RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation

Authors: jiaming  liu
mengzhen  liu
zhenyu  wang
pengju  an
xiaoqi  li
kaichen  zhou
senqiao  yang
renrui  zhang
yandong  guo
shanghang  zhang
  robotics
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-13 07:00:52

Abstract: A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots can handle a range of basic tasks, they still face challenges in two areas: (1) insufficient reasoning ability to tackle complex tasks, and (2) high computational costs for VLA model fine-tuning and inference. The recently proposed state space model (SSM) known as Mamba demonstrates promising capabilities in non-trivial sequence modeling with linear inference complexity. Inspired by this, we introduce RoboMamba, an end-to-end robotic VLA model that leverages Mamba to deliver both robotic reasoning and action capabilities, while maintaining efficient fine-tuning and inference. Specifically, we first integrate the vision encoder with Mamba, aligning visual tokens with language embedding through co-training, empowering our model with visual common sense and robotic-related reasoning. To further equip RoboMamba with SE(3) pose prediction abilities, we explore an efficient fine-tuning strategy with a simple policy head. We find that once RoboMamba possesses sufficient reasoning capability, it can acquire manipulation skills with minimal fine-tuning parameters (0.1\% of the model) and time. In experiments, RoboMamba demonstrates outstanding reasoning capabilities on general and robotic evaluation benchmarks. Meanwhile, our model showcases impressive pose prediction results in both simulation and real-world experiments, achieving inference speeds 3 times faster than existing VLA models.

