Differentiable Robot Rendering

Authors: ruoshi  liu
alper  canberk
shuran  song
carl  vondrick
Venue: robot-learning_org_CoRL_2024_Conference
Published: 2024-05-24 08:01:22

Abstract: Vision foundation models trained on massive amounts of visual data have shown unprecedented reasoning and planning skills in open-world settings. A key challenge in applying them to robotic tasks is the modality gap between visual data and action data. We introduce differentiable robot rendering, a method allowing the visual appearance of a robot body to be directly differentiable with respect to its control parameters. Our model integrates a kinematics-aware deformable model and Gaussians Splatting and is compatible with any robot form factors and degrees of freedom. We demonstrate its capability and usage in applications including reconstruction of robot poses from images and controlling robots through vision language models. Quantitative and qualitative results show that our differentiable rendering model provides effective gradients for robotic control directly from pixels, setting the foundation for the future applications of vision foundation models in robotics.

Main paper: 
 introduction: 
 How to represent a robot? Classically, the appearance of a robot is modeled by a set of geometric primitives (e.g. triangle meshes), and the deformation of its morphology is modeled by its kinematic structure. In this paper, we introduce Dr. Robot, a representation of robot self-embodiment based on Gaussians Splatting that is fully differentiable from its visual appearance to its control parameters.The differentiability of a robot's self-model [1, 2, 3] is crucial. A differentiable representation allows us to pass the control signal from pixel space to control space through back-propagation. Having a differentiable self-model of the robot allows us to optimize for visual reward and constraints with gradients instead of resorting to gradient-free optimization methods such as evolutionary algorithms or reinforcement learning. This is particularly relevant today, given the growing evidence [4, 5, 6] that visual foundation models can provide robust and generalizable signals for control.To obtain high-quality gradients, Dr. Robot tightly integrates three key components:• Gaussians Splatting. The appearance of a robot in its canonical pose can be modeled by Gaussians Splatting, which models the robot's geometry and texture. Given a viewpoint, an image can be rendered differently through a differentiable rasterizer.• Implicit Linear Blend Skinning. We adapt the traditional Linear Blend Skinning (LBS) technique in graphics to work with Gaussian splitting. Combining with differentiable forward kinematics, the implicit LBS model can project the positions of 3D Gaussians to target positions conditioned on a target pose.• Pose-Conditioned Appearance Deformation. To model the change of appearance of a robot under various poses, we use a pose-conditioned appearance deformation model to change the spherical harmonics, scale, opacity, covariance matrix of the 3D Gaussians conditioned on a pose.We validate the effectiveness of the gradients produced by our model by performing robot pose reconstruction from in-the-wild videos. Our optimization-based method outperformed the previous state-of-the-art method by a large margin. We also showcase various potential applications of Dr. Robot in several tasks, as shown in Fig. 1 . By connecting Dr. Robot to various visual foundation models, we can perform planning and control of robot actions through optimization.The primary contribution of this paper is a differentiable robot rendering method for vision-based robot learning. Our experiments demonstrate that our method provides a tool for solving complex robotic tasks through visual foundation models. We believe that as visual models, such as textto-image and text-to-video models, continue to grow in spatial and physical reasoning ability, Dr. Robot will serve as the bridge between these foundation models and robotic control. 
overview and formulation: 
 Given a robot pose p, we aim to model the visual appearance of the robot I from an arbitrary camera perspective as a differentiable function f ,EQUATIONwhere π denotes the image formation process under a pinhole camera model, which is a standard differentiable process. In the rest of this section, we will discuss how we formulate f as a differentiable function. A differentiable rendering model for robots needs to have three key properties: full-body differentiability, deformability, and efficiency in rendering. We propose to model these three properties with forward kinematics (FK), linear blend skinning (LBS), and Gaussians Splatting (GS), respectively, as depicted in Fig. 2 .Forward Kinematics. Given a pose p, we represent the position and orientation of joint j with respect to the base joint 0 as the homogeneous matrix T j (p), which is the product of rigid transformations from parent joints along the kinematic chain:EQUATIONwhere A i denotes the coordinate frame of joint i with respect to joint i -1. Forward kinematics projects a pose vector into a skeleton, Implicit LBS projects 3D Gaussians to the robot surface, and Appearance Deformation adjusts appearance of 3D Gaussians.Canonical Gausssians Splatting. A robot in its canonical pose can be treated as a static 3D scene, which can be modeled by Gaussians Splatting (GS) [7] . Following the notation convention of the original paper [7] , the canonical pose robot can be represented as a set of 3D Gaussians G with a set of means at {µ k } where each Gaussian can be expressed as:EQUATIONwhere RSS T R T denotes the full covariance matrix, R denotes the rotation matrix, and S denotes the scaling matrix. In addition, a 27-dimensional spherical harmonics coefficients c shs and opacity o are used to represent the apperance of the Gaussian during rendering. During optimization, after every N number of optimization steps, each Gaussian can split into two to densify the volume, and Gaussians with low opacity are truncated to reduce memory consumption.Implicit Linear Blend Skinning (LBS). Canonical 3D Gaussian splatting can model the robot in a static pose, but we would like to render the robot at an arbitrary pose p. Since optimizing a separate canonical 3D Gaussian for every pose is unrealistic, we need to construct