,
Et [T]
arg min (-) S.t. axe[r]
Vt E [T]
Tw = W (T
By
V contact,, Ak E [K]]
9t D success
3t E [T] (task success)
Here, L(-) contains user-specified soft constraints. The function f(st, ae) denotes the system dy- namics. The constraints 9kin encode kinematic feasibility (e.g. joint limits), Gcoll encode collision avoidance, and Gvis encode visibility constraints (e.g. during manipulation).
4 MOMAGEN
Following the proposed problem formulation in Section 3, we develop MOMAGEN that solves a constrained optimization problem to generate demonstrations for bimanual mobile manipulation tasks. We first introduce the reachability and visibility constraints that are essential for bimanual mobile manipulation in Section 4.1. We then detail the data generation method in Section 4.2.
4.1 CONSTRAINTS FOR BIMANUAL MOBILE MANIPULATION
In our instantiation of MOMAGEN, beyond the commonly used constraints discussed earlier, we introduce several key technical innovations that crucial for generating high-quality bimanual mobile manipulation demonstrations, regarding reachability, visbility and retraction.
Reachability Hard Constraint. A key distinction of mobile manipulation is the need to control the robot base for effective downstream manipulation. Prior works (Mandlekar et al., 2023a; Jiang et al., 2025b) reuse base trajectories from source demos without adaptation, which fails when randomized target objects lie outside the arm's workspace. To address this, we impose reachability as a hard constraint, ensuring sampled base poses keep all required end-effector trajectories within reach.
Object Visibility during Manipulation as Hard Constraint. A valid base pose must also satisfy a hard visibility constraint: task-relevant objects must remain in view. Since the generated data meant to train visuomotor policies, we ensure each sampled pose allows the head camera to observe these objects without occlusion, using the additional camera or torso articulation when needed.
Object Visibility during Navigation Soft Constraint. Maintaining task-relen musk-relevant object visibility during navigation desirable but not required, so we treat it as soft constraint by adding a visibility cost that biases the head camera toward the target object during navigation.
Retraction as Soft Constraint. After manipulation, the robot retracts its torso and arms into a compact configuration, reducing its footprint and making subsequent navigation safer.
As illustrated above, the choice of constraints, particularly soft constraints, is highly dependent on the specific application or domain. In our case, we selected the aforementioned constraints because we believe they promote the generation of high-quality bimanual manipulation demonstrations that closely approximate human-level optimality for visuomotor policy training.
4.2 AUTOMATED DEMONSTRATION ATION GENERATION FOR BIMANUAL MOBILE MANIPULATION
In this section, we discuss in detail our framework that leverages the novel constraints introduced above to efficiently generate diverse demonstration data for bimanual mobile manipulation.
Source Demonstration Annotation. Each demonstration is segmented into temporally ordered subtasks, that are either single-arm motions or bimanual motions requiring synchronization at subtask boundaries. For each subtask, we annotate the target object Otarget, gripper-held object Ohetd. timestep immediately preceding contact Épregrasp- end timestep tend, and retraction type execute after manipulation. Figure 2 illustrates an annotated subtask of grasping cup with one arm. To stress-test method and minimize human effort, collect and annotate only single source demo (Na.=11).
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Reachability Verification
Visibility Verification
Source Demonstration
hand: Inft
1. EEF Transformation
2. Valid Base Pose Sampling
Nome
Obanget cup
&
Apregraap 450
Aand
y: default
SN
Subtask Annotation
3. Base/Torso Planning
Arm Planming Replay
Arrso Retraction
Figure 2: MOMAGEN method. Given single source demonstration, as well as annotations for object-centric subtasks for each end-effector, MOMAGEN first randomizes transforms the end-effector poses from the source demo to the new objects' frame of reference. For each subtask, it tries to sample valid base pose that satisfies reachability and visibility constraints. Once found, it plans base and torso trajectory to reach the desired base and head camera pose while trying look the target object during navigation. Once arrived, plans arm trajectory the pregrasp pose and uses task space control for replay, before retracting back to a tucked, neutral pose.
configuration, and
Algorithm MOMAGEN
Input:
original demo, new initial state 80 Output: generated demo
1: for each segment do
2: Get current huse Team torso qarm
3: if' held object not in hand then abort
4: Compute transformed end-effector pose Teef using new target object pose
5:
Check visibility of target object with Team
6:
Solve IK for arm trajectory {g..m } with current Thase while not visible
Team
7:
IK exists do
8:
Sample new base pose Thase Sample new camera pose Team
9:
10:
Solve IK for arm {gārm) and torso (ooo with sampled Thase Team
Plan motion for {q: [oros) from current Thase sampled Thase, Team w/ soft visibility
12:
Plan motion for {q.rm} from previous Teef to pregrasp Teef
Control end-effector in task space follow transformed Teef
14:
Attempt retraction
Demonstration Generation. MOMAGEN generates demonstrations for novel initial states by following Algorithm 1. For each subtask, we first verify that the robot holds the required object, aborting early if not (line 3), typically due failed grasp. We then compute end-effector poses for contact-rich motions by applying the transforms from the original demo (line 4). Next, we check reachability and visibility constraints for the current base and head camera configuration (lines 5-6); if satisfied, the robot proceeds to manipulation (lines 12-13). Otherwise, we sample base and head camera configurations until valid one is found (lines 7-11), using heuristics and inverse kinematics to ensure reachability and visibility (lines 8-10). Once valid one is reached via motion planning with soft visibility constraint (line 1), the robot executes the manipulation, moving the pregrasp pose via motion planning and replaying the contact-rich segment in task space (lines 12-13). Finally, the robot tries to retract to canonical or prior joint configuration (line 14). This process repeats for each subtask, with motion planning and IK handled by cuRobo (Sundaralingam et al., 2023).
Key Novelties. Our approach introduces four main innovations for generating mobile manipulation demonstrations. (1) Full-body Motion: Unlike prior work focused only end-effector pose T we jointly consider Teef head camera pose Team and base pose These,
(2) Visibility Guarantee: We enforce object visibility before manipulation (lines 9) and add soft constraint to maintain

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Pick Cup
Tidy Tible
Put Dishes Away
Clean Frying Pan
Figure 3: Task visualization. Our multi-step tasks include long-range navigation, sequential and coordinated bimanual manipulation, requiring pick-and-place and contact-rich motion.
visibility during navigation (line 11). (3) Expanded Workspace: We sample base poses near target objects (line 8) and plan base motions across the room to fully exploit mobility (line 11). (4) Efficient Generation: We accelerate generation by prioritizing fast IK checks over full motion planning whenever possible for preemptive filtering, and by decomposing robot's configuration into torso/arm subspaces for efficient conditional sampling, akin to task-motion planning (Garrett et al., 2021).