Main paper: 
 introduction: 
 The scaling up of data has significantly propelled research on Large Language Models (LLMs) [1] [2] [3] , showcasing notable advancements in reasoning and generalization abilities within Natural Language Processing (NLP). To comprehend multimodal information, Multimodal Large Language Models (MLLMs) [4] [5] [6] [7] [8] have been introduced, empowering LLMs with the capability of visual instructionfollowing and scene understanding. Inspired by the strong capabilities of MLLMs in general settings, recent research aims to incorporate MLLMs into robot manipulation. On the one hand, some works [9] [10] [11] [12] enable robots to comprehend natural language and visual scenes, automatically generating task plans. On the other hand, Vision-Language-Action (VLA) models [13] [14] [15] leverage the inherent capabilities of MLLMs, empowering them with the ability to predict low-level SE(3) poses.Figure 1 : Overview of RoboMamba. RoboMamba is an efficient robotic VLA model that combines reasoning and manipulation capabilities. First, we integrate and align a vision encoder with the Mamba LLM, endowing our model with common sense and robotic-related reasoning abilities. Subsequently, we introduce an efficient fine-tuning strategy to equip RoboMamba with pose prediction abilities, requiring a few dozen minutes to fine-tune a simple policy head (3.7M parameters). In terms of inference speed, RoboMamba achieves the highest control frequency, surpassing other VLA models, running on an NVIDIA A100 GPU without any quantization or inference acceleration techniques. More real-world downstream tasks are displayed in Figure 4 and Figure 5 .Robot manipulation involves interacting with objects in dynamic environments, requiring human-like reasoning abilities to comprehend the semantic information of scenes [11, 16] , alongside a robust low-level action prediction ability [17, 18] . While existing MLLM-based policies can handle a range of basic tasks, they still face challenges in two aspects. First, the reasoning capabilities of pre-trained MLLMs [6, 19] in robotic scenarios are found to be insufficient. As shown in Figure 1 (reasoning example), this deficiency presents challenges for fine-tuned robot MLLMs when they encounter complex reasoning tasks. Second, fine-tuning MLLMs and using them to generate robot manipulation actions incurs higher computational costs due to their expensive attention-based LLMs [20, 21] . To balance the reasoning ability and efficiency, several studies [22] [23] [24] have emerged in the field of NLP. Notably, Mamba [25] introduces the innovative selective State Space Model (SSM), promoting context-aware reasoning while maintaining linear complexity. Drawing inspiration from this, we raise a question: "Can we develop an efficient robotic VLA model that possesses strong reasoning capabilities while also acquiring robot manipulation skills in a cost-effective manner?"To address this, we propose RoboMamba, an efficient robotic VLA that empowers the Mamba LLM to achieve robust robotic reasoning and action capabilities. As shown in Figure 1 , we initially integrate a vision encoder (e.g., CLIP [26] ) with Mamba to empower RoboMamba with visual common sense and robotic-related reasoning. Specifically, we proceed with alignment pre-training, activating the cross-modal connector [4, 19] to convert visual information into Mamba's token embeddings. We then unfreeze Mamba for instructions co-training, utilizing its powerful sequence modeling to comprehend high-level robotic and general instruction data. On top of this, to equip RoboMamba with SE(3) pose prediction abilities, we explore an efficient fine-tuning strategy with a simple policy head. Notably, we discover that once RoboMamba possesses sufficient reasoning capabilities, it can acquire pose prediction skills with minimal parameter fine-tuning. The fine-tuned policy head constitutes only 0.1% of the model parameters, which is 10 times smaller than existing robotic VLA approaches [15, 14] . In this way, RoboMamba can simultaneously generate robot reasoning using language responses and predict end-effector poses via the policy head.To systematically evaluate our proposed RoboMamba, we conduct extensive experiments in both simulation and real-world scenarios. First, we assess our reasoning abilities on general and robotic evaluation benchmarks. RoboMamba, with only 3.2B parameters, achieves competitive performance on several MLLM benchmarks and also delivers promising results on RoboVQA [27] . With its strong reasoning abilities, RoboMamba achieves state-of-the-art (SOTA) manipulation performance in the SAPIEN simulation [28] , requiring only a 7MB policy head and a few dozen minutes of fine-tuning on a single A100 GPU. Moreover, RoboMamba achieves an inference speed that is 3 times faster than previous robotic VLA models [29, 15] . Additionally, we evaluate RoboMamba in real-world scenarios, where it can generate long-horizon planning and predict the end-effector pose for each atomic task. In summary, our contributions are as follows:• We introduce RoboMamba, an efficient VLA model that integrates a vision encoder with the linear-complexity Mamba LLM, which possesses visual common sense and robotic-related reasoning abilities.• To equip RoboMamba with action pose prediction abilities, we explore an efficient finetuning strategy using a simple policy head. We find that once RoboMamba achieves sufficient reasoning capabilities, it can acquire pose prediction skills with minimal cost.• In our extensive experiments, RoboMamba excels in reasoning on general and robotic evaluation benchmarks, and showcases impressive pose prediction results in both simulation and real-world experiments. 
related work: 
 State Space Models (SSMs). SSMs have become effective substitutes for transformers and CNNs due to their linear scalability with sequence length [30, 23] . Recent works [22, 31, 32] use the state space to robustly establish dependencies across long sequences. Especially, Mamba [25] designs the SSM matrices to be functions of the input, creating a learnable selection mechanism that improves adaptability and reasoning capabilities. [33] [34] [35] [36] [37] [38] expand selective SSMs to vision and video tasks. Furthermore, MambaIR [39] focuses on image restoration, and PanMamba [40] addresses pan-sharpening, while DiS [41] integrates SSMs into diffusion models. These findings demonstrate that Mamba exhibits promising performance and efficiency in various visual downstream tasks. With the emergence of SSMs, we make the first attempt to introduce Mamba to address critical challenges in robotics, which demands efficient action capabilities.Multimodal Large Language Models. Large language models (LLMs) have exhibited remarkable reasoning capabilities across various downstream tasks [19, 42, 2] . When addressing complex multimodal reasoning challenges, multimodal large language models (MLLMs) have shown exceptional visual understanding, i.e., BLIP-2 [43] , OpenFlamingo [44] , LLaMA-Adapter [19, 45] , and LLaVA [46] . Additionally, the introduction of 3D MLLMs [12, 47, 48] seeks to expand the reasoning and conversational capabilities of LLMs to include the 3D modality. However, deploying LMMs is expensive due to their significant computational overhead, primarily caused by their billions of parameters. To mitigate these challenges, recent small-scale models [49, 50] demonstrate impressive performance while maintaining manageable computational costs. LLaVA-Phi [49] empowers the recently developed smaller LLM, Phi-2, for visual instruction tuning. TinyLLaVA [50] and MobileVLM V2 [51] demonstrate that high-quality training data and schemes can effectively compensate for the reasoning abilities of smaller LMMs. Furthermore, Cobra [52] innovatively utilizes an SSM-based Mamba LLM to reduce complexity and improve inference speed on common sense reasoning tasks. Different from previous works, our goal is to develop an efficient Robotic VLA model using the SSM-based language model. This model not only possesses common sense understanding but also has the capability to complete manipulation tasks effectively.Robot Manipulation. Traditional robotic manipulation employs state-based reinforcement learning [53] [54] [55] [56] . In contrast, [57, 11, [58] [59] [60] use state with visual observation as input and then make predictions. Specifically, Where2Act [61] takes visual observations and predicts on actionable pixels and movable regions in objects. Flowbot3d [57] predicts point-wise motion flow on 3D objects. Anygrasp [17] employs point cloud data to learn grasp poses on a large scale datasets. Inspired by the success of MLLMs in general scenarios [43, 44, 19, 45, 46] , several VLA models [13, 16] explore utilizing their common sense reasoning capabilities to address manipulation problems. Palm-E [10] integrates multimodal encodings with LLMs, training them end-to-end for manipulation planning. VoxPoser [11] extracts affordances and constraints from MLLMs to further zero-shot predict trajectories. RoboFlamingo [14] fine-tunes MLLM on vision language manipulation dataset to complete language-conditioned manipulation tasks. ManipLLM [15] introduces specific training scheme for manipulation tasks that equips MLLMs with the ability to predict end-effector poses.ManipVQA [62] , enhancing robotic manipulation with physically grounded information processed by MLLM. In this paper, instead of fine-tuning a pre-trained MLLM, we introduce a novel efficient VLA model that possesses both robotic-related reasoning and low-level pose prediction capabilities. 
robomamba: 
 In Section 3.1, we introduce the preliminaries of our proposed RoboMamba, including the problem statement and a description of the language model. Subsequently, in Section 3.2 and 3.3, we describe the architecture of RoboMamba and how we empower it with common sense and robotic-related reasoning. In Section 3.4, we outline our proposed robot manipulation fine-tuning strategy, which equips our model with pose prediction skills by minimal fine-tuning parameters and time.Problem statement. For robot visual reasoning, our RoboMamba generates a language answer L a based on the image I ∈ R W ×H×3 and the language question L q , denoted as L a = R(I, L q ). The reasoning answer usually contains individual sub-tasks (L a → (L 1 a , L 2 a , . . . , L n a )) for one problem L q . For example, when faced with a planning question like 'How to clean the table?', the response typically includes steps such as 'Step 1: Pick up the object' and 'Step 2: Place the object in the box'. For action prediction, we utilize an efficient and simple policy head π to predict an action a = π(R(I, L q )). Following previous works [63, 15] , we use 6-DoF to express the end-effector pose of the Franka Emika Panda robot arm. The 6-DoF includes the end-effector position a pos ∈ R 3 representing a 3D coordinate and direction a dir ∈ R 3×3 representing a rotation matrix. If training for a grasping task, we add gripper status to the pose prediction, resulting in a 7-DoF control.State Space Models (SSMs). In this paper, we select Mamba [25] as our language model. Mamba consists of numerous Mamba blocks, with the most crucial component being the SSM. SSMs [21] are designed based on continuous systems, projecting the 1D input sequence x(t) ∈ R L into a 1D output sequence y(t) ∈ R L through a hidden state h(t) ∈ R N . An SSM consists of three key parameters: the state matrix A ∈ R N ×N , the input matrix B ∈ R N ×1 , and the output matrix C ∈ R N ×1 . The SSM can be represented as follows:EQUATIONRecent SSMs (e.g., Mamba [25] ) are constructed as discretized continuous systems using a timescale parameter ∆. This parameter transforms the continuous parameters A and B into their discrete counterparts A and B. The discretization employs the zero-order hold method, defined as follows:EQUATIONEQUATIONEQUATIONMamba introduces the Selective Scan Mechanism (S6) to form its SSM operator in each Mamba block. The SSM parameters are updated to B ∈ R B×L×N , C ∈ R B×L×N , and ∆ ∈ R B×L×D , achieving better content-aware reasoning. The details of the Mamba block are shown in Figure 2 .To equip RoboMamba with both visual reasoning and manipulation abilities, we start from pretrained Large Language Models (LLMs) [25] and visual models to construct an effective VLA model architecture. As shown in Figure 2 , we utilize the CLIP visual encoder [26] to extract visual features f v ∈ R B×N ×1024 from input images I, where B and N represent batch size and tokens, respectively. In contrast to [64, 52] , we do not adopt the vision encoder ensemble technique, which employs various backbones (i.e., DINOv2 [65] , CLIP-ConvNeXt [66] , CLIP-ViT) for image feature extraction. The ensemble introduces additional computational costs that severely impact the practicality of VLA model in the real world. Therefore, we demonstrate that a simple and straightforward model design can also achieve strong reasoning abilities when combined with highquality data and appropriate training strategies. To enable the LLM to understand visual features, we connect the vision encoder to the LLM using a multilayer perceptron (MLP). Through this simple cross-modal connector, RoboMamba can convert visual information into language embedding space f L v ∈ R B×N ×2560 . Note that model efficiency is crucial in the field of robotics, as robots need to respond quickly based on human instructions. Therefore, we select Mamba as our language model due to its context-aware reasoning ability and linear computational complexity. Text prompts are encoded into embedding space f t ∈ R B×N ×2560 using the pre-trained tokenizer, then concatenated (cat) with visual tokens and input into Mamba. We leverage Mamba's powerful sequence modeling to comprehend multimodal information and utilize effective training strategies to develop visual reasoning capabilities (as described in the next section). The output tokens T a are then detokenized (det) to produce responses in natural language L a . To equip our model with both reasoning and manipulation abilities, we meticulously design a comprehensive training pipeline, which is divided into two stages. We introduce the training recipes of Stage 1 in Section 3.3 and present the robot manipulation fine-tuning in Section 3.4.After constructing the RoboMamba architecture, the next goal is to train our model to learn general vision and robotic-related reasoning abilities. As shown in Figure 2 , we divide our Stage 1 training into two steps: alignment pre-training (Stage 1.1) and instruction co-training (Stage 1.2). Specifically, unlike previous MLLM training methods [19, 67, 64] , we aim to enable RoboMamba to comprehend both common vision and robotic scenes. Given that the robotics field involves numerous complex and novel tasks, RoboMamba requires enhanced generalization capabilities. Therefore, we adopt a co-training strategy in Stage 1.2, combining high-level robotic data (e.g., task planning) with general instruction data. We find that co-training not only leads to more generalizable robotic policies but also enhances the majority of general scene reasoning abilities due to the complex reasoning tasks embedded in the robotic data (demonstrated in Appendix C). The training details are shown below:Stage 1.1: Alignment pre-training. We adopt LLaVA [4] filtered 558k image-text paired dataset for our cross-modal alignment. As shown in Figure 2 , we freeze the parameters of the vision encoder and Mamba language model, and only update the project