a geometric deformation function to relocate the Gaussians in 3D space given a pose. Following SMPL [8] , we learn a linear blend skinning (LBS) for modeling geometric deformations. Classical LBS expresses the transformation of each vertex on a mesh as a linear combination of joint transformations.Different from [8] , the splitting and truncation mechanism of 3D Gaussians during optimization makes it incompatible with the classical LBS, which assumes the input to be a fixed set of vertices. We therefore propose implicit LBS, which accepts an arbitrary 3D coordinate, and outputs a set of weights representing the influence of each of the J joint transforms. Specifically, let W (µ) : R 3 → R J be the implicit LBS function, and µ be the position of one of the 3D Gaussians in canonical GS, the geometric deformation function D can be expressed as:EQUATIONAppearance Deformation. So far, we've constructed an implicit LBS function to relocate 3D Gaussians according to robot poses, but a 3D Gaussian also contains appearance and shape information in addition to its 3D position, namely the rotation matrix R k , scaling matrix S k , and spherical harmonics coefficients c shs,k , and opacity o k . Therefore, we additionally learn another appearance deformation function X to predict the change of these parameters conditioned on the canonical and projected positions of 3D Gaussians:EQUATIONSimilar to the implicit LBS function, the appearance deformation function is also implicit, allowing them to share the same MLP architecture except for the input and output dimensions.Optimization. Putting everything together, given the canonical GS G and a robot pose p, the final posed GS G p can be expressed as: Here, we showcase the learned robot model's visual quality by comparing it with results obtained from Deformable Gaussians [9] . Due to the complicated kinematic structure of a robot, [9] is unable to fit the deformation while ours can.EQUATIONGiven a camera viewpoint π and its corresponding visual observation I, we minimize the meansquared error (MSE) between the prediction and observation to learn the full robot model:EQUATIONTest-Time Optimization At test time, we can perform planning by optimizing robot actions through image gradients for various objectives, such as visual rewards calculated from visual foundation models. Please refer to Eq. 8, 9, 10 for examples of these optimization objectives.3 Experiments 
self model quality: 
 A robot self model needs to represent the appearance and morphology of the robot accurately. Therefore, in this section, we evaluate the faithfulness of the renderings and geometry when compared with ground truth against several baselines.Training data. To train our model, we first curate a training dataset using Mujoco based on robot's URDF file composed of images of a robot under 10k poses, each captured from 12 viewpoints and split them into training and testing sets. We then use this data to train Dr. Robot by optimizing the objective function in Eq. 7.Evaluation Metrics. Once training is finished, we evaluate the quality of the model on the testing set. We evaluate the appearance with PSNR score between the rendered images and ground truth.For geometry, we use the positions of the target pose Gaussians and evaluate the Chamfer distance between the ground truth pointcloud.Baselines. We benchmark our results on 4 different baselines. Firstly, we compare against Deformable Gaussians [9] , a state-of-the-art method to model deformable objects using GS. Secondly, we include nearest neighbor, where we retrieve the most similar rendering / pointclouds from 1000 samples for evaluation. Thirdly, we include random, where we draw a random sample from the training dataset for evaluation. Nearest neighbor and random baselines benchmark the difficulty of our tasks. Finally, we also include an ablated version of our model where the appearance deformation model is disabled as an ablation studies which we refer to as no deform.Results. The quantitative results are shown in Table 1 , and we also show examples in 3. Foremost, we observe that using LBS as a prior for geometric deformation has a significant positive impact on fidelity when compared with Deformable Gaussians, which uses K-plane [10] for modeling deformation. This gap is particularly notable for long robot arms with many chained joints such as the Panda Arm, where efficiently modeling movement in such high degrees through a neural network becomes infeasible. We evaluate the quality of gradients produced by Dr. Robot on a practical task -robot joint angles estimation from a single in-the-wild image. Given a monocular RGB image I of a robot, we optimize jointly the robot pose p and camera pose θ π to minimize the following objective function,EQUATIONEvaluation Protocols. Following prior works [11, 12] , we used the Panda-3CAM dataset composing around 10K images with corresponding robot poses created by [11] for evaluation. We evaluate the reconstruction results by calculating the L1 distance between the estimated joint angles and the ground truth joint angles. The dataset is composed of three long videos; we, therefore, perform optimization from scratch on the first frame of the video and warm start later frames with joint and camera poses from the previous frame as initialization, similar to what's done in both [11, 12] .Baseline. We compare against the state-of-the-art method on this task -Robopose [12] . Robopose estimates robot poses from a single image by a refiner neural network which takes in the image and the current rendering of the robot from the CAD model of the robot and output the delta joint angles.Results. Quantitatively, we outperforms the previous SOTA method [12] by 32.9%. We perform consistently better across three camera platforms with different fields of view and mounting positions. As shown in Fig. 4 , our reconstruction is highly accurate and is able to event reconstruct the gripper poses when they are visible which is challenging for prior works.While camera and robot pose are both unknown in the preceding experiments, our method allows for reconstruction with known camera or robot parameters, or even using multiple views at once. Step 200Step 145 87Step 32Step 10 "rock on hand" "thumbs up" In this section, we connect Dr. Robot with vision foundation models through image