EXPERIMENTS AND RESULTS
We evaluate MOMAGEN on four household tasks (Section 5.1) under three object/scene randomiza- tion schemes (Section 5.2). Compared with two baselines (Jiang et al., 2025b; Garrett et al., 2024), MOMAGEN produces much more diverse data, higher generation success rates, and substantially greater object visibility, which is crucial for visuomotor policies (Section 5.3). We further assess how these demonstrations help train imitation learning policies, with sim-to-real potentiality (Section 5.4).
5.1 TASK SETUP
We evaluate MOMAGEN on four household tasks that require mobile manipulation and involve various manipulation skills, including pick-and-place, contact-rich interactions and bimanual coordination, illustrated in Figure 3. Inspired by BEHAVIOR- IK, all tasks are implemented OmniGibson (Li et al., 2023; 2024a). Pick Cup: navigate to table and lift a cup. Tidy Table: move cup from the countertop to the sink, requiring long-range mobile manipulation. Put Dishes Away: stack two plates from the countertop onto shelf using two arms independently, requiring bimanual uncoordinated motion. Clean Frying Pan: use both arms to scrub a dusty pan with a brush, requiring contact-rich bimanual coordinated motion. For each task, we collect single 1-3 minute human teleoperated demonstration, with base motion accounting for 45% of the duration.
5.2 DOMAIN RANDOMIZATION SCHEMES
For each task, we define three domain randomization levels of increasing difficulty. DO: task-relevant objects randomized within 155 cm and 15 on the same furniture. D1: task-relevant objects placed anywhere on the furniture with unrestricted orientation (Figure 4a). D2: D1 plus additional objects on furniture (manipulation obstacles) and on the floor (navigation obstacles) (Figure 1, upper right). This scheme significantly more aggressive than prior work (Mandlekar et al., 2023b; Garrett et al., 2024; Jiang et al., 2025b), enabled by MOMAGEN is unique ability to generate novel base motions. More details and visualizations can be found in Sec. B.3 and Fig. 12 in the Appendix.
5.3 DATA GENERATION COMPARISON
We evaluate MOMAGEN for bimanual mobile manipulation under varying randomization schemes, comparing it to two baselines: SkillMimieGen (Garrett et al., 2024), which generates single-arm tra- jectories via motion planning and task-space control, and DexMinieGen (Jiang al., 2025b), which targets dexterous bimanual data. Since all the evaluated tasks require substantial base movement, both baselines are extended with base trajectory replay from source demos, similar to MimicGen (Mandlekar et al., 2023a). We assess methods using three metries: (1) data diversity (object pose and action variation), (2) generation success rate, and (3) object visibility ratio during navigation.
How diverse are the demonstrations generated by MOMAGEN? Figure 4a shows the variation in task-relevant object poses for the Tidy Table task under MOMAGEN (DO/D1) and SkillMimicGen (DO). The baselines succeed only in DO, as they cannot generate novel base motions. In contrast, MOMAGEN (D1) covers a much wider range of object poses, spanning the entire counter rather than small corner. Figures 4b-c further demonstrate that MOMAGEN DI yields substantially broader coverage of base and end-effector poses compared to DO and the baselines. Figure 4d shows PCA projections of arm and torso joints under DO. Despite identical object pose distributions, randomizing feasible base placements allows MOMAGEN to achieve far greater action diversity. Moreover, with its motion planner, MOMA can generate demonstrations in cluttered scenes with distractors

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Mellfaaiin 100 Ga0
Saafate D6
Vallatia au 200
SulMirkki 30
NoMaSca 30 SutHaaeS DO
(3) Object Diversity
Base Divensity
(c) End-Eiiector Diversiyy
(4) Robot Posttio Diveriity
Figure 4: Generated data diversity amalysis for Tidy Table task (50 trajectories, subsampled). Given the same object randomization (DO) (a), compared to SkillMimicGenn MOMAGEN samples diverse base poses (b), and as a result, diverse end-effector poses (c) and joint positions (d). MOMAGEN is also the only method that can generate data for DI randomization (red) for even greater diversity.
Methods
Pick Cup
Tidy Table
Put Dishes Away
Clean Frying Pan
MOMAGEN
0.86
0.80
0.38
0.51
SkillMimieGen
1.00
0.69
0.38
0.40
DexMimicGen
1.00
0.72
0.38
0.35
DO
MOMAGEN w/o soft vis. const.
0.88
0,78
0.50
0,46
MOMAGEN w/o hard vis. const.
0.97
0.59
0.29
0.24
MOMAGEN w/o vis. const.
0.97
0.74
0.29
0.36
MOMAGEN MOMAGEN w/o vis. const.
0.60
0.64
0.34
0.20
DI
0.66
0.48
0.23
0.13
MOMAGEN
0.47
0.22
0.07
0.16
D2
MOMAGEN w/o vis. const.
0.50
0.16
0.05
0.12
Table 2: Data generation success rates comparison. For simpler tasks (Pick Cup), ablations and baselines achieve higher data gen success rates because of fewer constraints and less motion planning stochasticity. However, for more complex tasks (the other three), enforcing hard visibility constraints helps position the robot torso to suitable configuration that facilitates downstream manipulation, leading to higher success rates. The baselines suffer from zero success rates and hence are omitted for D1/D2 because the objects are beyond the reachability of replayed base poses from the source demo.
obstacles (Figure 12), further enhancing the diversity of both robot actions and visual observations. Additional results for other tasks shown in Figures 13, 14, and 15 in the Appendix.
Can MOMAGEN achieve high-throughpe data generation? Table 2 shows that MOMA- GEN achieves a 63% average data generation success rate for DO and can generate data for all tasks across all randomization levels, though throughput decreases with higher difficulty. In contrast, the baselines perform well on simple tasks like Pick Cup but fail on harder ones such as Clean Frying Pan, where adapting base motions is critical, and cannot handle DI or D2 randomization at all.
Can MOMAGEN generate demonstrations with high object visibility? Because task- relevant object visibility critical for visuo- motor policy learning (Sec. 5.4), evaluate whether MOMAGEN indeed produces data with high object visibility. We compare it against baselines and three ablations that remove hard and/or soft visibility constraints. As shown in Table 3, MOMAGEN achieves substantially higher relk-relevant object visibility (often dou- ble) relative to both baselines and ablations, es- pecially for multi-step tasks. In the Tidy Table task (Figure 5), results further show that both hard and soft constraints are essential for main- taining high visibility during navigation.
Lddy Table (DO)
MoMaGien Full
Trajectory Count
200
MoMaGen vis. Const.
MoMaGen w/o Ulrrd Vis. Coust.
150
MoMaGen w/o Vis, Const.
