Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training
Authors: Shuo Cheng
Liqian Ma
Zhenyang Chen
Ajay Mandlekar
Caelan Reed Garrett
Danfei Xu
Venue: NeurIPS 2025 poster ()
Keywords: Imitation Learning
Optimal Transport
Sim-to-Real
Primary area: reinforcement_learning

Abstract: Behavior cloning has shown promise for robot manipulation, but real-world demonstrations are costly to acquire at scale. While simulated data offers a scalable alternative, particularly with advances in automated demonstration generation, transferring policies to the real world is hampered by various simulation and real domain gaps. In this work, we propose a unified sim-and-real co-training framework for learning generalizable manipulation policies that primarily leverages simulation and only requires a few real-world demonstrations. Central to our approach is learning a domain-invariant, task-relevant feature space. Our key insight is that aligning the joint distributions of observations and their corresponding actions across domains provides a richer signal than aligning observations (marginals) alone. We achieve this by embedding an Optimal Transport (OT)-inspired loss within the co-training framework, and extend this to an Unbalanced OT framework to handle the imbalance between abundant simulation data and limited real-world examples. We validate our method on challenging manipulation tasks, showing it can leverage abundant simulation data to achieve up to a 30\% improvement in the real-world success rate and even generalize to scenarios seen only in simulation.

Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training
Shuo Cheng
Liqian Ma
Zhenyang Chen' Danfei Xu
Ajay Mandlekar Caclan G Georgia Institute of Technology 2 NVIDDI Corporation
and denote equal contribution (shuocheng, mlq)@gatech. edu
Abstract
Behavior cloning has shown promise for robot manipulation, but real-world demon- strations are costly to acquire at scale. While simulated data offers scalable alternative, particularly with advances in automated demonstration generation, transferring policies to the real world is hampered by various simulation and real domain gaps. In this work, we propose unified sim-and-real co-training frame- work for learning generalizable manipulation policies that primarily leverages simulation and only requires a few real- task-relevan feature space. Our key insight is that aligning the joint distributions of observations and their corresponding ac- tions across domains provides a richer signal than aligning observations (marginals) alone. We achieve this by embedding an Optimal Transport (OT) -inspirdd loss within the co-training framework, and extend this an Unbalanced OT framework to handle the imbalance between abundant simulation data and limited real-world examples. We validate method on challenging manipulation tasks, showing it can leverage abundant simulation data to achieve up to 30% improvement in the real-world success rate and even generalize to scenarios seen only in simulation. Project webpage: https:: www.eeal..gitu............ gitggb.ttbbi io/
proach is learning domain-invariann real-world demonstrations. Central to our ap-
Introduction
Behavior cloning is promising approach for acquiring robot manipulation skills directly in the real world, due to its simplicity and effectiveness mimicking expert demonstrations [2] However, achieving robust and generalizable performance requires collecting large-scale datasets across diverse environments, object configurations, and tasks. This data collection process is labor-intensive, time-consuming, and costly, posing significant challenges scalability in real-world applications.
Recently, with rapid advancements in physics simulators 6 77 procedural scene generation 88 [9) and motion synthesis techniques [1O [1]. there has been growing interest in leveraging simulation as an alternative source of training data. These simulation based approaches enable scalable and controllable data generation, allowing for diverse and abundant supervision at fraction of the real-world cost. However, transferring policies trained in simulation to the physical world remains non-trivial challenge due to sim-to-real gap- the discrepancies between the simulated and real- world environments that a policy encounters during execution. These differences can manifest in various forms, such as variations in visual appearance, sensor noise, and action dynamics []2] [3]. In particular, learning visuomotor control policies that remain robust under changing perceptual conditions during real-world deployment continues be an open area of research.
Common strategies to bridge this domain gap include domain randomization augmentation [14 15), though these often require careful tuning. Domain adaptation (DA) techniques
12 13 and data
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Simulation
esaas dorce
Optimal Transport Alignment
Palicy Co-Training
Large demos settings
Generalize states seon only in
Real-world
π (f)(
settings
Shared Palicy Latent Space
Real-world Deployment
Figure I: Sim-and-Real Co- Do-Training with Optimal Transport. We use behavior cloning to train real-world policy from sparse real-world and dense simulation demos. Leveraging Optimal Transport to align feature spaces, our method enables generalization to scenarios seen only in simulation.
aim to explicitly align distributions, either at pixel [6 [7 or feature levels 18 19 20 However, many feature-level methods align only marginal observation distributions (e.g.. MMD [8 19]). which
can be insufficient for fine-grained manipulation alignment as may not preserve action-relevant relationships across domains. More recently, sim-and-real co-training-siin simply training single policy on mixed data from both domains [21 has shown surprising effectiveness. We argue that while beneficial for data diversity, such co-training approaches typically lack explicit constraints for feature space alignment across domains, potentially hindering optimal transfer and generalization because they don't it enforce consistent mapping of task-relevant structures.
We present a unified sim-and-real co- co-training framework that explicitly learns shared latent space where observations from simulation and the real world aligned and preserve action-relevant infor- mation. Our key insight is that aligning the joint distributions of observations and their corresponding actions task-relevan states across domains provides direct signal for learning transferable features. Concretely, we leverage Optimal Transport (OT) [2 as an alignment objective to learn represen- tations where the geometric relationships crucial for action prediction
consistent, irrespective of whether the input comes from simulation or the real world. Further more, to robustly handle the data imbalance in co-training with abundant simulation data and limited real-world data, further extend to an Unbalanced OT (UOT) formulation [24] 25 and develop a temporally-awand sampling strategy improve domain alignment learning in mini-batch OT setting.
Our contributions are: (1) sim-and-real co-training framework that learns a domain-invarinn yet task-salient latent space to improve real-world performance with abundant simulation data, (2) an Unbalanced Optimal Transport framework and temporally-aware sampling strategy to mitigate data imbalance and improve alignment quality in mini-batch OT training, (3) comprehensive experiments using both image and point-cloud modalities, evaluating tom-to-sim and sim-to-real transfer across diverse manipulation tasks, demonstrating up to 30% average success rate improvement and achieving generalization to real-world scenarios for which the training data only appears in simulation.
Related Work
Behavior Cloning for Robot Manipulation. Behavior cloning (BC) trains policies to map ob- servations to actions by imitating expert demonstrations [2.
to human-like manipulation skills. Generalization heavily depends offering an effective path dataset diversity. While some efforts focus on large-scale real-world data collection 5 4 or more efficient collection tech- niques 29 30 31,, this remains costly and time-consuming. Physical simulators provide a low-cost alternative, with automatic motion synthesis leveraging privileged information to generate large- Our work combines abundant simulated data with few
26 27 28
scale simulated demonstrations 10
32
33
real-world demonstrations to train robust BC policies.
Sim-to-real Transfer and Co-training. Policies trained solely in simulation often underperform in the real world due to the sim-to-real gap dap-discrepancies visual appearance and dynamics. For quasi-static manipulation, the visual domain gap is typically the primary bottleneck. Domain

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randomization exposes policies to varied simulated visual conditions to build robustness to real-world However, its success depends on how well randomized parameters cover true real-world distributions, often requiring manual tuning. Domain adaptation (DA) explicitly aligns
variability [ 34 35].
source (simulation) and target (real) domains [36
37 Pixel-level DA uses image translation to make
simulated images resemble real ones [66
17,38 (38] Feature-level DA, which is often more scalable
for end-to-end learning, focuses on learning domain-invariant representations [9 20 [8] 39 40]. Sim-and-real co-training, where policy is jointly trained on mixed data 21 [2]. offers a simple and effective alternative. While co-training enhances generalization through data diversity, it typically lacks explicit constraints align learned feature spaces across domains. We build upon co-training by incorporating feature-level domain adaptation via Optimal Transport to promote latent space alignment, thereby improving real-world policy performance.
Optimal Transport for Domain Adaptation. Optimal Transport (OT) offers a principled frame-
work for aligning distributions, widely adopted for domain adaptation 23 41 42
43
44
45, 46].
Traditional OT methods compute transport plan between source and target samples, then train new model on the transported source. Most relevant to is DeepJDOT 47), which builds on JDOT [44] to align joint distributions of features and (pseudo) labels for unsupervised domain adaptation, where target labels are unavailable. Our work builds on these principles for sim-and-real co trainini imitation. Unlike unsupervised DA, we leverage available action or state labels from limited real-world demonstrations as "soft" supervision to guide more task-relevant alignment of joint observation label distributions across domains. To robustly handle the inherent data imbal- ance between abundant simulation and scarce real data, we incorporate an Unbalanced OT (UOT) loss [24] into our co-training framework and develop a temporally-awan sampling strategy to improve mini-batch UOT training.
3 Preliminaries and Problem Setting
Our method builds on the principle of Optimal Transport (OT) for aligning two empirical distributions. Let U- {u.)== and V { represent the data points drawn from source domain and target domain, with corresponding empirical distributions p = Σ'= Prou; and q = Σド= 47005- We define the ground cost matrix C = (Ct.)) E <R=* with Ci.j c( 0z). where c(:, is cost function, which is often defined as squared Euclidean distance. Optimal Transport (OT) seeks find an optimal plan II that maps the distribution p to q that minimizes the displacement cost W (P. q):
Wa g)
min (I,C),, s.t. m = P. II 1n = q.
(1)
NERa
3.1 Problem Setting: Sim-and-Real Policy Co- Co-Training
We address the challenge of learning robust real-world robotic manipulation policies т. Our approach minimizes the need for extensive real-world data collection by primarily leveraging abundant sim- ulation data alongside a small set of real-world demonstrations. This is framed as a sim-and-roe co-training problem (22 48). where a single policy trained on data from both domains. Specifically, we consider a source domain (simulation, denoted src) and a target domain (real-worid, denoted tgt). We model the domains as Partially Observable Markov Decision Processes (POMDPs) that share an underlying, generally unobserved, state space S and an action space A. The policy receives observations comprising nighddimesiional visual input o E O (e.g.. RGB images, 3D point clouds) generated by the emission function E- S + O, together with low-dimensional proprioceptive information I E X' (e.g., robot joint angles, end-effector pose).
[22448),
Domain Gaps. The central challenge is the domain gap. particularly the visual observation gap. For the same underlying robot and environment state s S. visual observations emitted in simulation, Oare = Esve(s) (s), can differ significantly from those in the real world, Otgt Enge This discrepancy arises from factors like variations in visual appearance (textures, lighting), sensor noise, and rendering artifacts (e.g., differences between simulated ray casting and real-world light transport). As result, the marginal observation distributions differ between the domains, namely (ore()) # In contrast, actions a and proprioceptive states x are assumed to be largely consistent for given s due to consistent data generation strategies (discussed next) and accurate robot state estimation. While differences in dynamics also contribute to the domain gaps, our focus on learning quasi-static prehensile manipulation tasks from human-sourced demonstrations means that the dynamies gap is typically less dominant than the observation gap.
3

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Sim Batch
A
Lgc
aaIV Desance
fΦ
Sim Demos
Luor
Compute joint cost (
OT Map
fΦ
Action Space
Tt
Lgc
Real Demos
Real Batch
Temporally Aligned Sampling (Sec 4.3)
Feature Alignment via (Sec 4.1, 4.2) Joint Co- Traininn (Sec 4.4)
Figure 2:
Method Overview. Our sim-and-real co-training framework learns a domain-invarian
latent space improve real-world performance using abundant simulation demos and small number of real-world demos. It leverages an Unbalanced Optimal Transport loss and a temporal sampling strategy address data imbalance and improve alignment quality during mini-batch training.
Data Sources and Data Imbalance. In the source (simulation) domain, leverage the ability to automatically generate large dataset of Nare trajectories, Dere {(o'me: x agrr)) N ) (=1
Specifically, we leverage an automated demonstration generation tool MimicGen [10], which utilizes privileged information available in simulation to create a diverse of experiences covering broad underlying state space, Sare- MimicGen populates synthetic demonstrations based on a handful of human demonstrations, which ensures that the generated data is behaviorally consistent with human
demonstrations. We collect a limited number of Ntgt demonstrations, Digs = {(ol Ziger dlge)) ) Negs (where N > Negt), typically through human teleoperation. These real-world demonstrations will naturally cover much smaller and potentially distinct subset of states, Stgt. This difference in data coverage leads to the challenge of partial data overlap. While we assume there is region of states common to both Sare and Sigt where direct alignment is possible, a significant portion of Sare (our rich simulated data) will not have corresponding real-world demonstrations in Segt. Conversely, Sige might contain details specific to real world (e.g.. demonstration behaviors). Effectively leveraging the entirety of Sre for real-world performance, especially for states outside the direct sim-real overlap in demonstrations, is the problem address.
j-1
Objective: Generalizable Domain Adaptation. Our goal is learn single, generalizable policy To(a|2, 2) and an observation encoder fo O U + Z. This encoder maps figh-dimensional visual observations o from both source and target domains to a shared latent space Z. The primary objective is to achieve high policy performance in the target (real-world) domain, especially in scenarios not explicitly covered by the limited real- woal-world demonstrations Degt- This entails two objectives. First, for states within the overlapping regions of Serc and Stat, aim to learn high- quality embeddings : = (o) that are well-aligned across domains, such that (O & fo (Otgt) in corresponding states, facilitating effective policy learning. This is also the assumption of most co- co-training methods [21 (2). Second, for states covered in Save but not Sigt, the encoder fo must produce embeddings fo (Otgt) for novel target observations that are consistent with the embeddings of their simulated counterparts fOrre... This requires the learned representations to capture domain- invariant, fask-relevant features, enabling policy generalize target (real-world) scenarios for which training data is only present in the source (simulated) domain.
4 Method
To learn a generalizable policy Ta with robust feature fo from imbalanced sim-and-real datasets <(De >> (Deet)) )). outlined in Section 3 we propose a co-training framework that explicitly aligns the latent representation through Optimal Transport (OT). Our core strategy is to leverage action information to guide the alignment of learned visual latent features (z (o)). This aligmment objective simultaneously encourages the encoder fo to discover domain-invariam representations while preserving detailed information for action prediction. Specifically, we propose formulation based on OT to align joint observation-aciin imbalance problem through Unbalanced Optimal Transport (UOT) (Sec. 4.2 and temporally-awan
distributions (Sec. 4.1 We further address the data
sampling strategy (Sec. 4.3) all integrated into unified co-training framework (Sec. 4.4)