gradients. Different from Sec. 3.2 where we reconstruct robot poses from images/videos, here we show how visual foundation models can help robot plan future actions by passing gradients from the robot's visual appearance to its control parameters.Contrastive language-image models pretrained on internet data have shown remarkable knowledge of open-world images. In this demonstration, we leverage this ability to optimize the similarity of a robot hand to what CLIP judges to be similar to the given text prompt. In particular, let CLIP be denoted by C, which takes in an image and a language embedding, returns the content similarity. Since CLIP is completely constructed from neural networks, it is fully differentiable. Furthermore, let π be our differentiable rasterizer. We optimize the joint angles p for the following objectiveEQUATIONAs shown in Fig. 5 , with only gradients provided by CLIP, we were able to perform optimization in the 24-dimensional action spaces of Shadow Hand to obtain semantically meaningful hand poses that correspond to given language prompts. These results demonstrate that Dr. Robot can be directly connected with off-the-shelf large-scale vision-language models (VLM) through image gradients.Recent works [13, 5] have shown that generative video models can be used for planning and guiding robot behavior. Since action data is not in the pre-training of these models, these approaches train their own inverse dynamics models to estimate robot poses in the generated videos. The ability of Dr. Robot to reconstruct images and videos in section 3.2 makes it an easy drag-and-drop replacement for an inverse dynamics model. To demonstrate this application, we fine-tune Stable Video Diffusion on 100 episodes from the ASU Table Top dataset [14] conditioned on language embeddings of prompts following implementation of [15] . At test time, we are able to condition the video model on novel language prompts, and we reconstruct the robot by optimizing the reconstruction loss L Rec outlined in section 3.2. We display samples from this process in figure 6 .Motion retargetting [16, 17] is a long-standing problem in robotics that studies transferring motion from one embodiment to another. In prior works, this is typically done through estimating and matching the kinematic structure, e.g., between a humanoid robot and the human body. Here, we explore a new way to perform motion retargeting by matching the keypoint trajectories detected by video point trackers [18] . By using the point tracking model P and a differentiable renderer π, we can track the initial points c r 0 on the robot across time to obtain c r 1:T = P(c r 0 , π(p 1:T )) ∈ R T ×C2×2 . Our goal is to maximize the similarity between the tracked robot points c r 1:T ∈ R T ×C1×2 and the goal points c 1:T ∈ R T ×C2×2 . Since these points lack correspondence, we minimize the sum of Chamfer distances across each time step:EQUATIONAs shown in figure 7 , gradients produced by the sum of Chamfer distance between two sets of point tracks can be back-propagated through the point tracker, the image, and the robot model to optimize 24/37-dimensional control parameters. Since our method only requires test-time optimization, we relax the need for domain-specific training data, which is very hard for traditional motion re-targeting. 
related works: 
 Differentiable Rendering Rendering transforms 3D representations into 2D images, with differentiable rendering enabling gradient calculations via images. Research in this domain explores differentiable rendering using explicit 3D representations like meshes [19, 20] , voxels [21, 22] , and point clouds [23, 24] , often converting these discrete structures into probabilistic distributions to facilitate gradient calculations. Since 2019, neural 3D representations have become prominent, providing fully differentiable frameworks. Key developments include NeRF [25] for volumetric rendering, DeepSDF [26] parameterizing signed distance functions, and Occupancy Networks [27] for volume occupancy modeling, applied in tasks like 3D reconstruction [28, 29, 30] . Recent advancements address the limitations of explicit representations and slow neural rendering, with Gaussian Splatting [7] introducing efficient differentiable rasterization using 3D Gaussians. This approach has been extended to model dynamics [9] , physics [31, 32] , and deformable objects [33] .Visual Foundation Models for Robotics Recent advancements have been made in pre-training perception models within visuomotor policies to develop robust visual representations, focusing on video prediction and contrastive learning to grasp dynamics and causality crucial for physical interactions. Techniques include predicting future events from current observations [34, 35, 36, 37, 38] and improving visual representations in robotics through contrastive learning and masked autoencoding [35, 39, 40, 41, 42, 43, 44] . Studies also focus on deriving a generalizable reward function from visual pretraining for reinforcement learning [4, 45, 46] .With the rapid advancements in text-to-video generative models [47, 48, 49, 50, 51] , there is renewed interest in utilizing internet-scale video pretraining for robotics [5] . Some research employs video generative models as world simulators [52, 13, 47] , predicting future scenarios conditioned on specific actions. Concurrently, video-language models are being used for longer-horizon planning [53, 54, 55] . In this paper, we propose an interface between these visual foundation models and the action parameters of robots, allowing planning and control of robot actions through image gradients. 
conclusion: 
 In conclusion, our introduction of differentiable robot rendering bridges the modality gap between visual data and robotic action data, facilitating various applications of vision foundation models to robotic tasks. By integrating a kinematics-aware deformable model with Gaussian Splatting, our approach ensures compatibility across various robot form factors and degrees of freedom. Both quantitative and qualitative experiments confirm that our differentiable rendering model provides efficient and effective gradients for controlling robots directly from pixel data. We believe that Dr. Robot will become an effective tool for future applications with vision foundation models in robotics. 
 