50-
Object Visiale during Nav
Figure 5: Object visibility analysis for MOMA- GEN and ablations. The x-axis is the % of frames where the target object is visible during navigation, and the y-axis is the trajectory count (out of 1000). MOMAGEN significantly outperforms ablations thanks to both hard and soft visibility constraints. Can MOMAGEN generate demos with a new robot embodiment? We evaluate cross-embodiment data generation by using single source demo from a Galexea R1 robot to generate Pick Cup demos on a TIAGo robot. MOMAGEN successfully transfers across platforms by planning/replaying dense end-effector trajectories in the task space, which is largely agnostic to robot-specific kinematics, demonstrating robustness and flexibility of MOMAGEN, More discussions are found in Appendix B.2.
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Methods
Pick Cup
Tidy Table
Put Dishes Away
Clean Frying Pan
MOMAGEN
1.00
0,86
0.79
0.69
SkillMimicGenn
1.00
0.40
0.71
0,65
DexMimicGen MoMAGEN w/o soft vis. const.
1.00
0,39
0.71
0.67
1.00
0.63
0.62
0.56
MOMAGEN w/o hard vis. const.
0.98
0.63
0.68
0.55
MoMAGEN w/o vis. const.
0.90
0.46
0.40
0.35
MOMAGEN
0.93
0.89
0.78
0.80
DI
MOMAGEN w/o vis. const.
0.71
0.46
0.40
0.43
MOMAGEN
0.94
0.79
0.75
0.81
D2
MOMAGEN w/o vis. comst.
0.73
0.48
0.40
0.44
Table 3: Task-relevant object visibility comparison. Our hard and soft visibility constraints are exceedingly effective in keeping the object in view during navigation, achieving over 75% visibilityy even for aggressively randomized object pose (DI) and obstacles/occluders (D2). We omit baselines for D1/D2 due zero data generation success rates.
5.4 POLICY LEARNING WITH GENERATED DEMONSTRATIONS
While MOMAGEN can synthesize successful trajectories for the benchmark tasks, relies on privileged information such ground-truth object poses and geometry. To enable real-world deployment, visuomotor policies must still be trained from onboard sensory inputs (e.g.. RGB images). In this section, investigate: (1) whether MOMAGEN generated demonstrations improve imitation learning performance over other data generation methods; (2) whether these benefits hold across different imitation learning algorithms; (3) how task-relevant object visibility influences policy training; (4) how performance scales with the number of generated demonstrations; and (5) whether model trained on MOMAGEN data can be successfully deployed on real hardware.
Policy Learning Setup. We experiment with two imitation learning based methods, WB-
McKarien SullMinueeen DeaMiiitiiin
MoMatee
SkilMinic Arained DO)
VIMA (Jiang et al., 2025a) and
Shocceos Itate
0.75
25
MrMaaenn (tratned Bi)
BesMinoceen (rnined
To (Black et al., 2024). Both methods take as input proprio- ceptive info and RGB images from the head camera and two wrist-mounted cameras, and outputs the target robot joint position. For WB- further fuse and post-procens the three RGB images (with grountruth depth from the sim- ulation) into egocentric col- ored point cloud, before feed- ing into the policy network. For WB-VIMA. dividual single-task policies from scratch, whereas for Tn. we finetune a pre-trained To model with LoRA rank of 32. More implementation de- tails are Appendix C.
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Figure 6: Comparison between MOMAGEN and other data genera- tion methods on WB-VIMA's performances in (a) and (b), perfor- mances of WB- VIMA and To trained with MOMAGEN data in (c) and visibility ablations in (d). The success rate is averaged over 20 unseen evaluation episodes. Policies trained on MOMAGEN data consistently perform better than those trained others' data.
How do different data generation methods impact policy performance? We generate 1000 demonstrations with MOMAGEN for Pick Cup (DO/D1), and Tidy Table (DO), and compare against SkillMimicGenn and DexMinGGeen augmented with base trajectory replay. As shown in Figure 6a, MOMAGEN matches the baselines on Pick Cup (DO), where the small randomization range (0.3m x 0.3m) makes replayed navigation sufficient. On Tidy Table (D0), however, MOMAGEN significantly outperforms the baselines, which overfit to the long, nonsmooth replayed trajectories. For the more challenging Pick Cup (DI) task (1.3m 0.8m randomization). only MOMAGEN enables WB-VIMA to achieve a 0.25 success rate, while baselines trained on DO data fail completely (Figure 6b). The diverse base motions in MOMAGEN data also improve intermediate success like touching the cup.
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Does MOMAGEN generated data benefit different imitation learning methods? We fine-tune To on MOMAGEN data for Pick Cup (DO/D1) and Tidy Table (DO) using 1000 demonstrations. As across all three
shown in Figure 6c, the fine-tuned Mo achieves success rates comparable to tasks. These results indicate that MOMAGEN data can effectively improve the performance of diverse imitation learning approaches (either trained from seratch, or pretrained and LoRA fine-tuned).
How does object visibility ratio affect policy performance? In MOMAGEN, the head camera constrained to focus on the task-relevant object, aiding visual servoing during navigation and improving visibility during manipulation (Sec. 5.3). To assess the impact of these constraints on imitation learning, we conduct an ablation study with WB-VIMA on Pick Cup (DO) and Tidy Table (DO), comparing the full method with three variants: (1) without soft visibility (camera not encouraged to track the object during navigation); (2) without hard visibility (camera not enforced to observe the object during manipulation); and (3) without any visibility constraints. As shown in Figure 6d, ablations on Pick Cup (DO) achieve success rates of 0.45 to 0.65, below the 0.75 of MOMAGEN. The gap is even larger Tidy Table (DO), where ablations peak at 0.05 while MOMAGEN reaches 0.40. These results demonstrate that enforcing visibility constraints during data generation substantially improves policy performance, particularly when policies rely short history inputs.
How is the model performance scaled with the number of MOMAGEN generated demonstrations? We extend the To experiments to examine the effect of scaling MOMAGEN- generated data. Models were fine-tuned with 500, 1000, and 2000 demonstrations for 50K steps and evaluated across tasks and randomization levels (Figure 7). Results show promising scaling trends, particularly under D1 randomiza- tion, where larger datasets improve coverage of both state and action spaces. These findings indicate that increasing synthetic data yields meaningful performance gains.