layer. In this way, we can align image features with the pre-trained Mamba word embedding.State 1.2: Instruction co-training. In this stage, we first follow previous MLLM works [4, 64, 52] for general vision instruction data collection. We adopt the 655K LLaVA mixed instruction dataset [4] , the ShareGPT4V-SFT dataset [68] , or the LLaVA-Next dataset [69] . In our dataset selection, mitigating hallucination is crucial in robotic scenarios, as the robotic MLLM needs to generate task planning based on real scenes rather than imagined ones. For example, existing MLLMs might formulaically answer "open the microwave" with "step 1: find the handle," but many microwaves do not have handles. Next, we incorporate the RoboVQA dataset [27] to learn high-level robotic skills, such as long-horizon planning, success classification, discriminative and generative affordance, past description, and future prediction. During co-training, as shown in Figure 2 , we freeze the parameters of the CLIP encoder and fine-tune the projection layer and Mamba on the combined instruction datasets. All outputs from the Mamba language model are supervised using the cross-entropy loss.Building upon RoboMamba's strong reasoning ability, we introduce our robot manipulation finetuning strategy in this section, termed Training Stage 2 in Figure 2 . Existing manipulation VLA models [29, 15, 14] require updating the projection layer and the LLM during the manipulation fine-tuning stage. While this paradigm can develop action prediction capabilities, it also breaks the inherent abilities of the pre-trained model and demands significant training resources. To address these challenges, we propose an efficient fine-tuning strategy, as shown in Figure 2 . We freeze all the parameters of RoboMamba and introduce a simple policy head to model Mamba's output tokens. The policy head contains two types of MLPs that separately learn the end-effector's position a pos and direction a dir , collectively occupying around 0.1% of the model's total parameters. Following [61] , the position and direction losses are formulated as follows:EQUATIONEQUATIONwhere N represents the number of training samples, T r(A) means the trace of matrix A. In our open-loop simulation experiments, RoboMamba only predicts the 2D position (x, y) of the contact pixel in the image, which is then translated into 3D space using depth information. We also derive the gripper's left direction (gripper z-forward) from its up and forward orientations based on geometric relationships. To evaluate this fine-tuning strategy, we generate a dataset of 10k end-effector pose predictions using the SAPIEN simulation [28] . After manipulation fine-tuning, we find that once RoboMamba possesses sufficient reasoning capabilities, it can acquire pose prediction skills with extremely efficient fine-tuning. Due to the minimal fine-tuning parameters (7MB) and efficient model design, we need only a few dozen minutes to achieve novel manipulation skills. This finding highlights the importance of reasoning abilities for learning manipulation skills and presents a new perspective: we can efficiently equip an VLA model with manipulation abilities without compromising its inherent reasoning capabilities. Finally, RoboMamba can use language responses for common sense and robotic-related reasoning, and the policy head for action pose prediction. 
experiment: 
 In Section 4.1, we introduce our experiment settings, including dataset, implementation, and evaluation benchmark details. Subsequently, we conduct extensive experiments to demonstrate Robo-Mamba's reasoning and manipulation abilities in Sections 4.2 and 4.3, respectively. To thoroughly validate the effectiveness of each method design, we perform an ablation study in Section 4.4. Finally, the qualitative results of real-world experiments are presented in Section 4.5.Datasets (Stage 1) In the alignment pre-training stage, we utilize the LLaVA-LCS 558K dataset [67] , which is a curated subset of the LAION-CC-SBU dataset, supplemented with captions. During the instruction co-training stage, we combine general instruction datasets with the robotic instruction datasets. Specifically, for the general instruction dataset, we selectively adopt the LLaVA mixed instruction dataset [4] , the ShareGPT4V-SFT dataset [68] , or the LLaVA-Next dataset [69] . For the robotic instruction dataset, we randomly sample some image-text paired training samples from the RoboVQA [27] dataset. In our main experiments, a mixture of the LLaVA 1.5 instruction dataset and the 300K RoboVQA dataset is used during the co-training stage. Detailed descriptions of these datasets are provided in Appendix B.For the dataset used in the robot manipulation fine-tuning stage, we follow the data collection process of previous works [61, 15] , adopting the SAPIEN engine [28] to set up an interactive simulation environment with articulated objects from PartNet-Mobility [58] . The Franka Panda Robot, equipped with a suction gripper, serves as the robotic actuator. During data collection, we randomly select a contact point p on the movable part and orient the end-effector's z-axis opposite to its normal vector, with a random y-axis direction to interact with the object. Successful operations are categorized as successful samples and integrated into the dataset. Reasoning evaluation benchmarks To evaluate reasoning capabilities, we employ several popular benchmarks, including VQAv2 [71] , OKVQA [72] , RoboVQA [27] , GQA [73] , VizWiz [74] , POPE [75] , MME [76] , MMBench [77] , and MM-Vet [78] . As detailed in Appendix E, we describe the key aspects each benchmark focuses on when assessing models in the field of robotics. Notably, we also directly evaluate RoboMamba's robotic-related reasoning abilities on the 18k validation dataset of RoboVQA, covering robotic tasks such as long-horizon planning, success classification, discriminative and generative affordance, past description, and future prediction.Manipulation evaluation benchmarks To evaluate our model's manipulation capabilities, we follow previous works [57, 63, 15] and test open-loop task completion accuracy exclusively in the simulator [28] . The predicted contact point and rotation are used to interact with objects. To measure the model's performance, we use the classical manipulation success rate, defined as the ratio of successfully manipulated samples to the total test samples. A manipulation action is considered successful if the difference in the object's joint state before and after interaction exceeds a threshold of 0.1 meters. In real-world experiments, we use the Franka Panda robot to manipulate several articulated objects. achieves competitive results across all benchmarks. Specifically, our model achieves satisfactory results on the POPE benchmark, helping to reduce failed robot actions caused by hallucinations.Although some performances of RoboMamba are still below those of LLaVA1.5 and SPHINX, we prioritize using a smaller and faster Mamba to balance the efficiency of the robotic model. In the future, we plan to develop RoboMamba-7B for scenarios where resources are not limited.Robotic-related reasoning. To comprehensively compare RoboMamba's robotic-related reasoning abilities, we benchmark it against LLaMA-AdapterV2 [45] and TinyLLaVA [83] on the RoboVQA [27] validation set. We choose LLaMA-AdapterV2 as a baseline because it serves as the base model for the current state-of-the-art (SOTA) robot MLLM, ManipLLM [15] . Meanwhile, TinyLLaVA is chosen as a representative tiny MLLM, enabling a comparison of robotic-related reasoning abilities. For a fair comparison, we load the pre-trained parameters of both LLaMA-AdapterV2 and TinyLLaVA and fine-tuned the baseline models on the RoboVQA training set for two epochs, using