 Appendix: We initialize the canonical 3D Gaussians using 10,000 uniformly sampled point cloud on the robot mesh, and train the canonical Gaussians for 2,000 steps using the L1 image reconstruction loss. Simultaneously, we train our implicit LRS model using the chamfer distance between canonical and posed robot point clouds obtained from simulation. Finally, we train the canonical gaussians, the LRS model, and our appearance deformation network jointly on the L1 image reconstruction loss until convergence. The overall process takes 15-30 minutes on a single NVIDIA 3090 GPU depending on the robot embodiment.Implicit LRS and appearance deformation modules of our method are modeled using 4-layer 256 hidden dimension lightweight MLPs. We encode coordinates using Fourier features as inputs to the networks.We keep all canonical 3D Gaussian training hyperparameters consistent with the original 3D Gaussians codebase, and we use the same training hyperparameters across all robot embodiments.In section 3.3.1 as our CLIP model, we use openai/clip-vit-base-patch32 from Huggingface. We minimize the dot product between language and image embeddings that are output by their respective towers.To obtain the generative video model shown in the paper, we fine-tune SVD-XT, which can generate 24-frame videos from an image. To enable language conditioning of this model, we replace the OpenCLIP laion2b_s32b_b79k model image embeddings with text embeddings from the same model. With this modification, we fine-tune this model on 256x256 videos from the OpenEmbodiment ASU dataset at 10fps following the implementation of [15] until convergence, which takes 12 hours on 2 A100 GPUs. The initial image conditioning and the prompt for the generated videos in the paper are chosen from a set of 12 validation episodes not seen during training. 