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Can policies trained with MOMAGEN' generated data be deployed in the real world? To evaluate the real- world utility of MOMAGEN, we tested the Pick Cup task using the same robot platform and setup similar to our simulation (white table, green cup). Because zero-shot sim-to-real transfer is challenging, collected 40 real-world demos and assessed the benefit of pretraining on 1,000 synthetic demos generated by MOMAGEN, For WB- VIMA, pretraining on synthetic data followed by fine-tuning on real data achieved a 10% success rate, while training on real data alone yielded 0%. Although the overall success was low, the pretrained model consistently exhibited meaningful behavior (e.g.. reaching the cup), whereas the baseline failed to progress. For Ty. the effect was stronger: the pretrained model achieved 60% success compared to 0% for the baseline. In this case, the baseline attempted grasps but failed due to poor precision, with the cup slipping from the gripper, underscoring the difficulty of learning robust policies from limited real data alone, even when starting from a strong pretrained foundation (i.e. To pretrained weights). Despite the sim-to-real gap, these results show that diverse synthetic data provide a valuable prior, enabling efficient policy learning in low-data regimes. This highlights the practical utility of MOMAGEN for real-world deployment (see Appendix A.
Figure 7: Data Scaling of To
5.5 MOMAGEN FAILURE AND COMPUTATIONAL COST ANALYSIS
Failure Analysis. We analyze the causes of unsuccessful data generation episodes by categorizing failures into base sampling, base-level planning, arm-level planning, and simulation instabili- ties. Base Sampling failures occur when no base pose is found that satisfies reachability and visibility constraints. Base MP IK and Base MP TrajOpt correspond to inverse- -inemmtis and trajectory- optimization failures during base motion planning. Similarly, Arm MP IK and Arm MP TrajOpt capture IK and trajectory- optimization failures during arm motion planning, while Arm MP Other includes additional arm-planning issues such as invalid start or goal states. Simulation Instabilities arise from controller inaceuracies and stochastic effects in the simulator. Fig. 8 shows that across all three domain randomizations, simulation instabil- ities account for substantial share of failures (35%), which can
DO Randomizzation
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base NP
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Figure 8: Failure Analysis

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500
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Figure 9: Compute costs (wall-clock time) for MOMAGEN data generation components.
be mitigated by improvements in controller robustness and simulation fidelity. Arm-level motion planning contributes the largest proportion of planner-related failures (40% on average), exceeding base-level planning failures (26% on average). Notably, in D2 randomization, navigation- related failures (base sampling, base IK, and base TrajOpt) increase significantly due to floor obstacles in an already tight navigation space. We also analyze the distribution of failures across task-level steps within each multi-step task (Fig. 16 Appendix).
Compute Cost Analysis. To better understand the ef- ficiency of our data generation pipeline, we analyze the computational cost contribution of each component. Fig. 9 shows the wall-clock time distributions for base sampling, motion planning, and simulation execution. Simulation ecution dominates total computation time, greatly exceed- ing the corresponding planning durations. For instance, base motion planning averages 18 seconds, whereas ex- ecuting the resulting motion simulation takes 100 sec- onds. Base sampling also exhibits high variance due to our current strategy of random sampling within ring-shi shaped region around the target: when only few poses are fea- sible, the search becomes slow, whereas abundant feasible poses lead to quick success. Future work may reduce this variance through more informed sampling methods for example, biasing toward regions with greater free space. Fig. 10 reports the average compute cost of each compo- nent percentage of the total episode duration, com- puted only over successful episodes to ensure fair comparison across all components. Notably, base sampling becomes increasingly expensive from DO D2-reflecting the rise in scene complexity, where fewer feasible base poses exist and longer searches are required.
DO Randomization
D1 Randomization
D2 Randomization
Compute cost of each com- ponent as a percentage of the episode
Figure 10:
CONCLUSIONS AND LIMITATIONS
In this work, present MOMAGEN, general data generation method for multi-step bimanual mobile manipulation using a single numan-collected demonstration. MoM AGEN formulates data generation as constrained optimization problem that satisfies hard constraints while balancing soft constraints. We propose key novelties that involve reachability and visibilty constraints, and evaluate our method on four challenging bimanual mobile manipulation tasks. We showcase superior diversity and task-relevant object visibility of D.MMAGGN-eeeraaie data compared to those generated by baselines and ablations, which further translates to better policy learning results.
Limitations. We currently assume access to full scene knowledge during demonstration generation. While this is straightforward in simulation, it poses challenges in real-world scenarios. A possible solution is to incorporate vision models such as SAM2 to estimate object poses relative to the robot. Additionally, we only show data generation results with alternating phases of navigation and manipulation, although doors) and we leave it for future work. Lastly, our approach depends on sizable GPU resources to run apP-aaceler motion generators, which can be computationally intensive during data generation.
framework is casily extensible to whole-body manipulation (c.g. opening
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REPRODUCIBILITY STATEMENT
We provide all the source code for simulation task setups, data generation, policy training, and detailed instructions for reproducing all data generation and policy learning results in our code submission (included in the supplementary materials) as well as on our website. All training hyperparameters and implementation details are documented in Appendix C facilitate full reproducibility.
ACKNOWLEDGMENTS
This work is in part supported by the Stanford Institute for Human--Cntereed Al (HAT), ONR MURT N00014-22-1-2740, ONR MURI N00014-24-1-2748, UT-CNS Catalyst Grant and Amazon Personal Robotics Group (PRG) Faculty Award.
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A ADDITIONAL EXPERIMENTAL RESULTS
A.1 SIM-TO-REAL
Real robot setup. We investigate whether MOMAGEN can support real-world deployment, using the Galaxea RI mobile manipulator, the same platform used for data collection and generation in simulation. Point clouds are captured with RI's onboard ZED 2 stereo camera and an additional ZED Mini mounted above the left gripper. To mitigate depth noise, we apply the built-in Al model for point cloud refinement, followed by farthest point sampling to 4096 points, as in simulation. We evaluate on the Pick Cup (DO) task with table of comparable height to the simulated setup and 3D-printed green cup. The real-world configuration is shown in Figure 1 lla.
Experiment setup. sero transfer is challenging due to domain gaps, particularly for vision- based policies (Jiang et al., 2024a). In our case, simulated and real RGB images differ substantially, and real-world depth estimates are noisy. To mitigate this, we collect 50 real-world demonstrations for the Pick Cup (DO) task and evaluate whether simulation pretraining improves real-world learning.
Sim-to-real experiments demonstrate the benefit of simulation pretraining, For WB-VIMA VIMA, we compare models initialized from cntuulatton-peetrrinn checkpoint (1.8M steps 1,000 synthetic demos) against random initialization. Both are fine-tuned for 35k steps on 40 real demos, with 10 held out for validation. As shown in Figure 1 lb, the pretrained model converges much faster, reaching a validation loss of 3.0 compared to 6.0 for the baseline. On real hardware, it achieves 10% success, while the baseline fails entirely. Despite low absolute success, the pretrained model consistently exhibits meaningful behaviors (e.g., reaching the cup), unlike the baseline, which shows no progress.