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4.1 Optimal Transport for Action-Aware Feature Alignment
While standard co-training methods 21 22 offer implicit feature alignment, and marginal distribution matching (e.g,, Maximum Mean Discrepancy (18) can overlook fine- fine-grained correspondences, we seek a more structured approach. To learn predictive of actions E A. we propose to align the joint distributions Pare (Osre), (sre) and (S (tgt)) using Optimal Transport. This encourages the encoder J to learn representations where the geometric relationships between (visual feature, action) pains are preserved across domains. By minimizing an OT-based loss, the encoder fo is trained to shape the embedding space Z such that structures relevant to action prediction are consistent between simulation and the real world.
fo(o) that are also invariant visual features
Formally, given source samples (gre)) Err and target samples {lolge: dege) ) aim to find an optimal transport plan II" and an optimal encoder f that minimize the transportation cost between their joint distributions in the (z, a) space. Based on the general OT formulation in Eq. [ learning objective for the encoder fa. and implicitly the transport plan II, can be expressed as finding f that minimizes the Wasserstein distance between We miny, in both the learned visual latent space 2 and the action space A, for instance:
(re(ff((ere)) Gare) and Piot (fo (Otgt), atgt): asre), Ptge (f (Oigt), atg:)). The ground cost e ideally combine distances
Co (ffoldere), d'roe, (fo (olgt)) d a1 dz (f(s(do) foldlgt) + a2 dd(a'ror degt). (2)
Minimizing this objective using iterative algorithm like Sinkhorn 49 creates bi-level optimiza- tion. In the inner loop, for fixed fa. an approximately optimal transport plan II is computed. In the outer loop. fo is updated to reduce the cost incurred by this plan. This process effectively trains the encoder fo to produce embeddings that make the source and target joint distributions (z, a) less costly to align. A key advantage of OT its ability preserve geometric structures; by guiding alignment with action similarity (via d'A,, we shape the embedding function fo to cluster visual observations that lead similar actions, irrespective of their domain of origin.
Practical Implementation: Proprioception as Guidance. While direct alignment of (z, a) is principled, discrepancies in controller characteristics action representations between simulation and real-world teleoperation can make d (Asre: (tgt) an unreliable indicator of behavioral similarity. As robust practical compromise, we leverage proprioceptive information æ E X (c.g.. end-effector pose), which is more consistently represented across domains (Section 3.1) and highly correlated with robot behavior. Thus, our implemented ground cost c replaces actions a with proprioceptive states x, which is used in our UOT formulation (detailed in Section
4.2 Unbalanced Optimal Transport for Robust Alignment
The standard OT formulation (Eq. D enforces strict marginal constraints, requiring all mass from the source distribution to be transported to the target and vice-versa. This is problematic in our sim-to-real setting primarily due to significant data imbalance (Derrel >> |Digel) and the partial overlap between the state spaces covered by Dere and Deys (Section 3.1 Standard OT would either distort the latent space by forcing many-to- mappings or create spurious alignments between non-corresponding states. To address these challenges, we employ Unbalanced Optimal Transport (UOT) 25 24 UOT relaxes the hard marginal constraints of OT by introducing regularization terms that penalize deviations, thereby allowing for partial mass transport. This enables UOT to selectively align subsets of the distributions that are most similar according to the ground cost, while effectively deighweighting or ignoring the transport for dissimilar or unmatched portions.
UOT Loss Formulation. Consider a mini- of Nbatch source samples {(o x 1 Nuatoh and xf zigt) Nmyce. Let their empirical distributions in the joint (fo(o),
Noatch target samples {(olye>
space be fare and jittt- Our UOT loss, Luor(fé), is based on the Kantorovich formulation with entropic regularization and KL-divergence penalties for marginal relaxation:
Luor(fo)=
min IE Nhatch Kbatch
(I, Co) Q(TT) + T KL (p) + KL(TTT 1|/a).
(3)
Here, Co is the Noatch x Nbatch ground cost matrix, where each element (Co)ss is computed using the joint ground cost described in Sec. 4.T The term IT is the transport plan; 6 >> 0 is the entropic regularization strength with Q(II) Ex, May log 11g being the entropy, facilitating efficient solution
5

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via algorithms like Sinkhorn- Sinkhorn-Knopp 149 T > 0 controls the penalty for deviating from the batch
marginals p and q (typically uniform); and (-I-ll) denotes the Kullback-Leiller divergence.
4.3 Temporally Aligned Sampling for Effective Mini-Batch Learning
The efficacy of mini-batch OT, including UOT, hinges on presenting the solver with comparable source and target samples within each batch. For sequential robotic data, naive random sampling of individual transitions from Dare and Digt may yield pairs from different stages of tasks, leading to noisy transport plans and sub-optimal feature alignment by fo- Increasing the minibatch size may lead to higher likelihood of sampling aligned pairs but requires more computational resources.
To address this, we introduce a temporally aligned sampling strategy designed construct mini- batches with a higher density of meaningfully corresponding state-pairs. Our strategy leverages trajectory-level similarity as heuristic. We first quantify similarity between source trajectories [shre) C Dare and target trajectories {Eige] C Digt using Dynamic Time Warping (DTW) [50] on their respective proprioceptive state sequences {xz}. The resulting normalized DTW distance, d(ck Sigr) = dptw dDTw(ESre / max((ssrel. (6tg)) ), reflects overall behavioral similarity. To turn these distances into sampling weights, we apply softplus-based transformation: w Sigt)
1/(1 + e fit ).01).. Mini-batch construction then proceeds by (1) sampling pair of trajectories (Esres Stge) with probability biased towards pairs exhibiting high similarity (i.e., low DTW distance) and (2) subsequently sampling individual transition tuples (Osres Eare; (sre) and (Oigt, atgt) from this selected, behaviorally similar trajectory pair.
Fine-grained temporal alignment, such as sampling around DTW-matched time steps, can optionally be employed here. We describe how the UOT loss (Eq. B) is adapted with this new sampling procedure in the appendix.
This two-stage process significantly increases the likelihood that source and target samples within mini-batch share similar proprioceptive states x. Consequently, the UOT optimization (Eq. 3 can more effectively focus aligning the visual latent features (0 and fo (e(ge)) for these relevant state-pairs. We empirically verify the importance of this sampling strategy in appendix.
4.4 Joint Co- To-Trrinnng Framework
Putting all components together, our final approach is a joint co-training framework where the visual feature encoder fo and the policy Ty (a|z, are optimized concurrently. The Unbalanced Optimal Transport loss (Luor) serves as regularization term, guiding fo to learn domain- invariaat and action-relevant latent representations drive the policy learning. The overall training objective L(fa, To) LBc T&) + X Luot (f)) combines these components, where Lac represents the combined behavior cloning losses calculated over both source (Dsre) and target (Degt) datasets using a standard imitation loss (e.g., MSE). The hyper-pane parameter À > 0 balances feature alignment with policy imitation. The Luor(fo) term is computed as defined in Equation 3 with
fo), while standard Behavior Cloning (BC) losses
sampled with strategy described Sec. 4.3 The overall training process is detailed in the appendix.
5 Experiments
We aim to validate the following core hypotheses. H1: Our method effectively learns complex manipulation tasks in both simulation and the real world. H2: Our method generalizes to target domains only seen in simulation. H3: Our method is broadly applicable to multiple observation modalities. H4: Scaling up simulation data coverage improves generalization performance.
5.1 Experiment Setups
To evaluate the effectiveness of our approach, we conduct comprehensive experiments in both sim- to-sim and sim-to-real transfer scenarios on a suite of robotic tabletop manipulation tasks: Lift, BoxInBin, Stack, Square, MugHang, and Drawer. These tasks are designed to test the system's 's ability to handle key challenges in robotic manipulation, including dense object interactions, long- horizon reasoning, and high- preciprciiiion control.

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Lift
Stack
BoxInBin
MugHlag
Square
Figure 3: Evaluation Task Suites. We evaluate our methods on 6 different tasks in the real world (lop) and simulation (boltom) to demonstrate the effectiveness of sim-to-real transfer.
Environment setup. For the real-world experiments, we deploy our method on a Franka Emika Panda robotic arm, controlled at 20Hz using joint impedance control. Visual and point cloud observations are captured using Intel RealSense D435 depth camera. Our simulation environments use the Robosuite [5T] simulation framework. We calibrate the camera pose and camera intrinsics in simulation to match those obtained from the woalwwrldd setup reduce the domain gap.
Simulation data. For simulation experiments, we begin by collecting 10 human demonstrations per task. Using MimicGen [10), we synthesize 200-1000 trajectories in the source domain, covering the full range of initial states (denoted as Source). the target domain, we divide the reset region into two subregions: one is populated with 10 trajectories for training (denoted as Target), while the other remains completely held out from training (denoted as Target-00D), This held-out subregion is used to evaluate each method's generalization under Out-Of-Distribund
(OOD) conditions.
Real data. For real-world experiments, we adopt a similar strategy by partitioning the reset re- sion-aligned with the simulation setup into two subregions. Based on task complexity, we collect 10-25 human demonstrations within one subregion and generate 1000 simulated trajectories. To evaluate generalization in OOD scenarios, we consider the following settings in the real world: Shape, where the test object has been seen during real data collection; Reset, where the initial object pose falls outside the range covered by demonstrations; and Texture, where the object is wrapped in a novel texture not present in any real-world training data. Detailed visualizations of each task and reset configuration are included in the appendix.
Observation modality and domain gaps. We evaluate our approach using two observation modal- ities: point clouds and RGB images. For point cloud observations, our method and the baselines adapt 3D Diffusion Policy (2 with PointNet encoder
[52]. For RGB image observations, we use Diffusion Policy [3 with a ResNet-18 18 encoder [54). To evaluate the generalization capabilities of different methods under visual domain shifts in simulation, we introduce several target domain vari- Perturbation-Pa -Pontt, Viewpoint deepointtImaaee
ations: Viewpoint1-Pee -oint, Vieupoint3-Points and rexture-Image. Descriptions of each domain shift are provided in the appendix.
Baselines. We compare our method against the following baselines: MMD-mmniniize the distance between the mean embeddings of source and target data [18; Co-training--rriin the model using a mixed batch of source and target domain data, following the strategy proposed by [21 22]; Source-only-tue tray-trains the model exclusively on data from the source domain, which in sim-to-real experiments corresponds to using only simulation data; on data from the target domain, which in sim-to-real experiments trains with only real world data.
the model exclusively
5.2 Cross-Domain Generalization Results
Stack (V)
Square (V)
BoxInBin (V)
Stack (T)
Square (T)
BoxInBin (T)
Average
T-0
T-O
T
T-O
T-0
T-0
T
T-0
T-O
S. Snno
1..00
0.00
10.00
0.00
00.00
0.00
0.00
0.00
10.00
0.00
00.00
0.00
0.00 0.00
T. Tnny
0.30
0.00
0.20
0.00
0.82
0.00
0.42
0.00
0.48
0.00
0.64
0.00
0.48 0.00
MMD
0.38
0.00
0.18
0.04
0.82
0.16
0.44
0.4
0.38
0.34
0.80
0.70
0.50 0.30
Co-train.
0.44
0.04
0.76
0.00
0.90
0.14
0.54
0.34
0.66
0.46
0.98
0.72
0.71 0.28
Ours
0.65
0.04
0.86
0.02
0.88
0.26
0.66
0.52
0.68
0.54
0.96
0.82
0.78 0.36
Table I: Sim-to-Sim Success Rates for Image-Based Based Policies. V and represent Viewpoint- Image
and Texture-Inae Image domain shifts, respectively. T and T-O correspond to the target domain and
target domain with
(OOD) scenarios.
7