their official instruction-tuning method. As shown in Table 1 , RoboMamba achieves superior performance across BLEU-1 to BLEU-4. The results indicate that our model possesses advanced robotic-related reasoning capabilities and confirms the effectiveness of our training strategy.In addition to higher accuracy, our model achieves inference speeds 7 times faster than LLaMA-AdapterV2 and ManipLLM, which can be attributed to the content-aware reasoning ability and efficiency of the Mamba language model [25] . Finally, we visualize the qualitative results in Figure 4 .Baselines. To evaluate RoboMamba's manipulation abilities, we compare our model with four baselines: UMPNet [63] , Flowbot3D [57] , RoboFlamingo [14] , and ManipLLM [14] . Before comparison, we reproduce all baselines and train them on our collected dataset. For UMPNet, we execute manipulation on the predicted contact point, with the orientation perpendicular to the object's surface. Flowbot3D predicts motion direction on the point cloud, selecting the largest flow magnitude as the interaction point and using the direction of the flow to represent the end-effector's orientation. RoboFlamingo and ManipLLM separately load the pre-trained parameters of OpenFlamingo [44] and LLaMA-AdapterV2 [45] , and follow their respective fine-tuning and model updating strategies.Results. As shown in Table 2 , our RoboMamba achieves a 7.0% improvement on seen tasks and a 2.0% improvement on unseen tasks compared to the previous SOTA ManipLLM. Moreover, our method showcases SOTA performance across 14 of 20 seen tasks, highlighting its effectiveness and stability in predicting action poses. For unseen tasks, the recent three MLLM-based methods-RoboFlamingo, ManipLLM, and our method-all achieved promising performance. The results demonstrate that leveraging the strong generalization abilities of MLLMs can effectively improve the policy's generalization ability while enhancing accuracy on unseen objects. Regarding efficiency, RoboFlamingo updates 35.5% (1.8B) of the model parameters, ManipLLM updates an adapter (41.3M) comprising 0.5% of the model parameters, whereas our fine-tuned simple policy head (3.7M) only constitutes 0.1% of the model parameters. RoboMamba effectively updates 10 times fewer parameters than previous MLLM-based methods while achieving seven times faster inference speeds.The results reveal that our RoboMamba not only possesses strong reasoning abilities but also can acquires manipulation capabilities in a cost-effective manner.Table 2 : Comparison of the success rates between RoboMamba and baselines across various training (seen) and test (unseen) tasks. The representation for each task icon is shown in Table 3 .Method UMPNet [63] 0.28 0.41 0.25 0.20 0.49 0.20 0.35 0.57 0.51 0.25 0.66 0.17 0.17 The impact of LLM on reasoning abilities. As shown in Figure 3 a), we explore the impact of different LLMs on general and robotic-related reasoning abilities. Given that efficiency is crucial in robotic tasks and directly affects the practicality of policy models, we compare Mamba-2.7B with other linear complexity LLMs. For all experiments, we utilize the same training data and strategy.Compared with RWKV-3B [24] , Mamba-2.7B achieves significant improvements on both common sense and robotic-related reasoning benchmarks. The results demonstrate that the Mamba-2.7B model not only possesses linear complexity but also efficiently acquires strong reasoning abilities through our proposed training strategy. Meanwhile, our proposed RoboMamba VLA framework and training strategy can also be adapted to other, more advanced linear-complexity LLM models.The impact of reasoning abilities on manipulation accuracy. We explore whether utilizing MLLMs with different reasoning abilities affects manipulation skill learning. For a fair comparison, we use the same manipulation fine-tuning strategy, injecting and fine-tuning a simple MLP policy head after the MLLM (while freezing other parameters). We compare our RoboMamba-2.7B (Ours-2.7B) with OpenFlamingo, LLaMA-AdapterV2, and our RoboMamba-1.4B. As shown in Figure 3 b), Ours-2.7B achieves promising results compared with other methods, which is proportional to its reasoning ability. Meanwhile, Ours-2.7B (w/o C) indicates that we did not use the instruction co-training method, omitting the robotic-related RoboVQA dataset during fine-tuning. We find that this also impacts the accuracy of manipulation, especially reducing the model's generalization ability when facing unseen objects. The results confirm our finding: fine-tuning an MLLM to learn robot skills does not require extensive resources; it only requires that the MLLM possesses strong robotic-related reasoning abilities. Additionally, we present more ablation studies in Appendix C, including explorations of different vision encoders, training datasets, and policy head design. As shown in Figure 4 , we visualize RoboMamba's reasoning results across various robotic downstream tasks. For task planning, compared to LLaMA-AdapterV2, RoboMamba demonstrates more accurate and long-horizon planning abilities, thanks to its strong reasoning capabilities. For a fair comparison, we also fine-tuned the baseline LLaMA-AdapterV2 on the RoboVQA dataset. Additionally, RoboMamba accurately performs fundamental robotic tasks such as affordance generation and discrimination, proving that it can understand robotic scenes. Notably, our model also possesses past and future prediction capabilities, further highlighting its robust reasoning capabilities. Prediction of past and future actions is crucial in robotic manipulation, as it not only enables effective rethinking of past failure actions but also enhances the robustness of future manipulation pose generation. For low-level action, we use a Franka Emika robotic arm to interact with various household objects. Due to the direct visualization of the gripper causing occlusion, we project RoboMamba's predicted 6 DoF pose onto a 2D image, using a red dot to indicate the contact point and the end-effector to show the rotation, as shown in the bottom right corner of the figure.More real-world demonstrations are provided in Appendix D and the supplementary video file. Meanwhile, as shown in Figure 5 , we also visualize the failure cases of RoboMamba's predictions in both reasoning and manipulation tasks. 
conclusion and future plan: 
 In this paper, we introduce RoboMamba, an efficient VLA model that combines a vision encoder with the linear-complexity Mamba LLM, equipped with visual common sense reasoning and robotic reasoning abilities. Based on our RoboMamba, we can impart new manipulation skills to the model by fine-tuning a simple policy head (0.1% of the model) in a few dozen minutes. This finding reveals how to efficiently equip an VLA model with manipulation abilities without compromising its inherent reasoning capabilities. Finally, RoboMamba excels in reasoning on both general and robotic-related evaluation benchmarks and showcases impressive pose prediction results. Regarding limitations, while our proposed RoboMamba achieves efficient inference speed, its reliance on a 2.7B LLM leads to limitations on certain complex reasoning tasks when compared to MLLMs built on 7B/13B LLMs. Looking ahead, our future work will focus on two main directions. 1) We plan to adapt the RoboMamba VLA framework and training strategy to more advanced linear-complexity LLM models to further enhance its reasoning and manipulation capabilities. 2) Constructing a 4D Robot VLA model [84, 48, 85] , as 3D point cloud and temporal data contain more robotics-specific information that aids in predicting robust low-level actions. 
safeguards: 
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 Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets? 
 