For TT. we compare two variants initialized from pretrained weights: one further trained on simulation data before fine-tuning on 40 real demonstrations, and one fine-tuned only on real data. The simulation pretriinnn- model achieves 60% success, while the baseline remains 0%. Although the baseline occasionally attempts grasps, it fails due to poor precision, highlighting the challenge of learning robust visuomotor policies from limited real data. This result shows that even with a strong foundation pretrained on 10k+ hours of robot data, it is beneficial to first adapt to the target robot embodiment and scene setup with simulation data before fine-tuning on scarce real-world data.
Overall, these results show that diverse simulation-generatie data provide strong prior, enabling efficient fine-tuning and effective sim-to-real transfer, particularly in low-data regimes. This finding demonstrates the practical utility of MOMAGEN-genent data for real-world robotics. More real-world policy rollouts can be found on our website.
Validation Loss
Validatooccon
20
500
1k
1.k
(a) Real World Setup
(b) Validation Loss
Figure 11: Real world setup for Pick Cup (a) and validation loss curve of WB-VIMA (b).
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B ADDITIONAL DATA GENERATION DETAILS
B.1 CAN MOMAGEN GENERALIZE ACROSS DIFFERENT FAMILIES OF MOBILE MANIPULATIONN TASKS?
Beyond the tasks analyzed in the main paper (rigid-body bick-and-place. wiping, and stacking), we further investigate whether MOMAGEN extends to other common families of mobile manipulation behaviors. In particular, we consider three representative tasks that span different "families" of mobile manipulation: (i) articulated-ohject interaction, (ii) pouring and particle-transfer tasks, and (iii) whole- body control. These proof-of-concepy demonstrations highlight the versatility of MOMAGEN in generating stable and collision-fie trajectories across diverse tasks. Vidcos for all tasks are provided our project website.
Get Bottle from Fridge (Articulated Object Manipulation). The robot navigates to fridge, grasps and opens the door with one arm, and then retrieves bottle inside. This task also demonstrates MoMAGEN's ability to manage occlusion, as the bottle is initially hidden until the fridge is opened. For the initial subtasks, the fridge treated as the task-relevant object; once the door open, the target switches to the bottle.
Pour Cat Food (Pouring and Particle Interaction). The robot grasps a tin of cat food containing rigid particles, navigates to bowl, and performs controlled pouring motion until all particles are transferred. This task highlights MOMAGEN's ability to generate trajectories that require coordinated container orientation, stable end-effector motion, and interaction with particle-like objects, representative of the broader family of pouring tasks.
Push Chair (Whole-oody Control). chair initially positioned outside table. The robot navigates to the back of the chair, establishes contact, and pushes it into place underneath the table. This task requires coupling the robot's base movement with the end-effector contact direction, demonstrating that MOMAGEN can generate whole-body coordination behaviors involving sustained contact and the manipulation of large objects.
B.2 CAN MOMAGEN ACHIEVE CROSS- EMMODIMETT DATA GENERATION?
We evaluate embodiment data generation by using a single demonstration collected Galexea RI robot to generate trajectories for TIAGo robot. Although both platforms feature dual 7-DoF arms and holonomic bases, their torso designs and arm workspaces differ substantially. Our experiments show that MOMAGEN can successfully produce Pick Cup demonstrations on TIAGo using the R1 source demo. This transfer is enabled by planning and replaying dense end-effector trajectories, rather than joint-space trajectories, making the process largely agnostic to robot-specific kinematics. These results highlight the robustness and flexibility of our framework across platforms. Videos of the generated demonstrations of this new robot embodiment are available on our website.
transfer, however, has limitations. Tasks requiring operation in confined spaces may fail due to differences in gripper size, leading to unavoidable collisions. Similarly, TIAGo's bulkier arms can render some trajectories from demos infeasible or even cause self-collisions. Thus, while MOMAGEN offers a proof of concept for cross-embodiment data generation, advancing more powerful approaches in this direction remains an exciting avenue for future work.
B.3 VISUALIZATIONS OF DOMAIN RANDOMIZATION SCHEMES
Figure 12 illustrates the domain randomization schemes (DO/D1/D2) across all four tasks. Lever- aging base sampling, MOMAGEN can generate successful demonstrations under more aggressive randomization (D1) than prior methods (D0). With motion planners in the loop, it further handles obstacles both base and arm motion (D2), producing richer trajectories and more diverse visual observations.
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Cup
Tidy Tahle
Put Awaay
Frying
Source
DO
DI
D2
Figure 12: Visualization of Domain Randomization. Blue represents the task-relevant objects. Green represents the approximate randomization range for these objects (for DI and D2, the range is anywhere the same furniture). Red represents the obstacles/dissraator objects.
B.4 VISUALIZATIONS OF DATA DIVERSITY
Figure 13, 14, and 15 extend the visualizations in Figure 4, comparing MOMAGEN to SkillMimieGen and DexMimicGen across the other three tasks. As expected, the object diversity (subfigures a) induces substantially more varied robot base and end-effector trajectories (subfigures b-c) and joint configurations (subfigure d), resulting in broader state- and action-space coverage.
B.5
COMPUTE RESOURCES
The compute resource required for data generation scales with 1) the number of subtasks, 2) the length of each subtask (including the free-space subtask and contact-rich subtask), affecting motion execution time, and 3) the complexity of the scene (task-relevant objects, obstacles, etc), affecting motion planning time. inversely scales with the data generation success rate. Each successful demonstration takes 0.1 to 1.3 GPU hours to generate, ranging from Pick Cup task to Put Dishes Away task. All data generation runs are conducted on single NVIDIA TITAN RTX GPU.
MrM.CDe DJ McM
McMo
Mo DE SeTMPacce
MrMaeiea Deddliiaoien
Mc Marin
Diversity
Diversity
Robot End-Effisctr Diversity
Joint Position Divenity
Figure 13: Generated data diversity analysis for Pick Cup task (50 trajectories, subsampled).
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MaStaran ckibbeescoe 100
MoMtuCes sld EC
Noliaaa Suileeccee DO
Moble
Object Diverity
Diversity
(c)
Diversity
Joint Diversity
Figure 14: Generated data diversity analysis for Put Dishes Away task (50 trajectories, subsampled).
Mastubes R0 Moskibbe D1
Nodiaake D Nobillee
PS Feclar
DHO DA
Objos Divenity
(b) Robol Basc Divanity
(6) Robut End-EEliictur Divenity
Robot Joint Posiium Divenity
Figure 15: Generated data diversity analysis for Clean Frying Pan task (50 trajectories, subsampled).
B.6 FAILURE STEP ANALYSIS
Fig. 16 illustrates the distribution of failures across the individual steps of each multi-step task. For example, in the put dishes away task, failures occur more frequently during the navigation and placement steps compared to the plate-picking step.
RUN
Pick Cup
Tidy Table
Put Dishes Away
Clean Frying Pan
Figure 16: Distribution of failures across task-level steps within each multi-step task.