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Stack (R)
Square (R)
BoxInBin (T)
Average
grasp
full
grasp
full
grasp
full
full
Source-only
0.0
0.0
0.0
0.0
0.0
0.0
0.00
Target-only
0.0
0.0
0.1
0.0
0.0
0.0
0.00
Co- po-training
0.0
0.0
0.3
0.0
0,4
0.3
0.10
Durs
0.4
0.4
0.5
0.1
0.7
0.7
0.40
Table 2: Real-World Image- Image-Based Policy OOD Success Rates, R and T denote Reset OOD and
Texture OOD, respectively. The Average denotes the average full task success rates over all tasks.
BoxInBin (V3)
BoxinBin (F)
Lift
(V1) T-0
Stack T
(V1)
Square (V1)
KugHang (V1)
Average
T
T-O
T
T-0
T-0
T
T-0
T
T-0
T
T-0
s.-only
0.08
0.10
0.52
0.60
0.32
0.40
0.52
0.64
0.10
0.08
0.12
0.10
0.28
0.32
T. T.-only
0.42
0.00
0.58
0.00
0.60
0.00
0.32
0.00
0.16
0.00
0.18
0.00
0.38
0.00
XVD 0.50
0.38
0.66
0.50
0.56
0.52
0.70
0.66
0.18
0.12
0.18
0.20
0.46
0.40
Co-train.
0.76
0.52
0.70
0.66
0.92
0.48
0.86
0.72
0.24
0.24
0.26
0.22
0.62
0.47
Dure
0.84
0.58
0.80
0.76
0.80
0.60
0.82
0.86
0.42
0.38
0.40
0.34
0.68
0.59
Table 3: Sim-to-sim Success Rates For Point Cloud-Based Policies. V1, V3, and indicate domain
shifts due to Viewpoint1 -Piint,,,, Viexpoint3- -Point, and Perturbation- Point, respectively.
T and T-O denote the target domain and target domain under out-of-distribution (OOD) conditions.
We report results for policies using point cloud and image- based observations in both simulation and real-world settings. For simulation, image based and point cloud based performance are shown in Tables T and 3 For real-world experiments, in-distribution results are presented in Tables 7 and g in
Appendix, while out-of-distribution (OOD) performance is reported in Tables andd
These results support the following key hypotheses:
Our method effectively learns complex manipulation tasks in both simulation and the real world (HI). Experimental results show that our approach consistently matches or outperforms in terms of success rates all baselines across source and target domains in both simulated and real-world settings. On real-world tasks, our method achieves average success rates of 0.73 and 0.77 for image-based and point cloud based policies, respectively. The Sarget-only baseline performs well in distribution but fails to generalize under domain shifts. The MMD baseline [18] offers limited improvement by aligning global feature statistics, but its coarse alignment often disrupts task-relevant structure and harms source-domain performance. In contrast, by using Unbalanced Optimal Transport, our method performs selective, structure-awee aware alignment, avoiding spurious matches.
Our method generalizes to target domains only seen in simulation (H2). In Target- O0D sce- narios, our method outperforms all baselines, underscoring the value of learning domain-invariant representations (see Tab. and Tab. 4 While Co-trainimg baselines [2] 22 perform well when the target-domain training data overlaps with the evaluation region, they struggle generalize when this overlap is absent. This limitation is especially evident under large domain shifts. For example, in the real-world BoxInBin and Stack tasks with novel textures or reset poses, our method achieves success rates of 0.7 and 0.4, respectively, using image-based 0.0 for the Co-tranning baseline. These results highlight the shortcomings of relying purely on supervised target- domain data without explicitly addressing domain shift.
compare to just 0.3 and
Our method is broadly applicable to multiple observation modalities (H3). We observe consistent performance gains across both image-based and point cloud inputs. In simulation, policies trained with either modality outperform all baselines, demonstrating that our approach effectively learns domain-invariant features that capture task-relevant information on multiple sensory modalities.
Simulation data provides scalable and effective way to augment real-world training. Across real-world tasks, both our method and the Co-training bascline benefit significantly from augment- ing limited real-world demonstrations with simulated data. Policies trained with this augmented data consistently outperform the Target-only baseline, especially in out-of nuttoo-disttrbuttin (OOD) settings where real-world coverage is sparse. This underscores the value of using low-cost simulation data to fill in gaps in real-world datasets, enabling more scalable and generalizable behavior cloning.
Scaling up simulation data coverage improves real-world performance (H4). To analyze sim- ulation data scaling, we consider the Stack task in the real world with point cloud observation. We generated 100, 300, 500, and 1000 simulated trajectories, combined them with 25 real-world
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Stack grasp
Square (R)
BoxInBin (R)
Lift (R) reach
Lift (S) reach
Lift
(R+S)
Average
full
grasp
full
grasp
full
full
full
reach
full
full
S. s.-only
0.6
0.3
0.1
0.1
0.3
0.2
0.8
0.8
0.6
0.6
0.9
0.9
0.48
T. T.-only
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
1.0
1.0
0.0
0.0
0.17
Co-train.
0.4
0.1
0.6
0.2
0.2
0.0
0.8
0.8
1.0
1.0
0.9
0.9
0.50
Ours
0.8
0.4
0.6
0.1
0.7
0.5
1.0
1.0
1.0
1.0
1.0
1.0
0.67
Table 4: Real World Point-Cloud-Basee Policy OOD Success Rates. R and S denote Reset OOD
and Shape OOD, respectively. The Average denotes the average full task success rates over all tasks.
demonstrations, and trained both our method and the Co Co-training baseline. As shown in Fig. 4b). increasing the amount of simulation data significantly improves our method's performance in target domain regions that lack real-world coverage, highlighting the importance of learning domain- invariant latent space that enables the policy to generalize beyond observed distributions.
Our method learns shared latent space that aligns simulation and real data. To better under- stand how the learned embeddings contribute generalization, we visualize them using t-SNE (55), as shown in Fig. a). Blue and red correspond to features extracted from source and target do- main observations, respectively. The left plot shows embeddings from the encoder trained with the Co-training baseline, while the right plot shows embeddings from our method. The visual- ization reveals that our approach leads to significantly better alignment between source and target distributions, highlighting its ability to learn generalization. We also visualize the transport plan on a randomly sampled batch, along with the corresponding image observations from the source and target domains. As shown in Fig, 10 in the Appendix, the transport plan effectively aligns data points with similar states across domains.
representations that facilitate robust
Co-training
Ours
Out-of-Distribution Performance
10
Styrce Tangel
Targei
Soarse
Oum PuD
Cormein my INV
0.8
Success Rate
0.4
0.0
500 1000
Simulation Demonstrations
I-SNE Visualization of Latent Space
(b) COD Success Rale Scaling
Figure 4: (a) Latent Space Visualization. Our OT alignment maps source domain samples (blue) and target domain samples (red) nearby in the latent space, yielding a single, well-mixed cluster. This overlap demonstrates that OT alignment effectively synchronizes cross-domain feature distributions. improving sim-to-real transfer. (b) Out-Of-Distribuut Performance. Scaling the number of simulation demonstrations leads to significant OOD success rate gains.
Limitations and Conclusions
The main limitation of our work is that we only address sim-to-real visual observation gaps. Ad- dressing action dynamics gaps remains future work. Because we use MimicGen [10] for automated simulation demonstration generation, we inherit its limitations, namely, our method primarily applies to tasks with prehensile and quasi-static interactions. We require a small number of real-world on-task demonstrations that are aligned with the simulated demonstrations. Future work involves relaxing this requirement, for example, by instead consuming unstructured real-world data, such play data.
In conclusion, we presented framework for effectively incorporating large datasets of simulation demonstrations into world policy learning pipelines via Seature-consisten co- co-training. We pro- posed using Optimal Transport (OT) align encoder features to be invariant whether observations are from simulation or the real world, improving the transferability of simulation data. Because we have much more simulation data than real-world data, we incorporated an Unbalanced OT loss within our training objective and devised a data sampling scheme that explicitly yields similar simulation and real-works demonstration pairs. Finally, we demonstrated the improved learning performance arising from the simulated demonstrations both in sim-to-sim testbed as well as in real-world tasks.

---

7 Acknowledgment
This work was supported in part by the National Science Foundation under Awards No. 2409016 and 2442393. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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A Table of Contents
The supplementary material has the following contents:
Task and Hardware Setups (Sec. B Detailed descriptions of the hardware setups, dataset, and task settings.
Model and Training Details (Sec. C): Descriptions the neural network architectures used in our experiments and the corresponding training procedures.
Ablation Study Sampling Strategy (Sec. Di: Evaluation and analysis of different
sampling strategies.
Additional Results (Sec. F In-distribution evaluation results for both image-based and point cloud-based policies in the real world.
Transport Plan Visualization (Sec. Visualizations of the optimal transport plan for randomly sampled training batches.
Visualization of Latent Space (Sec. D: Visual comparisons of the learned latent spaces
between our method and the Co-training baseline across additional tasks.
More video results and analysis can be found on our website: https://ot-sin2real wwwpiimmeeaaliihihiii. github.......... io/
B Task and Hardware Setups
To evaluate the effectiveness of our approach, we conduct comprehensive experiments on suite of robotic tabletop manipulation tasks, covering both sim-to-sim and sim-to-real transfer scenarios. These tasks are designed to test the system's ability handle key challenges robotic manipulation, including dense object interactions, long-horizon reasoning, and high-precision control:
Lift: Grasp the rim of mug and lift it vertically;
BoxInBin: Grasp tall box and place it into a bin;
Stack: Grasp small cube and stack on top of a longer cuboid;
Square: Grasp the handle of a square-shaped object and insert onto peg:
MugHang: Grasp the rim of mug and hang it on mug tree using the handle;
Drawer: Open a drawer, grasp coffee pod from the table, place it into the drawer, and close the drawer.
B.1 Hardware Setups
The system setup is illustrated in Fig. 5
We Franka Emika Panda robot controlled via joint impedance controller [56] running at 20 Hz for policy execution. For data collection, the robot is teleoperated using Meta Quest 3 headset, with tracked Cartesian poses converted to joint configurations through inverse kinematics. RGB image and depth are captured using an Intel RealSense D435 depth camera.
B.2 Domain Shifts and Observation Gaps
We assess generalization under visual domain shifts in simulation through designing the following target domain shifts:
Neewointt-Poinne The camera is rotated approximately 30 around the z-axis, resulting in a side view the target domain compared to a front-facing view in the source. Point cloud observations are used.
Viewpoint3-Point The camera is rotated approximately 90º around the z-axis, introduc- ing a more extreme viewpoint shift. Point cloud observations are used.
Perturbation-Pont Random noise sampled uniformly from the range [-0.01, 0.01] added to each point in the point cloud to simulate sensor noise domain shift.
23

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and Res RealSense
D435
Ezanka Robot
Quest 3
Figure 5: Hardware Setup. Our hardware platform uses a Franka Emika Panda robot, with an Intel RealSense D435 camera for capturing image and depth, and Meta Quest 3 headset for teleoperation.
Black (Inaço)
Stack Texture-Inae
VioNpoint
(Tmagel
Box Toxtnn Source (Point Cloud)
Rox Tnikin VLowpoint
Box Tolli Poz buzbat www.tutttttteeeoto ico-Point
Roxe TnBin
Boal (Point 2loud)
Figure 6: Observation Gap Across Domains. Top: image observations for the Stack task from Texture- Image, and real-world domains. Bottom: point cloud ob-
source, Viespointl-Image servations for the BoxInBin task from source, Viewpoint3-Par Point, Perturbation-Pon real-world domains. Point cloud color is for visualization only and not used as input to the policy.
Viewpointi-Imme Image: A 20" camera rotation around the z-axis is applied. RGB image observations are used.
Texture- Imatu: The table texture in the target domain is modified. RGB image observa- tions are used.
We illustrate the observation gap across all domains in Fig. 6 The first row displays image obser- vations for the Stack task from the source domain, Viewpointi Image, Texture- Image. and the real world. The second row shows point cloud observations for the BoxInBin task from the source domain, Seppontt--Point Perturbation on-tinn... and the real world. Point cloud color is for visualization only and not used as input to the policy.
B.3
Task Datasets, Reset Ranges, and OOD Variants
We focus primarily on evaluating policy performance in regions covered exclusively by source- domain demonstrations. conduct controlled experiments, we define three distinct reset regions for each fask-Source. Target, and Target-00D as shown in Fig. 7 Specifically:

