Title: AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Mobile Manipulation; Robotic Manipulation
Abstract: Recently, mobile manipulation has attracted increasing attention for enabling language-conditioned robotic control in household tasks.
However, existing methods still face challenges in coordinating mobile base and manipulator, primarily due to two limitations.
On the one hand, they fail to explicitly model the influence of the mobile base on manipulator control, which easily leads to error accumulation under high degrees of freedom.
On the other hand, they treat the entire mobile manipulation process with the same visual observation modality (e.g., either all 2D or all 3D), overlooking the distinct multimodal perception requirements at different stages during mobile manipulation.
To address this, we propose the Adaptive Coordination Diffusion Transformer (AC-DiT), which enhances mobile base and manipulator coordination for end-to-end mobile manipulation.
First, since the motion of the mobile base directly influences the manipulator's actions, we introduce a mobility-to-body conditioning mechanism that guides the model to first extract base motion representations, which are then used as context prior for predicting whole-body actions. 
This enables whole-body control that accounts for the potential impact of the mobile base’s motion.
Second, to meet the perception requirements at different stages of mobile manipulation, we design a perception-aware multimodal conditioning strategy that dynamically adjusts the fusion weights between various 2D visual images and 3D point clouds, yielding visual features tailored to the current perceptual needs.
This allows the model to, for example, adaptively rely more on 2D inputs when semantic information is crucial for action prediction, while placing greater emphasis on 3D geometric information when precise spatial understanding is required.
We empirically validate AC-DiT through extensive experiments on both simulated and real-world mobile manipulation tasks, demonstrating superior performance compared to existing methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes AC-DiT, a novel diffusion transformer framework designed for mobile manipulation. The method introduces two key modules: (1) **Mobility-to-Body Conditioning**, which uses latent mobility features to guide whole-body action generation, and (2) **Perception-Aware Multimodal Adaptation**, which dynamically adjusts the importance of 2D and 3D visual modalities based on task phase. The approach is validated through extensive simulation and real-world experiments, showing improved performance over imitation learning baselines like DP, RDT, and π₀. AC-DiT aims to address coordination challenges in mobile manipulation under vision-language guidance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n* **1) Problem Motivation is Well-Stated:** The paper clearly identifies critical coordination and perceptual gaps in prior mobile manipulation methods and proposes concrete solutions.\n* **2) Simple Yet Effective Architecture:** The proposed modules are computationally lightweight and integrate seamlessly into diffusion-based transformers, contributing to scalability.\n* **3) Strong Empirical Results:** AC-DiT outperforms multiple baselines across both simulated and real-world tasks, demonstrating its practical value.\n* **4) Detailed Ablation Studies:** The experiments carefully evaluate the impact of each component (e.g., 2D/3D fusion, conditioning mechanisms), reinforcing the modular design’s benefits.\n\n**Weaknesses**\n* **1) Method Novelty is Limited:** Both proposed modules—Mobility-to-Body Conditioning and Perception-Aware Adaptation—are essentially task-specific adaptations of attention-based conditioning mechanisms. The core architectural contribution is somewhat incremental.\n* **2) Unfair Baseline Comparison:** AC-DiT is compared primarily against methods like ACT, DP, and RDT, which were designed for tabletop or bimanual tasks, not mobile manipulation. Missing comparisons to Mobile-ALOHA [1], TidyBot++ [2], or EquiBot [3] weakens the empirical claims.\n* **3) Writing and Typographical Errors:** Several typos and formatting issues are present, such as \"RoboTwen\" (should be RoboTwin, line 270), \"issue\" spelled as \"tissue\" (line 322), and wrong reference to “Figure 3” where it should be “Table 3” (line 296), indicating the need for a more careful proofreading.\n* **4) Limited Theoretical Rigor:** The paper lacks a formal analysis or theoretical justification for why the proposed conditioning mechanisms are superior to simpler alternatives, such as vanilla self-attention across modalities or joints.\n\n```\n[1] Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation, CoRL 2024\n[2] TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning, CoRL 2024\n[3] EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning, CoRL 2024\n```\n\nIn a word, while the paper demonstrates solid engineering and compelling results, its conceptual contributions may be viewed as modest, and comparisons with stronger, more relevant baselines are missing. Strengthening the theoretical motivation and improving experimental fairness could help to raise its final score.\n\nQUESTIONS:\n* **1) How does AC-DiT compare with mobile manipulation-specific baselines like Mobile-ALOHA, TidyBot++, or EquiBot?**\nThe current comparisons seem limited to general-purpose or tabletop-oriented methods.\n\n* **2) Are the two proposed modules (Mobility-to-Body Conditioning and Perception-Aware Adaptation) significantly better than trivial attention mechanisms?**\nCould similar performance be achieved using standard cross-modal or self-attention?\n\n* **3) Can the same AC-DiT policy generalize across diverse mobile manipulation tasks without task-specific fine-tuning?**\nIf not, how scalable is the approach for real-world multi-task deployment?\n\nLIMITATIONS:\n**Limited Methodological Novelty:**\nThe core mechanisms are customized attention strategies without strong theoretical justification or architectural innovation.\n\n**Incomplete and Possibly Unfair Comparisons:**\nThe evaluation lacks direct comparison to mobile manipulation-focused baselines, weakening claims of state-of-the-art performance.