REVIEW 5 major objections 5 minor 4 cited by
ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models
T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read ManipLVM-R1 shows that rule-based reinforcement rewards let a 3B vision-language model outperform supervised fine-tuning on robotic affordance and trajectory tasks while using only half the training data.
desk verdict Real in-domain gains with a useful RLVR recipe, but the OOD trajectory claim rests on a possibly overlapping benchmark and the annotation-free framing overstates. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The machinery is the pair of reward functions and the GRPO update that turns them into gradients. The Affordance Perception Reward is $R_{spatial}=R_{format}+R_{aff}$, with $R_{aff}=\mathrm{IoU}(\hat{b}, b^*)$ over predicted and ground-truth boxes. The Trajectory Match Reward is $R_{trajectory}=R_{format}+R_{path}+R_{end}$, where $R_{path}=R_{DFD}+R_{HD}+R_{RMSE}$ aggregates normalized Discrete Fréchet, Hausdorff, and RMSE distances and $R_{end}=\exp(-k\|\hat{p}_N-p^*_M\|_2)$ penalizes endpoint error. Per response, the reward is normalized into an advantage via mean and standard deviation across a group of sampled responses, and the policy is updated with a KL constraint to a reference model. The two rewards are the carriers of the argument because they inject spatial-logical constraints directly into the learning signal.
What would settle it
Measure the overlap between the VAIT OOD trajectory subset and the ShareRobot training split (scene identity, robot embodiment, or episode source), or compute nearest-neighbor image and trajectory distances from VAIT samples to ShareRobot training data. If the OOD samples are as close to training data as the in-domain test set is, the reported out-of-domain advantage is not an out-of-domain result.
Extended reading notes
Core claim
ManipLVM-R1 is put forward as an alternative to supervised fine-tuning for the two perception-to-action subtasks that dominate object manipulation: locating where an object can be interacted with, and predicting a plausible end-effector path. The method replaces reasoning-trace supervision with two dense rule-based rewards, an affordance reward built on format compliance plus bounding-box IoU, and a trajectory reward built on format compliance plus normalized Discrete Fréchet, Hausdorff, and RMSE distances and endpoint proximity, and optimizes a Qwen2.5-VL-3B policy with GRPO-style advantage normalization. On half of the ShareRobot training data, it reports an in-domain affordance IoU of 31.0 versus 12.69 for the strongest SFT baseline and an average trajectory error of 110.87 that is competitive with full-data models; on the OOD benchmarks it reports the strongest results among all compared models, including the 32B instruct model on trajectory error. The author's reading is that spatially and logically constrained rewards elicit physical reasoning that imitation learning from static CoT data does not.
Load-bearing premise
The load-bearing premise is that the VAIT subset is genuinely out of domain for a model trained on ShareRobot, even though both datasets are derived from Open X-Embodiment; if the domain shift is small, the reported OOD generalization is overstated.
Editorial extensions
If this is right
- Training cost and label cost can drop sharply, since 50% of ShareRobot is enough to beat full-data SFT baselines on the in-domain benchmark.
- Models trained this way should transfer OOD: ManipLVM-R1 reports the best or near-best scores on UMD affordance categories and VAIT trajectories, surpassing the 32B open model on trajectory error.
- Multi-metric trajectory rewards are worth composing: the ablation shows DFD+HD+RMSE+endpoint dominates DTW or single-metric variants across training.
- Reasoning can emerge without CoT annotations: the policy spontaneously produces multi-step reasoning traces, including an 'aha moment' during training.
Reading between the lines
- The 'no human annotations' framing is narrower than it sounds: the rewards are computed against ground-truth boxes and trajectories, so task-level annotations remain; what is eliminated is reasoning-trace annotation.
- Because ShareRobot and VAIT both draw from Open X-Embodiment, the real test is overlap; the OOD numbers should be read as upper bounds until the domain shift is quantified.
- The same reward design should be testable as a plug-in for other VLA backbones or as a fine-tuning stage after SFT; nothing in the method is specific to Qwen2.5-VL-3B.