---

Lift
Stack
BoxInBin
MugHang
Square
Drawer
Number of real demos
10
25
20
15
25
25
Table 5: Number of Real-World Demonstrations. We collect 10 demonstrations task, varying with task difficulty.
Source Domain
Targel Domein (Sim, example)
No sim-lo-s sim exporiments
Target Domain (Real)
Distribution regeet range (sparse demos)
Out-On-iistibutton ("Arget--om range (no demos)
[cense demos)
Figure 7: Reset Ranges for Each Task. The first row illustrates the Source region, where dense source-domain demonstrations are collected. The second row shows the Target and Target- reset ranges used in sim-to-sim transfer experiments, In this setting, the Target region is sparsely covered by demonstrations, while the Target-00D region contains no demonstrations and used exclusively for policy evaluation. The third row similarly presents the Target and Target-00D regions for sim-to-real transfer experiments.
Source: A large region that is densely covered by demonstrations in the source domain. We generate 1000 demonstrations using MimicGen [0] within the Source region.
Target: A small subset the Source region. This region is sparsely covered by demon- strations in the target domain, and is therefore considered in-distribution during evaluation. For sim-to-sim transfer, we collect 10 demonstrations within this region. For sim-to-real transfer, the number of real-world demonstrations collected in the Target region is adjusted based on task difficulty, as detailed in Tab. 5
Target- No demonstrations are collected the Target-00D region, which used solely for evaluation and treated out-of-distribution (OOD).
For sim-to-real transfer experiments, in addition to the reset range OOD (denoted as Reset), we consider two additional OOD variants. In the Texture variant, the object's texture is modified to that is unseen in the real-world demonstrations. In the Shape variant, the object is replaced with novel shape not encountered in the real-world demonstrations. These variants illustrated in Fig.
Texture DOD
Shape OOD
Shape OOD
Target reset range
Terget-000 reset range
Figure 8: Texture and Shape OOD in Sim-to-Rent Real Experiments. Visualization reset ranges for the BoxInBin task under Texture OOD, and the Lift task under both Shape OOD and Shape+Reset OOD conditions.
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C Model and Training Details
For point cloud-based experiments, we adopt the 3D Diffusion Policy architecture (28] with a PointNet encoder [52]. The diffusion head receives features extracted from the point cloud observations along with robot proprioceptive inputs (joint and gripper positions), and outputs 7-DOF target joint positions and the gripper action. We project the depth map into the robot base frame to generate the scene point cloud. For pixel with coordinate (u, v) and depth d, the corresponding 3D location can be recovered by:
p" -R-KK I++
where / = (u-d, u. d), [R| denotes the camera pose obtained through hand-eye calibration [57] and K denotes the camera intrinsic matrix. We crop the reconstructed scene point cloud using bounding box defined by E [-0.2, 0.1]. E [-0.2, 0.2), and E [0.008, 0.588) to exclude irrelevant background information. The cropped point cloud is then downsampled to 2048 points using Farthest Point Sampling (FPS) (58).
For experiments with image-based policy, we adopt Diffusion Policy (7 with a ResNet-based [54] visual encoder. The original images are captured by the camera at resolution of 480 x 640. During preprocessing, the images are downsampled 120 160, followed by random cropping to 108 x 144 during training and center cropping during testing. The policy takes stacked history images and robot proprioceptive inputs (joint and gripper positions) as input, and outputs 7-DOF target joint positions along with the gripper action.
Our overall training procedure is summarized in Algm. We use batch size of 256 for the behavior cloning loss LBC, with a co co-training ratio of 0.9 following Maddukuri et al. [22]. For the optimal transport loss Lor. the batch size set to 128, with a weighting coefficient À 0.1. We use = 0.0005 and T = 0.01 in our experiments.
Algorithm Joint Policy Training with OT
Require: Source dataset Deres Target dataset Digt
1: Initialize encoder fø. and policy ma
2: Compute DTW distances for all trajectories pairs in Dave and Digt
3: for iteration t to T do
4: Sample a paired batch {(o
spcr x
srey aares ogg zbge alge)t with size N from Dere and Digt
using strategy described in Sec. 4.3
5: Compute features {= } and { using encoder fo
6:
Construct ground cost matrix Co as described in Sec.
4.1
7:
Compute optimal transport plan II' arg min ((II, Co)F + 2(III +
KL(III||P) + KII
1l[9)) KL(IIT1 via Sinkhorn-Knoph algorithm 49 Compute OT loss LuoT (fo) = (II", CC))
8:
9:
Sample (O srres ) from Dare and sample {(ol zige alge)) from Degs
10:
Compute BC loss LBc(fe. (f..TT)
11: Update f and Ty with gradients of Luc(Sa, + A Luor
12: end for
D Ablation Study on Sampling Strategy
To assess the effectiveness of our sampling strategy, we compare the full method against a variant (denoted as Durs w/o Sampler) that does not apply any trajectory-level sampling. In this baseline, source and target data are randomly sampled across trajectories and time steps, with no coordination. We also include an oracle variant (denoted as UOT-Oracle), which constructs perfectly paired batches-each state is observed in both the source and target domains to ensure that batch data originates from the same underlying states. We evaluate policy performance on the Stack task under the Viewpoint1-Post -Pinnt variation, with results shown in Fig.
26

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Performance Comparison Domains
1.2
0.96 0.6.
6.97
0.92
0:92
0.8
0.64
Success
0.6
0.52
0.50
0.2
0.0
Source
Target
Target-OOD
Paired
Figure Sampling Strategies Comparison. Our proposed sampling strategy (Ours) improves policy success rates on the Stack task with riexpoint1-Poime outperforming Ours w/o Sampler, and achieving performance comparable to the oracle-paired upper bound (UOT-Oracle).
Temporal-aware strategy improves pairing quality and downstream performance. The oracle baseline demonstrates that, given perfectly aligned data, unbalanced OT loss significantly enhances generalization by enabling the encoder to learn domain-invariant representations. In contrast, the no-sampling variant (Ours w/o Sampler) exhibits poor generalization in the Target- setting. This degradation likely stems from the low probability of encountering aligned state pairs in mini- batches batches-especialll especially problematic in hong-horizon tasks, where uncoordinated sampling rarely produces temporally aligned data.
E
Hyperparameter Sensitivity Analysis
We conduct an ablation study evaluate the sensitivity of our method to key hyperparameters, including the entropy regularization coefficient (e), the KL divergence penalty term (r), and the window size used in temporally aligned sampling. In each experiment, we vary a single hyperpa- rameter while keeping the others fixed, train the policy, and assess its performance via rollouts. We report results for the BoxInBin task under the Viewpoint-Imag Image setting and the Lift task under the Texture- Image setting, shown in Table 6 The results indicate that our method is robust to hyperparameter variations within reasonable ranges. Specifically, performance remains stable when and set between 0.001 and 0.1, and when the window size is varied between 5 and 20. Our method consistently outperforms the co-tra co-training baseline in OOD scenarios, where the baseline achieves success rate of 0. 0.14 BoxInBin and 0.6 Lift. These findings suggest that, although our approach introduces additional components, it does not require extensive tuning and offers clear advantages in terms of generalization.
F
Additional Real-world Evaluation Results
We conduct extensive real-world evaluations validate the effectiveness of our approach. Sim-to-real transfer results for in-distribution scenarios are reported in Tab and Tab for image-based and point cloud-based policies, respectively. Results for out-of-distributtio (OOD) scenarios are presented in the main paper.
Experimental results show that approach consistently outperforms all baselines in real-world in-distribution settings. Our method achieves average success rates 0.73 and 0.77 for image-based and point cloud-based policies, respectively, demonstrating its effectiveness in learning complex real-world manipulation tasks.
G Performance with Scarce Target Domain Data
To assess the effectiveness of our method under limited target domain data, we compare against several baselines in the low-data regime for the BoxInBin task with the SLewpoint3-Poine setting,
27

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0.0001
0.001
0.005
0.01
0.04
1
0.0001
0.001
0.01
0.04
0.1
T
0.90
0.94
0.92
0.88
0.90
0.88
T
0.84
0.88
0.80
0.76
0.78
0.76
ТO
0.18
0.16
0.26
0.22
0.18
0.20
T-O
0.60
0.74
0.62
0.66
0.68
0.54
0.0001
0.001
0.005
0.02
0.04
1
0,0001
0.005
0.02
0.04
0.1
T
0.88
0.96
0.94
0.94
0.92
0,94
T
0.78
0.70
0.76
0.76
0.78
0.74
Т-
0.28
0.26
0.20
0.28
0.22
0.22
T-O
0.56
0.67
0.64
0.66
0.62
0.66
winsize
winsize
5
10
20
40
120
10
20
40
120
T
0.82
0.92
0.86
0.90
0.94
0.84
T
0.86
0.80
0.70
0.78
0.74
0.82
TO
0.20
0.22
0.22
0.24
0.16
0.14
0.66
0.60
0.67
0.58
0.56
0.60
(a) BoxInBin
(b) Lift
Table 6: Hyperparameter Sensitivity. In each series of experiments, we vary a single hyperparameter while keeping the others fixed, train the policy, and assess its success rates via rollouts. T and T-O denote the target domain and the target domain under OOD conditions.
Stack full
Square grasp
BoxInBin
Average full
grasp
full
grasp
full
Source-only
0.1
0.0
0.0
0.0
0.0
0.0
0.00
Target-only
0.7
0.7
0.8
0.0
0.7
0.7
0.47
Co-training
0.8
0.7
0.8
0.1
0.9
0.8
0.53
Ours
0.9
0.9
0.9
0.4
0.9
0.9
0.73
Table 7: Real World Image-Basee Policy In-Distribution Success Rates. average full task success rates over all tasks.
The Average denotes the
Stack grasp full
Square grasp
BoxInBin grasp full
Lift reach full
MugHang
Drawer grasp
Average
full
grasp full
open
place
full
full
S. Snnonly
0.3
0.0
0.1
0.1
0.4
0.3
0.5
0.5
0.1
0.0
0.0
0.0
0.0
0.0
0.15
T.-only
0.7
0.4
0.6
0.1
0.9
0.8
1.0
1.0
0.8
0.8
0.9
0.5
0.5
0.5
0.60
Co-train,
0.7
0.7
1.0
0.5
0.8
0.8
0.8
0.8
1.0
0.8
1.0
0.7
0.7
0.4
0.67
Ours
0.8
0.8
1.0
0.4
0.9
0.9
1.0
1.0
1.0
0.8
1.0
0.7
0.7
0.7
0.77
Table 8: Real World Point-Cloud
Policy In-Distribution Success Rates. The Average
denotes the average full task success rates over all tasks.
Demo Target-OOD
5 Demo
Target
Target
Target-OOD
Ours
0.56
0.28
0.70
0.32
Co-training
0.46
0.00
0.38
0.22
MMD
0.42
0.16
0.34
0.22
oarget-only
0.00
0.00
0.46
0.00
Table 9: Performance with Limited Target Domain Data. We report success rates for various methods on the BoxInBin task with the Mooowoint3-Poine setting, under scenarios where data from the target domain is extremely limited.
As shown in Tab. D our approach consistently outperforms the baselines, highlighting robustness with extremely limited supervision.
28

---

Transcort Flan
Sourco Indox
Prtlosity
(a) sim-to-sim
(b) sim-to-real
Figure 10: Transport Plan Visualization. We visualize the transport plan for randomly sampled batch during training the image-based policy, alongside corresponding observations from both domains. The left figure shows a sim-to-sim experiment, while the right shows a sim-to-real experiment. The visualization reveals that the transport plan effectively aligns similar states across domains, as indicated by high transport probabilities.
H Transport Plan Visualization
To understand how optimal transport facilitates domain- invarininvariaat feature learning and enhances cross-domain generalization, visualize the transport plan for randomly sampled batch during training the image-based policy, along with corresponding observations from both domains (see Fig. TOp. The left plot shows results from the sim-to-sim transfer experiment, while the right plot depicts the sim-to-real setting. The results show that the transport plan effectively aligns similar states across domains, encouraging domain fomain-invariant representations.
I Visualization of Latent Space
Consshing
Soase
SNE Visualization for BoxirB Took
(S) Visualization for Square Task
Figure 11:
Latent Space Visualization. Latent space comparison between the Co-training baseline and our method. In our approach, source-domain points (blue) and target-domain points (red) form well-mixed cluster, illustrating how OT alignment harmonizes cross-domain feature distributions and enhances transferability and generalization.
Beyond the feature visualization for the Stack task with the Viespoint1-Point target domain, we also present additional t-SNE [55] visualizations in Fig. [I for the BoxInBin task with the Perturbation-Pon
target domain, and the Square task with the Viewpoint1-Poii domain. We compare the latent spaces produced by the Co Co-training baseline and our method. In our approach, source-domain points (blue) and target-domain points (red) form a well-mixed cluster, highlighting how OT aligmment harmonizes cross-domain feature distributions and improves both transferability and generalization.
target
29