 Appendix: Due to space limitations, we provide additional details of the proposed method in this supplementary material. In Appendix B, we offer a more detailed description of our training dataset, including alignment pre-training, instruction co-training, and robot manipulation fine-tuning datasets. Additional ablation studies are presented in Appendix C, exploring the impact of different image encoders on reasoning ability, the impact of different training datasets on reasoning ability, and the effect of various head designs on manipulation fine-tuning. In Appendix D, we show additional qualitative results across multiple robot-related downstream tasks. Finally, we provide the metric selection rationale and the usage of prompts during testing in Appendix E. [71] , GQA [73] , OKVQA [72] , OCRVQA [72] , A-OKVQA [87] , TextCaps [88] , RefCOCO [89, 90] , and Visual Genome (VG) [91] . This mix dataset is also one of the most renowned datasets used for instruction tuning in several works [4, 67] .2) ShareGPT4V-SFT dataset: The ShareGPT4V-SFT dataset is similar to LLaVA-v1.5 655K [67] , except that the 23K detailed description entries in LLaVA-1.5-SFT are replaced with detailed captions randomly sampled from the 100K ShareGPT4V data [86] . Regarding the variation between training and testing data, we followed the data collection settings of where2act [61] and ManipLLM [15] . The specific variations can be divided into two aspects: 1) Asset Variation and 2) State Variation.1) Asset Variation: We use 20 categories from PartNet [58] for seen objects and reserve the remaining 10 categories for unseen objects to analyze if RoboMamba can generalize to novel categories. Specifically, we further divide the seen objects into 1037 training shapes and 489 testing shapes, using only the training shapes to construct the training data. Thus, the shapes of the seen objects encountered during training and testing are different. For unseen categories, there are a total of 274 shapes, which are used exclusively in the testing data.2) State Variation: We observe the object in the scene from an RGB-D camera with known intrinsics, mounted 4.5-5.5 units away from the object, facing its center. The camera is located at the upper hemisphere of the object with a random azimuth between [-45, 45] and a random altitude between [30, 60] . Since the tasks involve 'pulling,' we also initialize the starting pose for each articulated part randomly between its rest joint state (fully closed) and any position up to half of its joint state (half-opened). These state settings are utilized for both training and testing data, aiming to boost the model's generalization ability. The impact of different image encoders on reasoning abilities In this section, we replace the CLIP encoder used in our initial submission with other linear-complexity encoders, such as XCiT [92] . Additionally, we supplement our experiments by using SigLIP [70] as an image encoder. As shown in Table 4 , we analyze the impact of different image encoders and input resolutions on reasoning abilities. The training dataset and strategy remain consistent with those in our main experiment.The results indicate that the choice of image encoder and input resolution does not significantly impact reasoning ability within our RoboMamba VLA framework. However, using an image encoder without cross-modality alignment (i.e., XCiT) presents challenges in converting image tokens to LLM language embeddings. Although our training process includes an alignment pre-training stage, this primarily trains the projection layer. Therefore, in future work, we aim to develop a robotics-specific image encoder capable of projecting image tokens into language embeddings while maintaining linear computational complexity to further improve inference speed.The impact of training datasets on reasoning abilities As shown in Table 5 , we examine the impact of different training datasets on common sense and robotic-specific reasoning abilities. Specifically, we conduct these experiments using 224 × 224 input images and the CLIP vision encoder. First, we observe that Ex2 outperforms Ex1 across two benchmarks, confirming that incorporating robotic instruction data can effectively enhance specific reasoning abilities. Similarly, comparing Ex3 and Ex4 shows comparable results, though performance on the GQA benchmark declines. However, in our generated descriptions within robotic scenes, we find that the inclusion of robotic instruction data enhances understanding of geometric relationships. Consequently, we plan to propose a roboticspecific geometric reasoning benchmark to more accurately assess the spatial reasoning capabilities of VLA models. Finally, comparing Ex1, Ex3, and Ex5, we find that using more advanced general instruction datasets does not yield significant performance improvements, which may be due to the model capacity of the 2.7B LLM.The impact of policy head designs on manipulation accuracy As shown in Table 6 , we explore the impact of different policy head designs on manipulation skill learning. In this table, MLP×1 means using only one MLP heads to predict the position and direction of the end-effector pose. MLP×2 means using one shared head to predict direction and another head to predict position separately. (SSM block+MLP)×2 is similar to MLP×2 but adds a State Space Model (SSM) block before the MLP to increase the parameter count of the policy head. The experimental results show that the manipulation accuracy across the three configurations is quite similar, indicating that the parameter count of the fine-tuning policy head has small impact on the results. Combined with Figure 3 b ), this further supports our finding that once RoboMamba achieves sufficient robotic reasoning capabilities, it can acquire pose prediction skills at a low cost, regardless of the policy head design. We conduct real-world experiments involving interactions with various household objects using a Franka Emika robotic arm. We modify the finger gripper by attaching double-sided tape to convert it into a suction gripper, providing the gripper head with adhesive properties. The video demonstrations are included in the supplementary video file. As shown in Figure 5 , we visualize our model's reasoning results on a series of robotic downstream tasks, including long-horizon planning, discriminative affordance, generative affordance, past description, and future prediction. Additionally, failure cases in reasoning are illustrated in Figure 6 . Compared to the ground truth, RoboMamba demonstrates limitations in reasoning ability on some complex tasks, occasionally misinterpreting the current task objective or the target manipulated object.• VQAv2 and OKVQA: These benchmarks are utilized to assess the model's proficiency in basic vision question answering, which is a foundational skill in embodied AI. This ability ensures that the model can understand and respond to visual content effectively.• POPE and VizWiz: These benchmarks are chosen to evaluate the model's capability to answer questions without falling prey to visual illusions or ambiguities. This aspect is crucial for avoiding significant errors in robotic applications. Question:<image>\n{current goal is: please remove the snacks from the tray immediate next step?}answer: RoboMamba(Ours): Place the popcorn packet on the table • GQA: These benchmarks are employed to test the model's ability to identify and comprehend the types and positions of important objects within an image. Such spatial identification skills are vital for tasks related to robotic manipulation and interaction with the environment. • RobotVQA: This benchmark is used to assess the model's ability to plan and understand actions based on both textual and visual inputs. This skill is indispensable in the realm of robotics, where understanding and executing complex actions is necessary. • MM-Vet, MME and MMB: These benchmarks are utilized to evaluate multimodal large language models's ability to integrate on complex multi-modal tasks including Recognition, Spatial awareness, OCR, and Math. All of them contain a wealth of evaluation indicators, such as perception and cognition, which can fully demonstrate the performance of the model under different tasks, and this performance is the best embodiment of the comprehensive application performance of multimodal large language models(MLLM).Answer • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 10. Broader Impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [Yes] Justification: Yes. Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 