C ADDITIONAL POLICY TRAINING DETAILS
In this section, we first describe the data cleaning process in Section C.1 and then provide additional training details for WB-V VIMA (Section C.2) and my (Section C.3). For both methods, 90% of the demonstrations are used for training and the remaining 10% for validation.
C.I DATA CLEANING
Teleoperation in simulation makes depth perception difficult, often causing operators to hesitate and leave the gripper nearly stationary before grasping or making contact. As a result, some demonstrations contain short "frozen" segments, which can degrade training, especially for imitation learning methods with limited temporal context (e.g., WB-VIMA uses 2-step history, and mo relies only on the current state). To address this, we introduce preprocessing step that removes frozen segments. Speifically, for trajectory of length T, if at any timestep i [0, T 5) the absolute joint position difference between step and i + 5 is below x 10-3 3 all dimensions, we treat the interval [i, i + 5] frozen and discard it prior to policy learnin..
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[-1
1-2
(a) Pick Cup point cloud visualization
FF
1-2
1-4
1-5
1-6
1-7
(b) Tidy Table point cloud visualization
Figure 17: Visualization of ego-centric point cloud for Pick Cup and Tidy Table, used for WB-VIMA training. We fuse the colored point-cloud from three RGB-D cameras, crop with a robot-centric bounding box, and then downsample to 4096 points via farthest point sampling.
C.2 WB-VIMA TRAINING DETAILS
Policy Architecture. WB-VIMA (Jiang et al., 2025a) takes as input both proprioceptive obser- vations and an egocentric colored point cloud. The proprioceptive inputs include base velocity base R3, torso joints grorso E R left arm joints glett R6 left gripper width qarip-left left E R right arm joints gright E IR5 and right gripper width qirip-right E RR. The point cloud is constructed by fusing RGB-D images from an eye-level camera and two crist-mounted cameras; examples for Pick Cup and Tidy Table are shown in Figure 17. It is then cropped with robot-centric bounding box and downsampled 4096 points via farthest point sampling. WB-VIMA uses 2-step history input. Additional architectural details are available in the original paper (Jiang et al., 2025a). For each task, we train WB-VIMA from scratch to obtain single-task policy.
Hy Hlyeepaaramttes. Training hyperparameters for the PointNet (point cloud processing), diffusion head, transformer backbone, learning rates, and task-specific point cloud clipping ranges are sum- marized in Table 4. The ego-centric clipping ranges customized per task to reflect differences in scene layout and object randomization. All hyperparameters are chosen via grid search.
C.3 TRAINING DETAILS
Policy Architecture. We fine-tune pretrained To model using LoRA with rank 32 for 50k steps and a batch size of 64. The model takes as input the RGB images from the eye-level and two wrist-mounted cameras, along with the same proprioceptive signals used by WB-VIMA and predicts the target joint position for the next 50 time steps.
Hyperparameters. All RGB inputs are resized to 224 x 224. Actions and proprioceptive signals normalized using the 1st-99th quantile and zero-padded match the 32-dimensional action
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Hyperparameter
Value
Number of points in point cloud
4096
PointNet hidden dim
256
PointNet hidden depth
PointNet output dim
256
PointNet activation
GELU
Proprioceptive MLP imput dim
Proprioceptive MLP hidden dim
2
Proprioceptive MI.P hidden depth Proprioceptive MLP output dim Proprioceptive MLP activation
256
256
ReLU
Transformer embedding size
512
Transformer layers
Transformer heads
8
Transformer dropout rate
Transformer activation
GEGLU
Action dim Unet down dims Unet kernel size
21
[128, 256]
Unet number of groups Diffusion step embedding dim Diffusion noise scheduler
8
256
DDIM
Number of training steps
100
Beta schedule
squaredcos _atpooss__p______e
Number of denoise steps per inference
16
Learning rate
le-4
Learning rate scheduler
Cosine decay 100000
Learning rate warmup steps Learning rate cosine steps
1300000
Optimizer
Adam W
Batch size per GPU
128
Number of GPUs in parallel
2
Pick Cup (DO) pointeloud clip range
[0.0, 2.3], y: [-0.5, 0.5], z: (0.7,
Pick Cup (D1) pointcloud clip range
x: [0.0, 2.7]. y: [-100, 1.0), z: (0.7,
Tidy Table (DO) pointeloud clip range
x: [0.0, 2.3), y: [-1.5, 1.5), 7: (0.7, 1.5]
Model size
37.1M
Table 4: Hyperparameters for WB-VIMA.
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Hyperparameter
Value
Proprioceptive MLP input dim Proprioceptive MI.P output dim
32
1024
Flow Matching MLP input dim
32
Flow Matching MLP hidden dim
2048
Flow Matching MLP hidden depth
2
Flow Matching MLP output dim
1024
Flow Matching MLP activation
swish
PaliGemma embedding size
2048
PaliGemma number of layers
18
PaliGemma number of heads
18
PaliGemma heads dimension
256
PaliGemma MLP dimension
16384
Action expert embedding size Action expert MLP dimension
1024
4096
Number of flow matching steps per inference
10
Learning rate
2.5e-5
Learning rate scheduler
Cosine decay
Learning rate warmup steps Learning rate cosine steps Optimizer
1000
30000
AdamW W
Batch size
64
Number of GPUs in parallel
4
Model size
3.3B
Table 5: Hyperparameters for mp.
space expected by Tu. The model uses PaliGemma VLM backbone (Beyer al., 2024) with 1000pparameter action expert. Further architectural details are (Black et al., 2024), and additional training hyperparameters listed in Table 5.
C.4 COMPUTE RESOURCES
With a batch size of 128, WB-V VIMA can be trained on two RTX 3090 GPUs (24GB each), taking approximately 40 hours reach million steps. With a batch size of 64, my can be trained four H200 GPUs, taking approximately 7 hours to reach 50k steps.
D USE OF LARGE LANGUAGE MODELS
We used large language model (ChatGPT) solely for writing refinement, including grammar correction and improving clarity and conciseness of the text. The model was not used for research ideation, experimental design, analysis, content generation beyond language editing.