Reviews:
[{"review_id": "YbbYWWc96E", "reviewer": "Reviewer_kja7", "summary": "This paper deals with the problem is using simulator data to train a behavioral cloned policy that can be transferred into the real world or a separate target environment which might have significant differences in perceptual space compared to the source simulator domain. It addresses this problem by assuming some data can be collected in the target domain with expert demonstration. Then it uses the idea of co-training on source and target data to train a policy that can leverage the large amount of data in the source and the grounded data in the target domain.\n\nThe contribution of this paper is to add in an unbalanced optimal transport loss that will pull together the representations for observations in the source and target domains that are behaviorally similar (based on  observation, action pairs) while ensuring that the representations of observations that are not close to these few demonstrations don't collapse due to the unbalanced data volumes.\n\nEvaluation is done in both sim-to-sim as well as sim-to-real settings, along with checking if adding more simulation data helps this approach learn better policies. t-SNE plots are also used to validate if the learned representations are close to each other.", "questions": "* Main questions are about validity of results in paper. Those are clarified in the weaknesses section above. Sufficient answers to them would be good enough for me to recommend acceptance.\n* Assuming the dynamics of the environment is the same in source and target, could $ f(o^i\\_{src}), f(o^{i+1}\\_{src})$ be compared to $f(o^j_{tgt}), f(o^{j+1}_{tgt})$ (i.e. transitions instead of state action pairs)to get a similar objective to the one proposed in the paper? Or does the action/proprioception act as an oracle that can be used by the objective to decide which observations to align?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n* The paper has a clear aim, a clear contribution, and experiments for the most part are designed to validate that contribution\n* Section 3 describing the technique is fairly well written and should not leave an intelligent reader with too many doubts. Appropriate references are made for a diligent reader to follow up and understand details.\n* Practical implementation details are called out in Section 3 instead of hidden in the appendix, which is appreciated.\n* Figures are clear\n* Most relevant work is cited. See some caveats in weaknesses\n\n## Weaknesses\n* The main reason for my on-the-fence recommendation is that this paper depends a lot on empirical evaluation, and there doesn't seem to be any statistical guarantee on any of the results in the paper. None of the tables discusses the number of trials, variance in the results, confidence bounds, or statistical significance in the results. Values are made bold in tables without clarifying what the criterion is for this formatting; are these values the highest numbers, or are they significantly higher? While I forgive forgoing these for real robot experiments, I would require some statistical guarantee from the sim-to-sim experiments to approve this paper. Below I now go in order from the greatest weaknesses to nitpicks.\n* The hypotheses laid out at the beginning of Section 5 are not the same ones mentioned later on page 8. Same or similar wording when mentioning the hypotheses being validated will help a reader to ground the experiments. \n* The phrasing of the hypotheses at the beginning of Section 5 is not clear enough: when mentioning words denoting comparison such as \"improves\" and \"enables generalization\", it is useful to point out what it is improving over (I am assuming baseline co-training performance) or generalizing better than (I am assuming other baselines). \n* `Our method is applicable to different observation modalities` is vague. It is better to say `multiple observation modalities`\n* On line 297, the paper points to the appendix for the validation result of the first hypothesis. First, please reference the particular figure or table a reader should reference. Second, if an result is essential to validate the presented approach, it should be part of the main paper and not the appendix.\n* On line 314, the paper says `our approach effectively learns domain-invariant features that capture task-relevant information regardless of the sensory input.` This seems like over-claiming. Better to say that it does so `on multiple sensory modalities`.\n* In the paragraph starting line 315, results are talked about without referencing the actual table being referenced.\n* In line 336, Figure 5 is no longer the correct figure being referenced (It is again in the appendix, and is now Figure 10).\n* Line 126, I am assuming $S_{src}$ is the number of states covered in the source domain. This distinction from the source state space $\\mathcal{S}$ should be better clarified.\n* Emission functions used in line 113 should be defined earlier when defining observation spaces.\n* I understand that overcoming dynamics mismatch is not an aim of this paper, but related work should acknowledge that dynamics mismatch is one of the challenges in sim-to-real transfer, but clarify that this paper does not deal with it. The authors should reference some seminal papers in this approach. I refrain from suggesting any to avoid biasing the authors.\n* Line 156, ` ... while preserve detailed information ...` should be `... while preserving detailed information ...`", "was_revised": true, "final_justification": "The additional results, with the information on number of trials as well as the variance in the results, have given me the confidence to increase my score.", "rebuttal": "Thank you for your time and constructive feedback. We sincerely appreciate the positive recognition of our work’s clear motivations and contributions, the sufficient implementation details provided, and the well-designed experiments validating these contributions. Below, we address each of the concerns in detail:\n- **Statistical guarantee from the sim-to-sim experiments:** For each task and method, we perform **50 policy rollouts with randomly sampled initial states, following standard protocol in prior works [1, 2, 3]**, where the number of trials is generally considered sufficient to ensure statistical significance. We additionally **report 95\\% confidence intervals using standard error** in Table R1 and Table R2, with the criterion used in prior works[4], for all simulation experiments. In our tables, bolded numbers indicate the best-performing method for each task.\n| method    | BoxInBin (V3) T | BoxInBin (V3) T‑O | BoxInBin \\(P\\) T | BoxInBin \\(P\\) T‑O | Lift (V1) T | Lift (V1) T‑O | Stack (V1) T | Stack (V1) T‑O | Square (V1) T | Square (V1) T‑O | MugHang (V1) T | MugHang (V1) T‑O | Average T | Average T‑O |\n|-----------|---------------|---------------|--------------|--------------|--------------|--------------|---------------|---------------|---------------|---------------|----------------|----------------|--------------|--------------|\n| `Ours`    | 0.65 (0.51, 0.77) | 0.04 (−0.01, 0.09) | 0.86 (0.76, 0.96) | 0.02 (−0.02, 0.06) | 0.88 (0.79, 0.97) | 0.26 (0.14, 0.38) | 0.66 (0.53, 0.79) | 0.52 (0.38, 0.66) | 0.68 (0.55, 0.81) | 0.54 (0.40, 0.68) | 0.96 (0.91, 1.01) | 0.82 (0.71, 0.93) | 0.78 (0.73, 0.83) | 0.36 (0.31, 0.41) |\n| `Co-train` | 0.44 (0.30, 0.58) | 0.04 (−0.01, 0.09) | 0.76 (0.64, 0.88) | 0.00 (0.00, 0.00) | 0.90 (0.82, 0.98) | 0.14 (0.04, 0.24) | 0.54 (0.40, 0.68) | 0.34 (0.21, 0.47) | 0.66 (0.53, 0.79) | 0.46 (0.32, 0.60) | 0.98 (0.94, 1.02) | 0.72 (0.60, 0.84) | 0.71 (0.66, 0.76) | 0.28 (0.23, 0.33) |\n| `MMD`     | 0.38 (0.25, 0.51) | 0.00 (0.00, 0.00) | 0.18 (0.07, 0.29) | 0.04 (−0.01, 0.09) | 0.82 (0.71, 0.93) | 0.16 (0.06, 0.26) | 0.44 (0.30, 0.58) | 0.40 (0.26, 0.54) | 0.38 (0.25, 0.51) | 0.34 (0.21, 0.47) | 0.80 (0.69, 0.91) | 0.70 (0.57, 0.83) | 0.50 (0.44, 0.56) | 0.30 (0.24, 0.35) |\n| `T.-only`     | 0.30 (0.17, 0.43) | 0.00 (0.00, 0.00) | 0.20 (0.09, 0.31) | 0.00 (0.00, 0.00) | 0.82 (0.71, 0.93) | 0.00 (0.00, 0.00) | 0.42 (0.28, 0.56) | 0.00 (0.00, 0.00) | 0.48 (0.34, 0.62) | 0.00 (0.00, 0.00) | 0.64 (0.51, 0.77) | 0.00 (0.00, 0.00) | 0.48 (0.42, 0.54) | 0.00 (0.00, 0.00) |\n| `S.-only`     | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) |\n\n**Table R1:** Sim-to-sim success rates with confidence intervals for image-based policies.\n\n| method     | BoxInBin (V3) T | BoxInBin (V3) T‑O | BoxInBin \\(P\\) T | BoxInBin \\(P\\) T‑O | Lift (V1) T | Lift (V1) T‑O | Stack (V1) T | Stack (V1) T‑O | Square (V1) T | Square (V1) T‑O | MugHang (V1) T | MugHang (V1) T‑O | Average T | Average T‑O |\n|------------|---------------|---------------|--------------|--------------|--------------|--------------|---------------|---------------|---------------|---------------|----------------|----------------|---------------|---------------|\n| `Ours`     | 0.84 (0.74, 0.94) | 0.58 (0.42, 0.70) | 0.80 (0.69, 0.91) | 0.76 (0.64, 0.88) | 0.80 (0.69, 0.91) | 0.60 (0.46, 0.74) | 0.82 (0.71, 0.93) | 0.86 (0.76, 0.96) | 0.42 (0.28, 0.56) | 0.38 (0.25, 0.51) | 0.40 (0.26, 0.54) | 0.34 (0.21, 0.47) | 0.68 (0.63, 0.73) | 0.59 (0.53, 0.65) |\n| `Co-train`  | 0.76 (0.64, 0.88) | 0.52 (0.38, 0.66) | 0.70 (0.57, 0.83) | 0.66 (0.53, 0.79) | 0.92 (0.84, 1.00) | 0.48 (0.34, 0.62) | 0.86 (0.76, 0.96) | 0.72 (0.60, 0.84) | 0.24 (0.12, 0.36) | 0.24 (0.12, 0.36) | 0.26 (0.14, 0.38) | 0.22 (0.11, 0.33) | 0.62 (0.57, 0.67) | 0.47 (0.41, 0.53) |\n| `MMD`      | 0.50 (0.36, 0.64) | 0.38 (0.25, 0.51) | 0.66 (0.53, 0.79) | 0.50 (0.36, 0.64) | 0.56 (0.42, 0.70) | 0.52 (0.38, 0.66) | 0.70 (0.57, 0.83) | 0.66 (0.53, 0.79) | 0.18 (0.07, 0.29) | 0.12 (0.03, 0.21) | 0.18 (0.07, 0.29) | 0.20 (0.09, 0.31) | 0.46 (0.40, 0.52) | 0.40 (0.34, 0.46) |\n| `T.-only`      | 0.42 (0.28, 0.56) | 0.00 (0.00, 0.00) | 0.58 (0.42, 0.70) | 0.00 (0.00, 0.00) | 0.60 (0.46, 0.74) | 0.00 (0.00, 0.00) | 0.32 (0.19, 0.45) | 0.00 (0.00, 0.00) | 0.16 (0.06, 0.26) | 0.00 (0.00, 0.00) | 0.18 (0.07, 0.29) | 0.00 (0.00, 0.00) | 0.38 (0.33, 0.43) | 0.00 (0.00, 0.00) |\n| `S.-only`      | 0.08 (0.00, 0.16) | 0.10 (0.02, 0.18) | 0.52 (0.38, 0.66) | 0.60 (0.46, 0.74) | 0.32 (0.19, 0.45) | 0.40 (0.26, 0.54) | 0.52 (0.38, 0.66) | 0.64 (0.51, 0.77) | 0.10 (0.02, 0.18) | 0.08 (0.00, 0.16) | 0.12 (0.03, 0.21) | 0.10 (0.02, 0.18) | 0.28 (0.23, 0.33) | 0.32 (0.27, 0.37) |\n\n**Table R2:** Sim-to-sim success rates with confidence intervals for point cloud-based policies.\n\n- **Transferring transitions instead of state-action pairs:** Our proposed framework **does not assume a fixed temporal length for input observations**, so extending it to incorporate temporal information—such as full transitions—is straightforward. In this work, we adopt standard behavioral cloning (BC) architectures, namely Diffusion Policy[5] and 3D Diffusion Policy[6], as concrete instantiations to validate the core function of our framework. Exploring extensions that leverage transition-level alignment is a promising direction for future work.\n- **Inconsistent wording in hypotheses:** We will revise the wording of the hypotheses to align more closely with the conclusions presented in the experimental section.\n- **Missing/incorrect reference:** We will correct all reference issues related to tables and figures in the revision.\n- **Unclear/inappropriate wording:** We will carefully review and address any ambiguous or awkward phrasing in the revision.\n\n[1] What Matters in Learning from Offline Human Demonstrations for Robot Manipulation, CoRL'21;\n\n[2] LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning, NeurIPS'23\n\n[3] What Matters in Learning from Large-Scale Datasets for Robot Manipulation, ICLR'25;\n\n[4] Empirical Analysis of Sim-and-Real Cotraining Of Diffusion Policies For Planar Pushing from Pixels, IROS'25.\n\n[5] Diffusion Policy: Visuomotor Policy Learning via Action Diffusion, RSS'23;\n\n[6] 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations, RSS'24."}, {"review_id": "EUVaSehq0z", "reviewer": "Reviewer_aDmq", "summary": "This submission proposes an algorithm for sim and real co-training of robotic manipulation skills using Temporally Aligned Sampling and Unbalanced Optimal Transport. It is assumed that we have access to a large sim training dataset and a small real-world training dataset. The goal is to generalize to novel states in the real-world that are covered in the sim dataset but not in the real-world dataset. This is achieved by training an image or point cloud encoder with a regularization loss computed using optimal transport. It is shown that performing behavior cloning with this encoder leads to positive sim-to-real and sim-to-sim transfer and the learned policies generalize to states previously unseen in the target domain. The proposed method outperforms baselines that perform co-training on sim and real data and a simpler version of visual feature alignment between sim and real. The method is tested in a real-world robot experiment with a Franka arm.", "questions": "I do not fully understand the motivation in Section 4.3. It is stated that naively sampling pairs of transitions from the source and the target dataset “may yield pairs from different stages of tasks”. However, I thought that the optimal transport was computed over the entire episodes; wouldn’t the OT computation already take care of aligning different stages of the task across different trajectories?", "limitations": "The limitations of the method are addressed in the conclusion. In particular, the section mentions lack of sim to real dynamics alignment and limited dexterity of skills generated by MimicGen-style demonstration cloning.