Decision: The submission initially received mixed reviews; the authors did a great job during the rebuttal, after which all reviewers became positive about the submission.  The AC agrees with the recommendations.  The authors should incorporate the rebuttal into the camera ready.

Reviews: review: paper_summary: paper_summary: The paper proposes a method for robot reasoning and manipulation by developing a multimodal large language model (MLLM) based on the Mamba state space model (SSM). The joint model is able to both attack robot reasoning tasks assessed via VQA, and solve robot manipulation tasks by fine-tuning a dedicated head that contains only a small fraction of the model parameters.
strength_weakness: strength_weakness: Although time efficiency is one of the major claimed contributions of the work, the aspect of time efficiency is not well covered in the experimental evaluation. The fact that the proposed method is 7 times faster than SOTA MLLM methods is mentioned a couple of times, however detailed results regarding inference times are not provided. 

Another aspect not covered in detail, regards the effect the number of parameters in the manipulation head has on the performance and efficiency of the manipulation task.

Another question that comes to mind, is whether it is possible to apply the idea of fine-tuning only a small-sized manipulation head to other MLLMs. Does Mamba work better with this constrained policy head and, if yes, why?

Finally, another issue regards reproducibility, as details regarding the structure of the projection layers and the MLPs used in the manipulation policy head are not provided. 

### Minor comments
- L.13: "action pose prediction abilities" is not very clear
- L.100 "ManipVQA, enhancing robotic" verb missing
- L.192: what is the angle representation considered?
- L.235: "on when"
questions: questions: - Can you please provide results regarding comparison with SOTA methods in terms of time efficiency?
- Can the training strategy, including the fine-tuning of only the manipulation policy head, be applied to other MLLMs?
- Is the performance in the manipulation tasks affected by the number of parameters in the corresponding head and how?
limitations: limitations: Limitations are discussed in the text.
paper_summary: paper_summary: This paper proposes RoboMamba, which applies the Mamba state space model architecture for robotic manipulation policy learning. Prior MLLM-based robot policy learning finetunes transformer-based models and suffers from two major problems: reasoning capabilities degrade with visual input; and training computational cost is high for end-to-end policy learning. RoboMamaba leverages the efficient Mamba architecture to improve learning efficiency and demonstrates simple vision encoder enables visual understanding. Similarly, a simple policy head is shown to be sufficient for generating performant robot behavior after RoboMamba had converged on robotic reasoning tasks.
strength_weakness: strength_weakness: The evaluation of robotic manipulation tasks is limited to one benchmark and contains only single-step open-loop action prediction (instead of learning a closed loop reactive policy). It is unclear how much training data is being used and how much variation there is in testing the policy.  At the same time, this model has many moving parts and design choices that were not carefully ablated. 

The presentation of this work can be improved in general for clarity. The authors could better motivate certain design choices as well as evaluation metrics.
questions: questions: What limits evaluating RoboMamba to predict one-step action? How does it generalize to multi-step or closed loop policy? Is inference spped, i.e. control frequency, the major bottleneck? 

How does RoboMamba compare with more structured LLM policies such as Code as Policies [1] and VoxPoser [2]?

[1] https://code-as-policies.github.io/
[2] https://voxposer.github.io/
limitations: limitations: see weakness
paper_summary: paper_summary: This work introduces RoboMamba to leverage SSM model’s capabilities in non-trivial sequence modeling with linear inference complexity. A simple policy head is employed for finetuning to enable RoboMamba to predict action poses. Evaluation is conducted both in simulation(SAPIEN) and real-world settings and shows consistent improvement compared to previous sota methods such as ManipLLM. Besides, fine-tuning parameters and time are significantly reduced and inference speeds achieve 7 times faster than existing models.
strength_weakness: strength_weakness: 1. In Table 1, RoboMamba(2.7B)’s performances on MME, MMB, MM-Vet are still below TinyLLaVA’s results.
2. In Table 2, RoboMamba does not achieve sota performance on some of the both training and test categories. 
3. Some minor mistakes like: In Figure 3(b), “Openfalmingo”
questions: questions: 1. Can RoboMamba support more complex robot manipulation tasks?
2. For delicate manipulation objects, RoboMamba seems have inferior performance in Table 2. Could you provide some analysis for this?
limitations: limitations: 1. As mentioned in Question 1, RoboMamba may not support more complex manipulation tasks.
2. RoboMamba only output 3D poses instead of direct policies.
paper_summary: paper_summary: This paper proposes a Mamba-based framework called RoboMamba that utilizes multimodal state space model for robotic reasoning and manipulation. It addresses the limitations of existing MLLMs in complex reasoning and high computational costs. RoboMamba integrates a vision encoder with the Mamba model to align visual data with language embedding, enabling robust visual reasoning and action capabilities. The model uses a simple lightweight policy head for action pose prediction, requiring minimal fine-tuning. Experiments show RoboMamba excels in reasoning and pose prediction tasks, achieving faster inference speeds and state-of-the-art performance in both simulated and real-world environments.
strength_weakness: strength_weakness: 1. Besides Mamba-like architecture, there exists many network architectures with linear computational complexity, like XCiT[1], xLSTM[2], hence my first concern is can these architectures work for the proposed framework? 
[1] Ali, Alaaeldin, et al. "Xcit: Cross-covariance image transformers." Advances in neural information processing systems 34 (2021): 20014-20027.
[2] Beck, Maximilian, et al. "xLSTM: Extended Long Short-Term Memory." arXiv preprint arXiv:2405.04517 (2024).

2. Following the first question, does Mamba has some unique attributes and advantages specially fit the robotic field? This work lack of related comparison and analysis.