Decision: The reviewers agree that the paper is well-written and presents an innovative approach combining Gaussian Splatting with robot kinematics. They highlight the potential impact on bridging visual foundation models with robotics and the method's versatility across robot types. Quantitative results show significant improvements in rendering quality and pose estimation. However, concerns are raised about limited real-world validation, heavy reliance on simulations, and potential computational complexity. Reviewers suggest more comprehensive comparisons with existing methods, ablation studies, and addressing the sim-to-real gap. While the paper demonstrates interesting applications, more rigorous evaluation of practical robotic scenarios and computational efficiency is recommended to strengthen the work.
Following the rebuttal, reviewers comments were addressed, and reviewer FkLZ Raised their scorer to a weak accept.

Reviews: review: title: title: Initial Review of Differentiable Robot Rendering Paper
paper_summary: paper_summary: The paper presents an approach for end-to-end differentiable rendering of robot images conditioned on joint angles and based on robot kinematics information. The method consists of 3 components, namely:  -	Robot’s forward kinematics is used to get a skeleton of the robot in a 2D image from specific viewpoint -	A modified version of Linear Blend Skinning (LBS) in combination with Gaussian Splatting is used to fit the position of the Gaussians for a pose-dependent the robot mesh -	Appearance deformation component is used to adjust the visual and shape information components of the Gaussians for further improvement of the rendering The authors show that their approach outperforms baselines in experiments related to rendering quality and robot pose reconstruction, and show several potential applications when combining the approach with foundation models.
main_review: main_review: The paper is well-written and well-organized, with enough detail to understand the concepts and enough information in the supplementary material regarding the network architectures and the training procedure to reproduce the results. The authors combine several existing approaches (Gaussian Splatting, Linear Blend Skinning) but also extend them in a meaningful way to propose an end-to-end differentiable robot rendering model, that would be useful and relevant to the robotics community. 

The experiments are sufficient and the quantitative results show that the proposed approach outperforms baselines in rendering quality and robot pose estimation. In addition, the authors present three applications of their methodology and conniving qualitative results (figures and video) for text-to-pose optimization using vision language models, recovering robot poses/actions from generative video model, and motion retargeting from human videos of hand/body to similarly structured robotic platforms.

Strengths:

-	Sound methodology and convincing experimental results show that the proposed method significantly outperforms baselines
-	Usefulness of the model in different kinds of vision-based robotic applications
	
Weaknesses:

-	Limited novelty in terms of methodology components (but with novel extensions and novel applications)
-	Only qualitative results reported for the application-related experiments
questions: questions: - For the self-model quality experiments, you are using nearest neighbor as a baseline, and you only use 1 000 samples from the training set (if I understand correctly) to find the nearest neighbor. However, your model is trained on a much larger training split from the 10k point clouds / 120k renderings respectively. Wouldn’t it be a fairer comparison if you use the complete training split for the nearest neighbor baseline when you compare to your method, and not only 1 000 samples?
- In the appendix you refer to LRS (do you mean LBS)?
- There are typos in lines: 139, 141 and 217
limitations: limitations: 3
review_summary: review_summary: The paper shows a methodology that enables end-to-end optimization of robot control policies from images/video and several interesting potential applications. There are no direct experiments on real robots but the paper demonstrates its applicability on different robotic platforms and due to this as well as due to the scope of the paper I think that hardware experiments are not necessary. Due to the sound methodology, the convincing quantitative and qualitative results and the potential applications I recommend accepting the paper.
title: title: An interesting, novel tool for robot learning pipelines.
paper_summary: paper_summary: This paper presents a method for generating images of robots given their poses. The renderings are done in a differentiable manner. This enables some interesting downstream tasks to be possible such as optimizing robot pose to match a text prompt using a pre-trained image-text encoder.
main_review: main_review: This paper and video were very clearly presented. It is a paper of good quality given that is has great presentation and a novel task and method introduced. 

Strengths:
The paper was easy and clear to read.
This is a very powerful tool for future robot learning researchers. The authors even demonstrate some simple ways to use the rendering pipeline for interesting robot performed tasks.
The rendered results look very similar to the ground truth robots. 

Weaknesses:
It is not clear from the paper how novel the method is. The related works section does mention a number of related rendering paper, but it does not explicitly make comparisons with the presented work. I think that it would help the paper to move the related works section after the introduction.

I think that this work could be very significant in what it can enable for future methods for robot learning. The authors do a good job in demonstrating some simple tasks that can be performed using this renderer.