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n**Novel combination of known methods.** While sim and real co-training, unbalanced optimal transport and temporally aligned sampling are known methods, their combination to solve visual sim to real alignment that is done in this submission is novel. In particular, unbalanced optimal transport is useful because not every state in the source domain might have a corresponding state in the target domain. \n\n**Excellent video.** The video provided in the supplementary material clearly communicates the idea that we are moving from a wide reset distribution in sim to a narrow reset distribution in real-world demonstrations and then shows examples of real-world policy inference with an OOD reset distribution. I think this is a great showcase of the idea of learning joint sim and real embeddings using temporally-aligned optimal transport.\n\n**Evaluation with both images and point clouds.** The submission includes evaluation of diffusion policies with image encoders and 3D diffusion policies with point cloud encoders. Both evaluations highlight the usefulness of optimal transport in sim and real co-training. Using both images and point clouds increases the impact this paper could have in the robot learning community.\n\n**Evaluation of OOD transfer.** The experiments specifically test transfer to OOD states but in the sim and in the real. Showing positive transfer in this setting is a step towards learning generalist real-world manipulation policies.\n\n## Weaknesses\n\n**No strong real-world baseline / oracle.** The performance of all methods in the real-world robot experiments is relatively weak. For example, all methods are limited to a 40% success rate for the task of stacking two blocks. This is not entirely unexpected, as the methods are provided with only 25 real-world demonstrations and the distribution shifts between training and testing. This set of experiments is missing an oracle that provides an upper bound on the real-world robot performance. Since we do not know how well a diffusion policy would perform with a few hundred demonstrations, it is not clear if the low performance is due to a lack of data or other problems, such as incorrect controller gains or bugs in real-world data collection. Providing an oracle performance would better contextualize the real-world results.\n\n**Unclear if method could be reproduced from its description.** The method section contains frequent references to prior works for practical algorithms and an algorithmic description of the full method is delegated to Appendix C, Algorithm 1. Even Algorithm 1 lacks details needed to fully understand the computation done during training. I would suggest adding more details into Algorithm 1 and providing more guidance for reproducibility.\n\n**Unclear sensitivity to hyper-parameters.** Each training step includes temporal alignment computation and optimal transport computation. It is unclear how the hyper-parameters of these two steps are tuned and how sensitive the method is to small changes in these hyper-parameters. This could be clarified in an additional ablation study.", "was_revised": true, "final_justification": "The submission proposes a novel method for co-training on a large out-of-domain dataset and a small in-domain dataset. The paper contains strong experiments in both simulation and in the real world. Further, the authors performed an additional hyper-parameter sensitivity analysis for the rebuttal, which further increases my confidence in their method.", "rebuttal": "Thank you for your time and thoughtful feedback. We are grateful for the positive recognition of the novelty of our work, the high-quality accompanying video, and the thorough evaluations conducted across diverse modalities and domains, particularly under challenging out-of-distribution (OOD) conditions. Below, we address the raised concerns in detail:\n- **No strong real-world baseline/oracle:** We interpret the reviewer’s concern regarding the “40\\% success rate for the real-world Stack task” as referring to the results in Table 2, which correspond to the out-of-distribution (OOD) generalization setting—specifically, cube placements in novel poses not seen during training. **These OOD configurations are intentionally challenging and designed to evaluate a model’s ability to generalize beyond observed real-world data**.\n    To provide additional context, we also report in-distribution (ID) performance for all methods, where our approach achieves a 90\\% success rate (see Table 6 in the supplementary). This sharp contrast between ID and OOD performance **confirms the validity of our hardware setup and data collection process, while underscoring the core challenge of sim-to-real generalization under OOD conditions—the primary focus of our work**.\n    Regarding the absence of a strong real-world oracle, we emphasize that our work is intentionally situated in the low-data regime. This reflects the practical difficulty of collecting large numbers of real-world demonstrations across diverse OOD scenarios. For example, training a diffusion policy solely on real-world data with sufficient OOD coverage would typically require hundreds of demonstrations[1]—an expensive and often impractical requirement.\n    **Our setting is deliberately designed to reflect these real-world constraints**. Instead of relying on extensive real-world data, we explore how simulation data can be effectively leveraged—combined with limited real-world supervision—to bridge the sim-to-real gap under OOD target distributions.\n- **Reproducibility:** To enhance the reproducibility of our work, we plan to include code examples illustrating the implementation of the core components in the revised version. We also intend to release the codebase and dataset upon acceptance.\n- **Sensitivity to hyper-parameters:** We conducted an ablation study to evaluate the sensitivity of our method to key hyperparameters, including the entropy regularization coefficient ($\\epsilon$), the KL divergence penalty term ($\\tau$), and the window size used in temporally aligned sampling. In each experiment, we varied a single hyperparameter while keeping the others fixed, trained the policy, and assessed its performance via rollouts. We report results for the *BoxInBin* task under the *Viewpoint-Image* setting and the *Lift* task under the *Texture-Image* setting, as shown in the following tables (Table R1-R6). The results indicate that **our method is robust to hyperparameter variations** within reasonable ranges. Specifically, performance remains stable when $\\epsilon$ and $\\tau$ are set between 0.001 and 0.1, and when the window size is varied between 5 and 20. Our method consistently outperforms the co-training baseline in out-of-distribution (OOD) scenarios, where the baseline achieves a success rate of 0.14 on *BoxInBin* and 0.6 on *Lift*. These findings suggest that, although our approach introduces additional components, it does not require extensive tuning and offers clear advantages in terms of generalization.\n\n| $\\epsilon$ | 0.0001 | 0.001 | 0.01 | 0.04 |  0.1 |   1  |\n|:----------:|:------:|:-----:|:----:|:----:|:----:|:----:|\n|      T     |   0.9  |  0.94 | 0.92 | 0.88 |  0.9 | 0.88 |\n|     T-O    |  0.18  |  0.16 | 0.26 | 0.22 | 0.18 |  0.2 |\n\n**Table R1:** Success rate of our method on the *BoxInBin* task when varying $\\epsilon$.\n\n\n| $\\tau$ | 0.0001 | 0.001 | 0.005 | 0.02 | 0.04 |   1  |\n|:------:|:------:|:-----:|:-----:|:----:|:----:|:----:|\n|    T   |  0.88  |  0.96 |  0.94 | 0.94 | 0.92 | 0.94 |\n|   T-O  |  0.28  |  0.26 |  0.2  | 0.28 | 0.22 | 0.22 |\n\n**Table R2:** Success rate of our method on the *BoxInBin* task when varying $\\tau$.\n\n| winsize |    1 |    5 |   10 |   20 |   40 |  120 |\n|:-------:|-----:|-----:|-----:|-----:|-----:|-----:|\n|    T    | 0.82 | 0.92 | 0.86 |  0.9 | 0.94 | 0.84 |\n|   T-O   |  0.2 | 0.22 | 0.22 | 0.24 | 0.16 | 0.14 |\n\n**Table R3:** Success rate of our method on the *BoxInBin* task when varying the window size.\n\n| $\\epsilon$ | 0.0001 | 0.001 | 0.01 | 0.04 |  0.1 |    1 |\n|:----------:|-------:|------:|-----:|-----:|-----:|-----:|\n|      T     |   0.84 |  0.88 |  0.8 | 0.76 | 0.78 | 0.76 |\n|     T-O    |    0.6 |  0.74 | 0.62 | 0.66 | 0.68 | 0.54 |\n\n**Table R4:** Success rate of our method on the *Lift* task when varying $\\epsilon$.\n\n| $\\tau$ | 0.0001 | 0.005 | 0.02 | 0.04 |  0.1 |    1 |\n|:------:|-------:|------:|-----:|-----:|-----:|-----:|\n|    T   |   0.78 |   0.7 | 0.76 | 0.76 | 0.78 | 0.74 |\n|   T-O  |   0.56 |  0.67 | 0.64 | 0.66 | 0.62 | 0.66 |\n\n**Table R5:** Success rate of our method on the *Lift* task when varying $\\tau$.\n\n| winsize |    1 |   5 |   10 |   20 |   40 |  120 |\n|:-------:|-----:|----:|-----:|-----:|-----:|-----:|\n|    T    | 0.86 | 0.8 |  0.7 | 0.78 | 0.74 | 0.82 |\n|   T-O   | 0.66 | 0.6 | 0.67 | 0.58 | 0.56 |  0.6 |\n\n**Table R6:** Success rate of our method on the *Lift* task when varying the window size.\n\n- **The importance of sampling strategy:** Although Optimal Transport (OT) can theoretically align samples across different task stages when full episodes are used, this approach is often impractical due to computation constraints. In mini-batch training with limited batch sizes, especially for multi-stage or long-horizon tasks, **semantically aligned samples may rarely co-occur within the same batch. Our sampling strategy addresses this by increasing the likelihood of including aligned pairs, thereby improving alignment quality and accelerating convergence**. As demonstrated in Figure 9, this approach leads to significant improvements in policy performance.\n\n[1] DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset, RSS'24."}, {"review_id": "iHATedLoqi", "reviewer": "Reviewer_4KCC", "summary": "This paper proposes a unified sim-and-real co-training framework for learning generalizable policies for robotic manipulation. It aims to address the challenges of costly real-world robot demonstration data acquisition and the sim-to-real domain gap by leveraging both a small number of real-world demonstrations and abundant simulated data. A key contribution is the proposal of an Optimal Transport (OT)-based loss function to align the joint distributions of observations and actions across domains, extended to an Unbalanced Optimal Transport (UOT) framework to handle imbalanced data. Furthermore, a temporally-aware sampling strategy is introduced to enhance the efficiency of mini-batch OT training. Through experiments on various robotic manipulation tasks, the proposed method intends to demonstrate improved real-world success rates and superior generalization capabilities, even for scenarios learned exclusively in simulation.", "questions": "1. Optimal Transport and Unbalanced Optimal Transport are well-known for domain adaptation. Please clearly explain what new insights or contributions, methodologically or theoretically, this paper offers compared to existing OT/UOT-based domain adaptation methodologies. Additional theoretical analysis is needed to demonstrate that this is not merely a combination of existing techniques.\n2. To solidify the superiority of the proposed methodology, please conduct comparative experiments against at least three more recent and relevant sim-to-real or domain adaptation techniques. Furthermore, quantitatively analyze the method's robustness to real-world uncertainties (e.g., varying lighting conditions, object deformations, robot pose errors).\n3. The paper claims to require \"only a few real-world demonstrations.\" Please provide a detailed analysis of the minimum threshold for \"a few real-world data\" used, and how the diversity of this data (e.g., different angles, lighting, object instances) influences the results. Can you clearly demonstrate the method's effectiveness even with extremely limited real-world data (e.g., fewer than 5 demonstrations)?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### Strengths\n\n- Addressing the sim-to-real transfer problem in robot learning remains a significant challenge, and the attempt to overcome the constraint of limited real-world data is relevant.\n- The conceptual idea of applying Optimal Transport (OT) to align joint observation-action distributions, utilizing Unbalanced Optimal Transport (UOT) to manage data imbalance, and introducing a temporally aware sampling strategy is interesting.\n\n### Weaknesses\n\n- The proposed methodology appears to be primarily a combination of existing research on Optimal Transport and Unbalanced Optimal Transport for domain adaptation. There is a lack of clear explanation and sufficient theoretical justification for how this combination offers fundamental new theoretical or methodological contributions beyond existing methods. The temporally aware sampling appears to be an auxiliary technique for solving a specific problem rather than contributing core originality.