3. The Fig.1 seems a bit of rough, for example, the formats and colors of arrows are inconsistent, some borders have jagged edges, and the overall resolution is not high enough.
questions: questions: Please refer to the weaknesses of the paper.
limitations: limitations: Please refer to the weaknesses of the paper.
paper_summary: paper_summary: This paper introduces RoboMamba, a Multimodal LLM, for robotic reasoning and low-level manipulation using the popular State-Space Model architecture. RoboMamba introduces a multi-stage joint language and vision training pipeline with robot-specific data fine-tuning. RoboMamba demonstrates strong reasoning performance on general and robot evaluation benchmarks while using a small amount of fine-tuned parameters.
strength_weakness: strength_weakness: - There is not much technical novelty in the method. It seems like RoboMamba is combining CLIP with SSMs and fine-tuning on robot-specific data (RoboVQA) which is a generic pipeline that several other MLLMs works have proposed. There are no new insights from the paper.
- The parameter-efficiency argument is mainly due to the use of SSM blocks instead of Transformer blocks. I think this is already obvious given the architecture design and is not a major contribution of this work.
- Using the term “real-world experiments” is confusing because it implies there is a physical robot executing a trained policy to perform some task which is not the case in this paper. Please provide quantitative results for the real-world reasoning experiments if possible and also provide some failure cases of your method otherwise it seems like the qualitative examples are cherry-picked.
- Please provide explanation as to why the RoboMamba training strategy may help learn better representation or capture knowledge that baselines or prior work fail to.
- Ablation study in Table 4 seem to suggest that the different fine-tuning strategies doesn’t seem to provide much benefit in terms of the general reasoning benchmarks. The improvement in downstream performance is minimal if you take out certain datasets. What about for the robot manipulation benchmarks?
- It would be nice to see experiments showing the scalability of RoboMamba, e.g. more SSM blocks, more data, etc.
questions: questions: - Why would the baselines perform worse than RoboMamba if they're trained on the same data, unless the main factor is the architectural choice?
- Similarly, why would RoboMamba perform better than general VLMs on those benchmarks in Table 1 and how is this comparable if they are not trained on the same data?
limitations: limitations: The authors mention that using MLLMs for task planning occasionally results in hallucinations.

Comments: title: Post-rebuttal comments
Post-rebuttal comments
thanks for the additional experiments
comment: We are pleased to have addressed your concerns. Based on your valuable feedback, we will incorporate the additional experiments and all extended ablation studies into the final version of our paper. Thank you once again for recognizing our work.
Thanks for the effort of making the response. The rebuttal and the supplemented experiments have basically addressed my concerns. The extended ablation studies in response to comments from other reviewers further verify the effectiveness of the proposed methods. Therefore, I will keep my score.
We greatly appreciate your recognition of our work. We will incorporate the additional experiments and detailed module descriptions into the final version. Thank you once again for your valuable time and feedback.
I thank the authors for their answers and clarifications. I think the additional experiments and results presented in their answers make the paper stronger, and they should be included in the final version of the paper. I retain my original rating, suggesting acceptance.
Dear Reviewer vV5n,

Sorry to bother you. We’re sending this comment to confirm whether our response has addressed your concerns. Please feel free to ask any remaining questions, and we’ll respond promptly. Thank you again for your valuable time and comments.

Best, Paper 5916 authors
Dear Reviewer QK1g,

As the discussion phase progresses, we would like to confirm whether our response has addressed your concerns. If you have any remaining questions, we would be happy to discuss and address them. Thank you once again for your valuable feedback.

Best, Paper 5916 authors
Thank you for acknowledging our work and rebuttal. We greatly appreciate your decision to upgrade the rating of our paper. We will incorporate all of your valuable suggestions into the revised version. Additionally, we will include the quantitative results of real-world experiments in the main text or appendix of our paper.
Thanks the authors for clarifying my concerns and adding the simulation experiments for closed-loop policy learning and comparing with VoxPoser. I am happy to raise my score to 6 but suggest the authors add additional comprehensive real-world experiments to have a broader impact.
Dear Reviewer iHpb,

We sincerely appreciate your recognition of our work. We will include the closed-loop experiments in the main text and appendix of our paper. May I know if it is possible for you to consider raising your rating above a borderline score if your concerns have been addressed. Thank you very much!

Paper 5916 authors
Thank you for your response to my concerns and the additional experiments you provided which greatly addressed my concerns. I will keep my score. Hopefully, closed-loop experiments will be presented in your final version as you mentioned in your answer to my limitation 2.
Dear Reviewer iHpb, 


As the discussion phase is nearing its conclusion, we would like to confirm whether our response adequately addresses your concerns. Feel free to inquire about any remaining questions, and we'll provide prompt responses. Thank you once again for your valuable comments.

Paper 5916 authors
Dear reviewer CsAK,

As the discussion phase is nearing its close, we would like to confirm whether our response adequately addresses your concerns. If you have any remaining questions, please don't hesitate to ask, and we'll respond promptly. Thank you for your valuable time and insightful comments.

Paper 5916 authors
Thank you for acknowledging the content and effort of our rebuttal. We also greatly appreciate you upgrading the rating of our paper. In the revised version, we will improve our paper according to your valuable feedback, particularly by including the scalability experiment (Rebuttal PDF) in the main text.  Finally, we will open-source our proposed all-round robotic MLLM system, providing the robotics community with a potential solution that balances high-level reasoning, low-level actions, and efficiency.
Thank you to the author for their additional experiments and clarifications in the rebuttal. 

First, in my opinion, the engineering effort in this work is good and comprehensive as SSM-based Transformers for robot MMLM is a reasonable thing to try. I would still argue that there is a lack of technical novelty as this seems almost a direct application of a new architecture design to a new problem domain. The benefits of efficiency, linear complexity, etc as described are due to the Mamba architecture. I also want to push back on the claim that combining general VQA data and robot specific-data is "innovative". Again, I feel that it is almost obvious that if we aggregate both types of data, then naturally you'll get the benefits of general reasoning and robot domain knowledge. 

However, I appreciate the author's efforts in addressing most of my other concerns and the new experiments demonstrating the scalability of their method and I will increase their score.