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Reviews:
[{"review_id": "NIgmu1izSW", "reviewer": "Reviewer_scaJ", "summary": "MoMaGen addresses the challenge of generating diverse demonstration data for bimanual mobile manipulation tasks, where collecting human teleoperation data is prohibitively expensive due to the complexity of controlling both the mobile base and two arms simultaneously. The key contribution is formulating data generation as a constrained optimization problem that satisfies hard constraints (reachability, collision avoidance, object visibility during manipulation) while optimizing soft constraints (visibility during navigation, compact retraction). Unlike prior methods (MimicGen, SkillMimicGen, DexMimicGen) that fail on mobile manipulation, MoMaGen jointly optimizes base pose, camera pose, and end-effector trajectories to generate diverse, high-quality demonstrations from a single human demonstration. Evaluated on four household tasks with aggressive randomization, MoMaGen achieves 63% generation success rate and 75-100% object visibility (vs 35-65% for baselines), enabling policies trained on synthetic data to outperform baselines and successfully transfer to real hardware with minimal fine-tuning (60% vs 0% success with π₀ using 40 real demos).", "questions": "**Questions**\n\n**Q1: Base Pose Sampling Specification** - What is the exact probability distribution for sampling base poses (Algorithm 1, line 8)? What are the spatial bounds relative to target objects? How many sampling attempts occur before declaring failure? This specification is critical for reproducibility and theoretical analysis of the method's convergence properties.\n\n**Q2: Failure Mode Distribution** - What percentage of generation failures are attributable to base sampling timeout, IK infeasibility, motion planning failures, and simulation instabilities respectively? Given that 37-78% of attempts fail across randomization levels, understanding the primary bottleneck is essential for targeted improvements.\n\n**Q3: Real-World Scaling and Task Complexity** - Why were multi-step bimanual tasks (Tidy Table, Clean Frying Pan) not evaluated on real hardware? Does the sim-to-real gap remain consistent across task complexity, or do coordination-heavy tasks exhibit degraded transfer?\n\n**Suggestions**\n\n**S1: Formalize Base Sampling Methodology** - Provide rigorous mathematical specification of the base pose sampling procedure in the main paper, including explicit probability distributions, parameterized spatial bounds, and well-defined termination criteria. This is essential for reproducibility and enables theoretical analysis.\n\n**S2: Comprehensive Failure Analysis** - Include a detailed breakdown categorizing failure types by frequency across tasks and randomization levels. Correlate failures with scene characteristics (object density, workspace constraints) to identify systematic limitations and guide algorithmic improvements.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "**Originality**\n\nThe paper makes several original contributions. The constrained optimization formulation elegantly unifies existing X-Gen methods while introducing novel visibility and reachability constraints specific to mobile manipulation. The distinction between hard constraints (must satisfy) and soft constraints (desirable) is intuitive and principled. Notably, the work is the first to tackle automated data generation for bimanual mobile manipulation, addressing visibility of moving cameras and reachability with mobile bases—problems unexplored in prior work. However, the core technical components (motion planning, IK solving, task-space replay) largely build on existing tools (cuRobo), with the main innovation being their orchestration under the constraint framework. The cross-embodiment generation, while interesting, is acknowledged to have significant limitations (gripper size, workspace differences) that limit its practical applicability.\n\n**Quality**\n\nThe experimental methodology is rigorous and comprehensive. The three-level randomization scheme systematically evaluates increasing difficulty, and comparisons against two strong baselines are fair. The evaluation covers multiple important dimensions: data diversity metrics, generation success rates, visibility analysis, and downstream policy performance with two different algorithms. Ablation studies effectively demonstrate the contributions of hard and soft visibility constraints. The sim-to-real experiments, while limited to one simple task, provide important validation.\n\n**Clarity**\n\nThe paper is well-structured with clear mathematical formulation and effective visualizations. Some technical details lack precision—the \"heuristics\" for base pose sampling (line 8, Algorithm 1) are mentioned but not specified, making full reproducibility difficult despite code availability claims.\n\n**Significance**\n\nThis work addresses a critical bottleneck in scalable robot learning for household tasks, and the ability to generate diverse demonstrations from a single source is valuable. The constrained optimization framework provides a principled foundation for future methods. The sim-to-real transfer demonstrates practical utility: achieving 60% success with $\\pi_{0}$ using only 40 real demonstrations (versus 0% without pretraining) shows that synthetic data provides a strong prior that improves sample efficiency in the real world. This represents meaningful and well-validated progress toward scalable mobile manipulation learning with clear pathways for real-world deployment.", "weaknesses": "**Limited Real-World Validation and Inadequate Scaling Analysis**\n\nThe sim-to-real evaluation is insufficiently comprehensive. Only the simplest task (Pick Cup) is deployed on real hardware, achieving modest success rates (10% for WB-VIMA, 60% for π₀), which fails to validate whether the diversity benefits generalize to multi-step bimanual coordination tasks. Furthermore, while Figure 7 demonstrates data scaling trends in simulation, the choice of specific quantities (500, 1000, 2000 demonstrations) is not justified, and no corresponding analysis exists for real-world deployment. The paper does not explain why 1000 demonstrations is chosen as the primary experimental setting, nor does it investigate whether this quantity represents an optimal trade-off between generation cost and policy performance. The optimal quantity of synthetic data for sim-to-real transfer remains unexplored. A rigorous evaluation should include at least one complex multi-step task on physical hardware, provide principled justification for dataset sizes, and conduct systematic scaling experiments to characterize sample efficiency in real-world settings.\n\n**Unquantified Annotation Overhead**\n\nWhile the method claims to require \"only a single source demo,\" the annotation requirements detailed in Section 4.2 (target object, gripper-held object, pre-grasp timestep, end timestep, retraction type) represent non-trivial human effort that is neither quantified nor analyzed. The absence of timing measurements or discussion of automation strategies (e.g., leveraging vision-language models for object identification or contact detection for grasp inference) limits assessment of the method's practical scalability. A complete evaluation should quantify annotation time per task and investigate semi-automated annotation approaches.\n\n**Underspecified Sampling Methodology**\n\nThe base pose sampling procedure (Algorithm 1, line 8) lacks sufficient mathematical specification. The paper references \"heuristics\" and sampling \"near target objects\" without defining the probability distribution, spatial bounds, rejection criteria, or termination conditions. This ambiguity impedes reproducibility and precludes theoretical analysis of convergence properties or sample complexity. The method requires rigorous formalization including explicit distributions, parameterized bounds, and well-defined stopping criteria.\n\n**Absence of Failure Mode Characterization**\n\nDespite generation success rates ranging from 22-63% across randomization levels, the paper provides no taxonomic analysis of failure modes. The relative contributions of base sampling failures, inverse kinematics infeasibility, motion planning failures, and simulation instabilities remain uncharacterized. This lack of diagnostic information obscures the primary algorithmic bottlenecks and limits targeted improvements. A systematic failure analysis categorizing failure types by frequency and correlating them with scene complexity, task structure, and randomization level would provide essential insights for future development.