MISC:
Typo Line 139 "we outperforms" -> "we outperform"
Typo Line 217 "can connects" -> "can connect"
questions: questions: When optimizing the joint states, do you find that it is highly dependent on the initial joint states? For example, if the initial poses for Figure 4 are substantially far from the ground truth, will the optimization still find the best pose? 

Could you please explain how your approach differs from any existing approaches? Is this perhaps the first approach?
limitations: limitations: 3
review_summary: review_summary: This is a well written work that presents and interesting and useful new tool for robot learning.
title: title: Promising Concept with Significant Technical Limitations and Lack of Real-World Validation
paper_summary: paper_summary: This paper introduces "Differentiable Robot Rendering" (Dr. Robot), a novel approach for creating a differentiable self-model of robots. It integrates Gaussian Splatting, Implicit Linear Blend Skinning, and Pose-Conditioned Appearance Deformation to enable full differentiability from visual appearance to control parameters. The authors demonstrate applications in text-to-robot pose generation, action sequence generation, and visual motion retargeting, aiming to bridge the gap between visual foundation models and robotic control.
main_review: main_review: Strengths:

1. Novelty: The integration of Gaussian Splatting with robot kinematics is an innovative approach to creating differentiable robot models.
2. Potential Impact: If successful, this method could significantly advance the application of visual foundation models to robotics.
3. Versatility: The approach is designed to work with various robot form factors and degrees of freedom.
4. Performance: The reported 32.9% improvement over state-of-the-art in robot pose reconstruction is impressive.

Weaknesses:

1. Limited Real-World Validation: The paper lacks substantial testing on physical robots, which is crucial for demonstrating real-world applicability.
2. Simulation Dependence: Many experiments seem to rely heavily on simulated environments, raising questions about sim-to-real transfer.
3. Computational Complexity: The method relies on optimization at test time, which may be computationally expensive and slow for real-time robotic applications. The paper does not adequately address or evaluate computational efficiency.
4. Robustness: There's insufficient discussion on the robustness of the method to sensor noise, lighting variations, and other real-world factors.
5. Comparison: While the paper claims superiority over previous methods, the comparison is limited and not comprehensive enough, especially for text-to-robot control and motion retargeting applications.
6. Lack of Ablation Studies: The paper would benefit from thorough ablation studies to justify design choices and demonstrate the importance of each component.
7. Weak Theoretical Foundation: The paper lacks a rigorous theoretical analysis of the proposed method's properties, convergence, or optimality guarantees.
8. Sim-to-Real Gap: The paper does not address the potential challenges in transferring the learned models to real robots, which is a critical concern for practical applications.

The paper presents an intriguing concept with potentially significant implications for robot learning. However, it falls short in demonstrating its practical applicability in real-world robotics scenarios. The reliance on simulated environments and lack of extensive physical robot testing is a significant limitation.

The use of visual foundation models like CLIP for robot control is innovative, but the paper doesn't adequately address the potential limitations of such an approach, such as the possibility of hallucinated or unsafe actions, especially in more complex, multi-step tasks.
While the paper demonstrates improvements in robot pose reconstruction, it's unclear how this translates to improved robot performance in practical tasks. More concrete examples of how this approach enhances robot capabilities in real-world scenarios would strengthen the paper significantly.
questions: questions: 1. Can you provide results from extensive testing on physical robots across various tasks and environments?
2. How does the computational complexity of your approach compare to existing methods, and is it feasible for real-time control on typical robotic hardware?
3. How robust is your method to real-world factors such as varying lighting conditions, occlusions, and sensor noise?
4. Can you provide a more comprehensive comparison with other state-of-the-art methods in robot control and pose estimation, especially for text-to-robot control and motion retargeting?
5. How well does your method generalize to more complex, multi-step robotic tasks beyond the simple scenarios demonstrated in the paper?
6. Can you provide ablation studies to justify your design choices and demonstrate the importance of each component in your framework?
limitations: limitations: 2
review_summary: review_summary: While the paper presents an innovative approach with potential significance for robot learning, the lack of substantial real-world validation and several technical limitations are major concerns. The reliance on simulated environments, limited physical robot testing, and simplified scenarios make it difficult to assess the true impact and applicability of the method. The paper would be significantly strengthened by addressing these issues, providing more comprehensive real-world results, theoretical analysis, and ablation studies. In its current form, I recommend major revisions to address these concerns before consideration for acceptance.