\n- While the presented experimental results show performance improvements in specific tasks, they lack rigor in several critical aspects.\n    - Comparisons to other state-of-the-art and more robust sim-to-real or domain adaptation techniques are very limited. The current baselines are insufficient to adequately prove the superiority of the proposed method, which could lead to an overstatement of the paper's contributions.\n    - While the paper emphasizes \"a few real-world demonstrations,\" there is insufficient detailed information about the scale, diversity, and complexity of the actual datasets used. The OOD generalization results might be attributable to the specific dataset characteristics or the simplicity of the simulation environment rather than the intrinsic strength of the methodology. Validation across a wider range of domain gap scenarios is necessary.\n- Unbalanced OT and the temporally-aware sampling can involve complex mathematical backgrounds, and their implementation and tuning in real systems are expected to be considerably complex. There is insufficient discussion on whether this added complexity sufficiently justifies the observed performance gains, and what potential issues might arise during real-world deployment.", "was_revised": true, "final_justification": "The additional experiments and clarifications have addressed my initial concerns.", "rebuttal": "Thank you for your time and constructive feedback. We appreciate the positive recognition of our work in proposing a conceptually compelling framework for addressing challenging real-world robotics problems, particularly under the practical constraint of limited real-world demonstrations—a setting that is often overlooked in prior work. Below, we address the raised concerns in detail:\n- **Theoretical or methodological contributions:** We thank the reviewer for their feedback. **Our work addresses a critical emerging challenge in robot learning: how to achieve real-world generalization when training primarily on simulated data with only sparse real demonstrations.** While Optimal Transport (OT) has been used in domain adaptation, existing methods fail to handle the unique requirements of policy co-training—particularly the need to preserve action-relevant features across domains while coping with severe data imbalance ($N_{src}$ >> $N_{tgt}$) and partial state overlap. **Our key innovations include: (1) joint alignment of observations and actions (rather than marginal features alone), (2) an Unbalanced OT formulation to handle domain mismatch, and (3) temporally-aware sampling for sequential data—all specifically designed for the sim-to-real policy setting**.\n    The problem setting itself represents an important and understudied challenge. As large-scale simulation systems like MimicGen[1] enable massive synthetic datasets, the fundamental question becomes how to effectively leverage this data for real-world generalization, especially for states unseen in real demonstrations. Our framework provides the first systematic solution, achieving +30\\% success rates on out-of-distribution real-world tasks compared to standard co-training (Table 2 and Table 4). This demonstrates that our methodological adaptations are not just applications of OT, but necessary innovations to bridge simulation and reality for policy learning. \n    We highlight the growing paradigm of scaling imitation learning by combining simulated and real-world data[2, 3]. Recent advances in robot data synthesis[1, 4, 5] enable controllable, large-scale simulation. **Our main contribution is to formalize and address the overlooked challenge of out-of-distribution (OOD) generalization in the target domain**. We propose a novel OT-based framework that addresses data imbalance and mini-batch limitations while retaining theoretical guarantees, and validate it through real-world experiments under practical constraints—often neglected in prior work.\n- **Complexity in implementation and tuning:** We thank the reviewer for pointing out the importance of evaluating the trade-off between algorithmic complexity and practical benefit. To address this concern, we conducted an additional ablation study to analyze the sensitivity of our method to its key hyperparameters, including the entropy regularization coefficient ($\\epsilon$), the KL divergence penalty term ($\\tau$), and the window size used in temporally aligned sampling. In each experiment, we varied a single hyperparameter while holding others fixed, trained the policy, and assessed performance via rollout evaluations. We report results for the *BoxInBin* task under the *Viewpoint-Image* setting and the *Lift* task under the *Texture-Image* setting, as shown in the following tables (Table R1-R6). The results show that **our method is robust to a range of reasonable hyperparameter choices** and does not require fine-grained tuning to achieve strong performance. Regarding the implementation complexity, while UOT and temporally-aware sampling do introduce some algorithmic overhead, **both components are modular and can be easily integrated into standard BC pipelines**. To further support reproducibility, we will release the codebase upon acceptance.\n\n| $\\epsilon$ | 0.0001 | 0.001 | 0.01 | 0.04 |  0.1 |   1  |\n|:----------:|:------:|:-----:|:----:|:----:|:----:|:----:|\n|      T     |   0.9  |  0.94 | 0.92 | 0.88 |  0.9 | 0.88 |\n|     T-O    |  0.18  |  0.16 | 0.26 | 0.22 | 0.18 |  0.2 |\n\n**Table R1:** Success rate of our method on the *BoxInBin* task when varying $\\epsilon$.\n\n| $\\tau$ | 0.0001 | 0.001 | 0.005 | 0.02 | 0.04 |   1  |\n|:------:|:------:|:-----:|:-----:|:----:|:----:|:----:|\n|    T   |  0.88  |  0.96 |  0.94 | 0.94 | 0.92 | 0.94 |\n|   T-O  |  0.28  |  0.26 |  0.2  | 0.28 | 0.22 | 0.22 |\n\n**Table R2:** Success rate of our method on the *BoxInBin* task when varying $\\tau$.\n\n| winsize |    1 |    5 |   10 |   20 |   40 |  120 |\n|:-------:|-----:|-----:|-----:|-----:|-----:|-----:|\n|    T    | 0.82 | 0.92 | 0.86 |  0.9 | 0.94 | 0.84 |\n|   T-O   |  0.2 | 0.22 | 0.22 | 0.24 | 0.16 | 0.14 |\n\n**Table R3:** Success rate of our method on the *BoxInBin* task when varying the window size.\n\n| $\\epsilon$ | 0.0001 | 0.001 | 0.01 | 0.04 |  0.1 |    1 |\n|:----------:|-------:|------:|-----:|-----:|-----:|-----:|\n|      T     |   0.84 |  0.88 |  0.8 | 0.76 | 0.78 | 0.76 |\n|     T-O    |    0.6 |  0.74 | 0.62 | 0.66 | 0.68 | 0.54 |\n\n**Table R4:** Success rate of our method on the *Lift* task when varying $\\epsilon$.\n\n| $\\tau$ | 0.0001 | 0.005 | 0.02 | 0.04 |  0.1 |    1 |\n|:------:|-------:|------:|-----:|-----:|-----:|-----:|\n|    T   |   0.78 |   0.7 | 0.76 | 0.76 | 0.78 | 0.74 |\n|   T-O  |   0.56 |  0.67 | 0.64 | 0.66 | 0.62 | 0.66 |\n\n**Table R5:** Success rate of our method on the *Lift* task when varying $\\tau$.\n\n| winsize |    1 |   5 |   10 |   20 |   40 |  120 |\n|:-------:|-----:|----:|-----:|-----:|-----:|-----:|\n|    T    | 0.86 | 0.8 |  0.7 | 0.78 | 0.74 | 0.82 |\n|   T-O   | 0.66 | 0.6 | 0.67 | 0.58 | 0.56 |  0.6 |\n\n**Table R6:** Success rate of our method on the *Lift* task when varying the window size.\n- **Effectiveness with extremely limited target domain data:** We argue that in real-world robotic settings, **10–25 demonstrations constitute a highly limited amount of data—particularly when compared to prior works such as [6], which often require more than 100 demonstrations**. This limitation is further compounded in our evaluation, which involves diverse object poses, textures, shapes, and extended temporal horizons. To address the reviewer’s concern, we provide a comparative analysis of different methods under this low-data regime in the target domain, specifically for the *BoxInBin* task with the *Viewpoint3-Point* setting. The results show that our approach consistently outperforms the baselines, **demonstrating its robustness even with extremely limited supervision**.\n\n|           |     | Ours (CI)       | Co-train (CI)      | MMD (CI)        | T.-only (CI)    |\n|-----------|-----|-----------------|-----------------|-----------------|------------------|\n| **1 Demo**| T   | 0.56 (0.42, 0.70) | 0.46 (0.32, 0.60) | 0.42 (0.28, 0.56) | 0.00 (0.00, 0.00) |\n| **1 Demo**          | T-O | 0.28 (0.16, 0.40) | 0.00 (0.00, 0.00) | 0.16 (0.06, 0.26) | 0.00 (0.00, 0.00) |\n| **5 Demo**| T   | 0.70 (0.57, 0.83) | 0.38 (0.25, 0.51) | 0.34 (0.21, 0.47) | 0.46 (0.32, 0.60) |\n| **5 Demo**  | T-O | 0.32 (0.19, 0.45) | 0.22 (0.11, 0.33) | 0.22 (0.11, 0.33) | 0.00 (0.00, 0.00) |\n\n**Table R7:** Success rates and confidence intervals for various methods on the *BoxInBin* task with the *Viewpoint3-Point* setting, under scenarios where data from the target domain is extremely limited.\n\n- **More recent and relevant sim-to-real baselines:** Our problem setting specifically focuses on **sim-to-real transfer under out-of-distribution (OOD) conditions** in the real world, with **limited access to real-world demonstrations** and **no prior knowledge of the target distribution**. While recent methods in robotic sim-to-real transfer have shown progress[2, 3, 7, 8, 9], **most do not directly tackle this challenging OOD generalization scenario**. For example, some approaches require human-in-the-loop interventions for online data collection after deployment[8], others rely on computationally expensive reinforcement learning pipelines[7], or adopt domain randomization[9], which assumes knowledge of the target domain distribution and may not generalize well when the test-time conditions fall far outside the training distribution. In contrast, our setting addresses scenarios where the target domain is not fully known a priori, and the task objective may vary or fall outside the training distribution, making traditional sim-to-real pipelines less suitable.\n    Among these works, co-training approaches[2, 3]—which train policies using a mixture of simulation and real-world data—are most aligned with our problem setting and have demonstrated strong performance. Therefore, we focus on these methods as baselines, as they are the **most relevant to our formulation and constraints, ensuring meaningful and fair comparisons**. We appreciate the reviewer for highlighting additional related works.\n- **Detailed information on real-world datasets:** We provide details of the real-world dataset in Section B.3 of the supplementary material. All baselines and our method are trained on the same dataset, ensuring a fair and consistent comparison.\n\n[1] Mimicgen: A data generation system for scalable robot learning using human demonstrations, CoRL'23;\n\n[2] Empirical Analysis of Sim-and-Real Cotraining Of Diffusion Policies For Planar Pushing from Pixels, IROS'25;\n\n[3] Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, RSS'25;\n\n[4] Dexmimicgen: Automated data generation for bimanual dexterous manipulation via imitation learning, ICRA'25; \n\n[5] DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning, RSS'25; \n\n[6] Natural Language Can Help Bridge the Sim2Real Gap, RSS'24; \n\n[7] From Imitation to Refinement -- Residual RL for Precise Assembly, arXiv'24; \n\n[8] TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction, CoRL'24; \n\n[9] DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality, ICRA'23."}, {"review_id": "65hULVde0T", "reviewer": "Reviewer_PAX9", "summary": "The paper proposes a unified co-training framework that trains a single policy on both simulation and real-world demonstrations. The core innovation is the use of an Unbalanced Optimal Transport (UOT) loss to align joint distributions of observations and actions (or proprioceptive states) across domains in a shared latent space, rather than aligning marginal distributions. This action-aware alignment, augmented with temporally aligned sampling, preserves task-relevant features and handles domain imbalance. \n\nThe authors evaluate across six manipulation tasks using both RGB and point cloud modalities. They compare against Source-only, Target-only, MMD-based, and naive co-training baselines. Metrics include task success rates in standard and out-of-distribution (OOD) scenarios in both simulated and real environments. Key tools include MimicGen for simulation data and Robosuite for simulation environments.", "questions": "1. How sensitive is the performance to the choice of UOT hyperparameters (e.g., entropy regularization, KL penalties)?\n2. Could the approach generalize to tasks with significant dynamics gaps, such as high-speed or contact-rich manipulation? It seems all evaluated tasks are transferred kinematically. \n3. How does the performance scale with even fewer real-world demonstrations (e.g., <5 per task)?\n4. Would alternative domain alignment methods (e.g., contrastive learning or adversarial domain alignment [2, 3]) offer competitive or complementary benefits?\n\n[2] Raychaudhuri, Dripta S., et al. \"Cross-domain imitation from observations.\" International conference on machine learning. PMLR, 2021.\n\n[3] Kim, Kuno, et al. \"Domain adaptive imitation learning.