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a diffusion-transformer (DiT)-based multi-modal imitation learning method for mobile manipulation. To model the influence of the mobile base on manipulation control, it proposes a mobility-to-body conditioning mechanism to predict manipulation action based on the extracted base motion representations. To better utilize multi-model observations, a perception-aware multimodal conditioning strateg that dynamicall adjusts the fusion weights between 2D images of three different views and 3D point clouds. The proposed method is evaluated and compared with other baseline methods on both simulated and real-world mobile manipulation tasks, and superior performance is achieved.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:  \n1) The paper writing is overall good, with clear description of the motivation and the proposed idea.  \n2) The proposed techniques seems interesting and reasonable for the mobility-to-body conditioning by modeling dependency relationship between mobile base and manipulator, and for the perception-aware multimodal adaptation by adjusting dynamic weights of different modalities during instruction execution.  \n3) The experiments on both simulated and real-world mobile manipulation tasks demonstrate superior performance of the proposed method.  \n\n**Weaknesses**:  \n1) Although the idea of the paper is interesting and reasonable, the proposed mobility-to-body conditioning and perception-aware multimodal adaptation are a bit of too straightforward, which makes the technical contribution of paper be of limited significance.  \n2) The paper lacks clarity about the implementation details. For example, the paper only simply describes the single denoising MSE loss for training without explaining the training procedure and computing cost. The model details of the two action heads (DiT) are not described either. In addition, it is difficult to understand how the mobility-to-body conditioning mechanism can be adapted into a dual-arm-to-dual-arm conditioning mechanism for bimanual manipulation.  \n3) As for the experiments, several details are also missing. it is not clear how the testing data is composed and how the testing data differs from the training data. It seems strange to me why the proposed method is compared with two different sets of baselines for simulated and real-world tasks separately. Finally, it is not explained on which benchmark the ablation study is conducted. \nOverall, the latter two weaknesses make the paper lack of reproducibility.\n\nQUESTIONS:\n1) Please add implementation details for the training procedure, computing resource and time cost.  \n2) Please state clearly in the paper how the training and testing data are composed, and how they differ from each in scenes, tasks (action+object) et al.  \n3) Do you use the same set of language input for training and testing? I am curious how the model is robust and generalizable to different language instructions.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces AC-DiT, an end-to-end framework designed to improve mobile manipulation by addressing two key challenges: coordinating the mobile base and manipulator, and adapting to varying perceptual needs at different task stages.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The mobility-to-body conditioning and perception-aware multimodal adaptation mechanisms are novel contributions, addressing key gaps in mobile manipulation.\n2. The proposed Perception-Aware Multimodal Adaptation (PMA) mechanism is quite simple and intuitive. Empirical results shows it also works well in practice.\n3. Real-world demos are given in the supplementary material.\n\n\n **Weaknesses**\n\n1. The weight analysis presented in Figure 7 may introduce potential misinterpretation due to the use of separate y-axes for 2D and 3D inputs, despite their equal treatment in the model architecture. For instance, during time t₁, while the point cloud appears dominant in the visualization, its actual computed weight is lower than the camera's weight, creating a discrepancy between the graphical representation and the underlying data.\n2. Real-world experiments are limited to a few tasks, and generalization to more diverse environments (e.g., cluttered or dynamic settings) is not fully explored.\n\n\n[minor] The reported \"Gain\" metric in the ablation study requires clarification, as the baseline performance (37.5%) appears inconsistent with the subsequent improvements. \n\n[minor] Several references to tables and figures in the appendix (Lines 31, 37, and 43) are either mislabeled or incorrectly cross-referenced\n\nQUESTIONS:\n1. Could you clarify the precise methodology for applying the importance weights to the corresponding features? Specifically, is this implemented via element-wise multiplication of the feature vectors, or through dynamic adjustment of attention weights within the cross-attention conditioning mechanism?\n2. What is the evaluation suite in Table.4? The reported values (e.g., 49%) are not aligned with any results in above tables.\n3. The supplementary results (Figure 7) indicate the computed importance weights exhibit a relatively constrained numerical range (e.g., point cloud weights varying between 0.73-0.76). Given this limited variation, what is the underlying mechanism that enables these weights to significantly influence the model's performance?\n4. Regarding the architectural implementation: does the manipulator's DiT policy operate in a strictly sequential manner, requiring completion of the mobile base's DiT head computation (to generate the latent mobility features) before initiating its own inference? Furthermore, what is the achievable control frequency of the complete system in your real-world deployment?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper presents a novel strategy to control both the mobile base and the dual-arm of a robot. This is mainly achieved by adding a lightweight mobility action head. Another contribution is a "conditioning strategy" of multimodal data (images and point cloud). Overall, this paper reports a practice of multimodal fusion on mobile manipulation tasks. A good paper.

STRENGTHS AND WEAKNESSES:
1. I'm not sure whether the word "coordination" is a terminology introduced by this paper. (There definitely exist works that handled mobile manipulation). If so, the authors should claim it explicitly and define it formally: what kind of end-2-end mobile manipulation is with coordination, and what is NOT.

2. The author should summarize the parameters of this model. 

3. Will the mobile base wiggle during the manipulation? or stay stably? In my own experience, if the output of one dimension (such as the velocity of the robot base) is always zero, then the algorithm does not know when to generate non-zero value. 

4.There are ?? in the appendix.

QUESTIONS:
Please see my comments above.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02