\n\n**Insufficient Computational Cost Analysis**\n\nThe reported computational cost (0.1-1.3 GPU hours per demonstration, Appendix B.5) lacks contextualization and granular analysis. The paper does not decompose this cost across algorithmic components (motion planning, inverse kinematics, sampling iterations, simulation), nor does it provide comparative analysis against human teleoperation baselines. Without quantitative cost-benefit analysis comparing alternative data collection strategies (e.g., 1000 human demonstrations versus 1 human + 999 synthetic), practitioners cannot make informed deployment decisions. A rigorous efficiency analysis should profile computational bottlenecks and establish cost-effectiveness relative to conventional data acquisition methods.", "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": "sGuxMLHO9k", "reviewer": "Reviewer_8M33", "summary": "The paper proposes MoMaGen, a data generation framework which leverages soft (object visibility during navigation, retraction) and hard constraints (visibility during manipulation, reachability) to generate mobile manipulation demonstrations (with navigation and manipulation in separate stages). The authors evaluate the data generated by MoMaGen (diversity, object visibility, cross-embodiment support), policies learned with MoMaGen demonstrations (SR compared with baselines, object visibility, data scaling), and sim2real via pretraining on MoMaGen demonstrations and finetuning on real demos.", "questions": "- What are the major causes for the relatively large computational cost per successful demonstration, i.e. is this due to the computational costs of the optimization scheme, due to the underlying simulator, etc? Are there means to reduce the computational cost to generate data more quickly, especially when given access to fewer GPUs?\n- Are there empirical benefits in MoMaGen to providing additional teleoperated demonstrations (since, once a teleoperation setup is built, generating and annotating a few source demos is approximately as difficult as generating a single demo)?\n\nSmall notes:\n- On page 6, final paragraph, \"robot-specific kinematic\" should be \"robot-specific kinematics\"\n- On page 19, in the Fig 13 caption, \"downsampe\" should be \"downsample\"", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Hard and soft visibility constraints are novel, and per experiments improve performance across tasks\n- Method is validated on a range of free-space and contact-rich tasks, at varying levels of randomization\n- Thorough experiments on the generated data and policy learning with MoMaGen data\n- Method works well with just one human demonstration, reducing human supervision requirements\n- Real-world transfer experiment (finetuned with 40 real demos), demonstrates benefits of pretraining on MoMaGen's generated demonstrations", "weaknesses": "- The current setup does not provide demonstrations for coordinated upper and lower-body control, a key developing area in mobile manipulation research\n- Authors note that each successful demonstration takes ~0.1-1.3 GPU hours, which can substantially limit large-scale data generation. Such large-scale generation is important for especially complex, long-horizon mobile manipulation tasks", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zDHf8nsQnT", "reviewer": "Reviewer_Nrsb", "summary": "This manuscript introduces MoMaGen, a novel framework designed to automate the generation of large-scale, diverse demonstrations for complex multi-step bimanual mobile manipulation tasks. The work addresses the significant challenge and cost associated with collecting such high-dimensional data via traditional teleoperation, which requires controlling a mobile base, dual high-DoF arms, and active vision simultaneously. MoMaGen leverages a constraint-based system to generate these demonstrations in simulation efficiently. The research validates the quality of the resulting comprehensive dataset by successfully training and testing two SOTA imitation learning baselines.", "questions": "1. **Automation Gap**: How can the reliance on manual annotations and heuristic checks for constraints (e.g., visibility, reachability) be eliminated to achieve a fully automated demonstration generation pipeline?\n\n2. **Scaling Task Diversity**: What specific, proposed architectural or procedural updates will be implemented to increase the framework's capability to generate a wide, scalable range of tasks beyond the current four?\n\n3. **Real-World Generalization**: What concrete steps will be taken to validate the system on more complex, multi-step tasks in the real world, and to demonstrate improved generalization or cross-embodiment transfer with less fine-tuning data?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Pioneering Problem Scope**: This research is groundbreaking as it is one of the first to focus on the full-body control problem, which integrates a mobile base, active vision, and dual-arm collaborative manipulation. This novel scope holds significant, pioneering implications for general-purpose robot control.\n2. **Comprehensive Dataset Contribution**: The study constructs the most comprehensive MoMaGen dataset to date within the X-Gen series. This large-scale, diverse resource is invaluable for advancing the research and development of mobile manipulation learning.\n3. **Superior Data Acquisition Efficiency**: The MoMaGen framework demonstrates an effective method for acquiring high-quality demonstration data with relatively lower cost and higher efficiency compared to mainstream, traditional teleoperation methods, contributing a smart solution to the data bottleneck problem.\n4. **Thorough Empirical Validation**: The authors performed substantial work by rigorously testing two SOTA imitation learning baselines on the newly generated MoMaGen dataset, which effectively substantiates the quality and utility of the synthesized data for policy learning.", "weaknesses": "1. **Over-reliance on Heuristics for Automation**: Despite the formal definition of numerous hard and soft constraints, the actual demonstration generation process in simulation still heavily relies on manual intervention, such as simple inverse kinematics, heuristic rules, and human-provided one-shot annotations.\n\n2. **Limited Task Generalization and Scalability**: The study currently only covers a small number of distinct task types (a total of four). This limited diversity is significantly less than what is typically observed in large-scale, simulator-based demonstration generation work, raising concerns about MoMaGen's efficiency and ability to scale up for generating true variety in large datasets.\n\n3. **Unconvincing Real-Robot Validation**: The physical robot experiments are narrow in scope, only covering the simplest 'pick cup' task with minimal cross-embodiment transfer and a scene closely resembling the simulation. The low final success rate, even after fine-tuning with additional dozens of real-world demonstrations, suggests that the proposed system's real-world robustness is limited, and simpler baselines (like ACT or DP) might yield similar performance with comparable fine-tuning.", "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": "kMZTQFFTcz", "reviewer": "Reviewer_MKkL", "summary": "The paper presents a demonstration generation framework through optimizing soft and hard constraints in bimanual mobile manipulation. Specifically, the hard constraints are formulated as the reachability, visibility during manipulation. The soft constraints are formed as the visibility while navigation. The proposed MoMAGen targets to satisfy the hard constraints while balance the soft constraints in the augmentation procedure of human demonstrations. The proposal is evaluated on four multi-step bimanual mobile manipulation tasks and it generates substantially more datasets than prior methods, which could be further exploited in the further imitation policy learning.", "questions": "please see the weaknesses.", "limitations": "", "rating": 6, "confidence": 1, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.\tThe involving of reachability and visibility constraints are reasonable in the general data generation for multi-step bimanual mobile manipulation. \n2.\tBetter diversity and task-relevant object visibility are achieved by MoMaGen compared to other baselines.\n3.\tThe ablation studies are very comprehensive to investigate the influences of the two constraints in demonstration data generation.\n4.\tGreate policy learning is attained on the generated demonstrations by MoMaGen.\n5.\tThe paper is clear written.", "weaknesses": "1.\tOne concern about this work is the efficiency of the demonstration collections and the computation cost of GPU for the constraint optimization. \n2.\tIs there any optimization for Eq.(1) when optimizing these constraints? Is there any conflicting terms in this equation? Some discussions should be included.", "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)