\" International Conference on Machine Learning. PMLR, 2020.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n- **Use of UOT:** The integration of UOT into a co-training framework for sim-to-real transfer is methodologically sound and innovative. OT provides a structured and geometry-aware way to align feature-action distributions, which is superior to marginal feature alignment techniques like MMD. The author should cite [1] since it is very relevant on latent-space alignment with UOT without the marginal constraints.\n- **Data Imbalance Handling:** UOT’s ability to handle domain imbalance is crucial in practical sim-to-real settings where simulated data vastly outnumbers real-world examples. This mitigates overfitting to the dominant domain and avoids spurious alignments. \n- **Improved OOD Generalization:** The method shows consistent performance gains in out-of-distribution (OOD) settings, highlighting its strength in extrapolating from simulated experiences to unseen real-world scenarios. Real-world experiments with the Franka Panda robot validate that the method is not only simulation-effective but deployment-ready for physical systems.\n\n**Weaknesses**\n\n- **Limited Addressing of Dynamics Gap:** The method only tackles the visual observation gap and assumes quasi-static, prehensile tasks. It does not address the sim-to-real dynamics mismatch (e.g., surface friction of push block), which can be substantial in more dynamic or contact-rich scenarios.\n- **Dependence on Demonstration Alignment:** The method assumes the overhead of behavioral similarity between simulated and real demonstrations, enforced via DTW. This may not hold in scenarios where real demonstrations deviate significantly in strategy or execution from simulated ones. Moreover, the approach relies on action or proprioceptive similarity across domains. This limits its application to settings where such consistency is hard to guarantee (e.g., different control schemes or hardware platforms).\n- **Limited Theoretical Development:** The paper does not present theoretical guarantees or convergence analysis for the proposed UOT-guided co-training. While empirical results are strong, formal properties are not fully explored.\n- **Hyperparameter Sensitivity Not Explored:** The paper does not provide an ablation or sensitivity analysis on key UOT parameters (e.g., entropy regularization, KL divergence penalties), which may influence performance stability.\n\n[1] Nguyen, Duy Minh Ho, et al. \"Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model.\" Asian Conference on Machine Learning. PMLR, 2025.", "was_revised": true, "final_justification": "I have read the long rebuttal of the authors, which satisfactorily addressed my questions and concerns on sensitivity analysis and further scalability issues. Hence, I am leaning towards accepting this paper.", "rebuttal": "Thank you for your time and thoughtful feedback. We appreciate the positive recognition of our integrated co-training framework for sim-to-real transfer, which is both methodologically sound and innovative. We are also grateful for the acknowledgment of its ability to handle imbalanced data distributions across domains—a critical challenge in practical real-world applications—and its consistent performance gains in challenging evaluation scenarios, including real-world experiments, a setting often underexplored in prior work. Below, we address the raised concerns in detail:\n\n- **Sensitivity to hyper-parameters:** We conducted an ablation study to evaluate the sensitivity of our method to key hyperparameters, including the entropy regularization coefficient ($\\epsilon$), the KL divergence penalty term ($\\tau$), and the window size used in temporally aligned sampling. In each experiment, we varied a single hyperparameter while keeping the others fixed, trained the policy, and assessed its performance via rollout evaluations. We report results for the *BoxInBin* task under the *Viewpoint-Image* setting and the *Lift* task under the *Texture-Image* setting, as shown in the following tables (Table R1-R6). The results indicate that **our method is robust to hyperparameter variations** within reasonable ranges. Specifically, performance remains stable when $\\epsilon$ and $\\tau$ are set between 0.001 and 0.1, and when the window size is varied between 5 and 20. Our method consistently outperforms the co-training baseline in out-of-distribution (OOD) scenarios, where the baseline achieves a success rate of 0.14 on *BoxInBin* and 0.6 on *Lift*. These findings suggest that, although our approach introduces additional components, it does not require extensive tuning and offers clear advantages in terms of generalization.\n\n $\\epsilon$ | 0.0001 | 0.001 | 0.01 | 0.04 |  0.1 |   1  |\n|:----------:|:------:|:-----:|:----:|:----:|:----:|:----:|\n|      T     |   0.9  |  0.94 | 0.92 | 0.88 |  0.9 | 0.88 |\n|     T-O    |  0.18  |  0.16 | 0.26 | 0.22 | 0.18 |  0.2 |\n\n**Table R1:** Success rate of our method on the *BoxInBin* task when varying $\\epsilon$.\n\n\n| $\\tau$ | 0.0001 | 0.001 | 0.005 | 0.02 | 0.04 |   1  |\n|:------:|:------:|:-----:|:-----:|:----:|:----:|:----:|\n|    T   |  0.88  |  0.96 |  0.94 | 0.94 | 0.92 | 0.94 |\n|   T-O  |  0.28  |  0.26 |  0.2  | 0.28 | 0.22 | 0.22 |\n\n**Table R2:** Success rate of our method on the *BoxInBin* task when varying $\\tau$.\n\n| winsize |    1 |    5 |   10 |   20 |   40 |  120 |\n|:-------:|-----:|-----:|-----:|-----:|-----:|-----:|\n|    T    | 0.82 | 0.92 | 0.86 |  0.9 | 0.94 | 0.84 |\n|   T-O   |  0.2 | 0.22 | 0.22 | 0.24 | 0.16 | 0.14 |\n\n**Table R3:** Success rate of our method on the *BoxInBin* task when varying the window size.\n\n| $\\epsilon$ | 0.0001 | 0.001 | 0.01 | 0.04 |  0.1 |    1 |\n|:----------:|-------:|------:|-----:|-----:|-----:|-----:|\n|      T     |   0.84 |  0.88 |  0.8 | 0.76 | 0.78 | 0.76 |\n|     T-O    |    0.6 |  0.74 | 0.62 | 0.66 | 0.68 | 0.54 |\n\n**Table R4:** Success rate of our method on the *Lift* task when varying $\\epsilon$.\n\n| $\\tau$ | 0.0001 | 0.005 | 0.02 | 0.04 |  0.1 |    1 |\n|:------:|-------:|------:|-----:|-----:|-----:|-----:|\n|    T   |   0.78 |   0.7 | 0.76 | 0.76 | 0.78 | 0.74 |\n|   T-O  |   0.56 |  0.67 | 0.64 | 0.66 | 0.62 | 0.66 |\n\n**Table R5:** Success rate of our method on the *Lift* task when varying $\\tau$.\n\n| winsize |    1 |   5 |   10 |   20 |   40 |  120 |\n|:-------:|-----:|----:|-----:|-----:|-----:|-----:|\n|    T    | 0.86 | 0.8 |  0.7 | 0.78 | 0.74 | 0.82 |\n|   T-O   | 0.66 | 0.6 | 0.67 | 0.58 | 0.56 |  0.6 |\n\n**Table R6:** Success rate of our method on the *Lift* task when varying the window size.\n\n- **Effectiveness with extremely limited target domain data:** We argue that in real-world robotic settings, **10–25 demonstrations constitute a highly limited amount of data—particularly when compared to prior works such as [1], which often require more than 100 demonstrations**. This limitation is further compounded in our evaluation, which involves diverse object poses, textures, shapes, and extended temporal horizons. To address the reviewer’s concern, we provide a comparative analysis of different methods under this low-data regime in the target domain, specifically for the *BoxInBin* task with the *Viewpoint3-Point* setting. The results show that our approach consistently outperforms the baselines, **demonstrating its robustness even with extremely limited supervision**.\n\n|           |     | Ours (CI)       | Co-train (CI)      | MMD (CI)        | T.-only (CI)    |\n|-----------|-----|-----------------|-----------------|-----------------|------------------|\n| **1 Demo**| T   | 0.56 (0.42, 0.70) | 0.46 (0.32, 0.60) | 0.42 (0.28, 0.56) | 0.00 (0.00, 0.00) |\n| **1 Demo**          | T-O | 0.28 (0.16, 0.40) | 0.00 (0.00, 0.00) | 0.16 (0.06, 0.26) | 0.00 (0.00, 0.00) |\n| **5 Demo**| T   | 0.70 (0.57, 0.83) | 0.38 (0.25, 0.51) | 0.34 (0.21, 0.47) | 0.46 (0.32, 0.60) |\n| **5 Demo**  | T-O | 0.32 (0.19, 0.45) | 0.22 (0.11, 0.33) | 0.22 (0.11, 0.33) | 0.00 (0.00, 0.00) |\n\n**Table R7:** Success rates and confidence intervals for various methods on the *BoxInBin* task with the *Viewpoint3-Point* setting, under scenarios where data from the target domain is extremely limited.\n\n- **Generalize to tasks with significant dynamics gaps:** As stated in Line 119-121, our work focuses on quasi-static tasks, which are representative of many everyday scenarios in home environments. In these settings, the observation gap is the primary bottleneck for sim-to-real transfer and presents a substantial challenge on its own. While handling dynamics mismatches is not the central focus of this work, **our method is built on a co-training framework, which inherently provides some robustness to dynamics gap**. Recent works[2, 3] have also demonstrated that co-training strategies can mitigate the effects of dynamics gaps and handle certain dynamic tasks. We acknowledge this as a limitation of the current study and view extending our framework to more dynamic tasks as a valuable direction for future research.\n- **Alternative domain alignment methods:** Our problem setting specifically targets sim-to-real transfer under out-of-distribution (OOD) conditions in the real world, with limited real-world demonstrations. The domain alignment methods mentioned by the reviewer[4, 5] primarily rely on adversarial training to learn a shared latent space for the transitions, but they **do not account for the significant data imbalance between the source (simulation) and target (real-world) domains—a core challenge in our setting**. While these approaches are relevant in broader domain adaptation contexts, they would require significant effort to be adapted to the problem formulation in this paper. We will cite and discuss these aspects in the related work section of the revised manuscript.\n- **Missing citations:** We will cite and discuss these works[4, 5, 6] in the revision.\n- **Dependence on demonstration alignment:** We would like to clarify the notion of \"alignment\" as used in our work. **Our method assumes alignment only at the level of task semantics**—that is, demonstrations across different domains are expected to operate on the same object geometry and pursue the same task objective. We **do not assume identical execution rates or strict temporal synchronization between trajectories**. To account for temporal variability, our framework incorporates a temporally aligned sampling strategy during training. \n    Comparable or even more restrictive assumptions regarding semantic alignment have been adopted in prior work. For example, Raychaudhuri et al.[4] referenced by the reviewer assumes that linearly normalized temporal positions correspond to the same task semantics—an assumption that may not hold in real-world robotics scenarios, where execution speeds can vary significantly across domains and task stages.\n    We contend that the semantic alignment assumption required by our method is **practical and readily achievable**, particularly given recent advances in perception systems[7], digital twin technologies[8, 9], and motion synthesis tools[10, 11] that enable the generation of diverse and semantically consistent demonstrations from a small number of source demonstrations.\n\n[1] Natural Language Can Help Bridge the Sim2Real Gap, RSS'24;\n\n[2] Empirical Analysis of Sim-and-Real Cotraining Of Diffusion Policies For Planar Pushing from Pixels, IROS'25;\n\n[3] Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, RSS'25;\n\n[4] Cross-domain Imitation from Observations, ICML'21;\n\n[5] Domain Adaptive Imitation Learning, ICML'20.\n\n[6] Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model, ACML'25\n\n[7] FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects, CVPR'24;\n\n[8] Automated Creation of Digital Cousins for Robust Policy Learning, CoRL'24;\n\n[9] Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation, RSS'24;\n\n[10] Mimicgen: A data generation system for scalable robot learning using human demonstrations, CoRL'23;\n\n[11] DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning, RSS'25."}]

Author rebuttal:

Meta-review: First concern is lack of statistical analysis; response here seems to address policy evaluation; inspired by theory but loses that in the approach; all reviewers positive

This paper studies the problem of using simulators to provide data for behavior cloning. One critical challenge is the sim2real visual gap and the work addresses this by learning a shared feature space between sim and real. Experiments show the approach enables sim2real transfer of manipulation policies.

Reviewers generally liked the papers motivation, clear contribution, and experimental design. A number of points about the writing were highly praised. The aim to use simulation to provide demonstration data is well-motivated and the approach is sound and novel.

One of the main concerns was a lack of statistical analysis given in the paper. This was clearly addressed in the rebuttal. Another limitation is that the paper only addresses visual sim2real gap and not dynamics sim2real gap. This limitation should be clearly acknowledged in the paper.
Decision: Accept (poster)