- The paper's own limitation appendix confines the claim to 2D image-space outputs; a rigorous follow-up would measure whether the trajectory reward correlates with physical success in 3D control.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes ManipLVM-R1, an RLVR-based training framework for two robotic manipulation subtasks: affordance perception (bounding-box prediction) and trajectory prediction (2D waypoint sequences). The method samples multiple responses from a policy, scores them with rule-based rewards (an IoU-based affordance reward and a trajectory reward combining format, path-similarity, and endpoint terms), and optimizes with GRPO-style advantage normalization and a KL constraint. Experiments compare Qwen2.5-VL-3B trained with the proposed RLVR objective against open-source LVLMs and SFT baselines, using 50% of the ShareRobot training data for in-domain evaluation and UMD and VAIT subsets for out-of-domain evaluation. Reported results show an in-domain affordance IoU of 31.0 versus 12.69 for the strongest SFT baseline, and competitive trajectory errors on the in-domain and VAIT sets. The paper concludes that RLVR with structured rule-based rewards improves sample efficiency, physical reasoning, and OOD generalization without human annotations.
Significance. If the reported gains are robust, the paper would make a useful contribution by showing that GRPO-style RL with dense, rule-based spatial rewards can replace SFT on reasoning-augmented manipulation data and improve sample efficiency. The decomposition into affordance and trajectory rewards, the comparison against a large set of baselines, and the use of only half the training data are concrete and potentially falsifiable strengths. However, the central significance claims depend on two premises that the manuscript does not yet establish: that the VAIT subset is genuinely out-of-domain relative to the ShareRobot training data, and that the reward functions are annotation-free in the sense claimed. The paper also lacks uncertainty estimates, so the magnitude of the reported margins cannot be assessed against run-to-run variability. These are correctable with additional analysis rather than being fatal to the core idea.
major comments (5)
- [Section 4.2, Tables 1 and 2] The OOD trajectory benchmark is not convincingly out-of-domain. Section 4.2 states that ShareRobot is curated from Open X-Embodiment and that the OOD trajectory test set, VAIT, is also derived from Open X-Embodiment, but the paper does not demonstrate that the 500 VAIT validation samples are disjoint from the ShareRobot training episodes in terms of scenes, embodiments, camera viewpoints, or trajectory instances. If the 50% ShareRobot split contains trajectories from the same source episodes, the VAIT numbers in Table 2 (DFD 146.82, HD 140.52, RMSE 108.64) would be near-domain regression rather than evidence of generalization. The authors should report exact overlap statistics (e.g., episode or scene IDs) or replace the VAIT subset with a benchmark whose source distributions are provably disjoint from ShareRobot.
- [Section 3.2, Equations (3) and (5); Abstract; Section 1] The claim that the framework requires no human annotations is contradicted by the reward design. The affordance reward Raff = IoU(b*, \hat b) is computed against a ground-truth box, and the trajectory reward compares the prediction to a ground-truth trajectory T* using DFD, HD, and RMSE; these ground-truth labels originate from ShareRobot's human-verified annotations and, for the VAIT set, are manually corrected by the authors in Section 4.2. The method is annotation-free only with respect to reasoning-trace annotations, not with respect to task labels. Since the annotation-free claim is used in the abstract and introduction as a primary motivation, it should be substantially restated or the rewards should be replaced with labels that are not human-derived.
- [Section 4.2, VAIT OOD paragraph] The manual correction of the VAIT test annotations creates a risk of evaluation bias. The authors state that they visualized all trajectories in the 500-sample subset and manually corrected instances with conspicuous deviations; this means the test labels are partly authored by the same researchers who designed the reward, and the corrected labels may encode expectations about what a good trajectory should be rather than raw sensor observations. Reporting the fraction of corrected samples, the correction criteria, and ideally making the corrected test set public would be necessary to assess the OOD trajectory results.
- [Section 4.3 and 4.6; Equations (3)-(6)] Several quantities needed to reproduce the method are underspecified. The normalization of DFD, HD, and RMSE into scores in [0,1] is described only verbally, without the functional form or the reference scales; the decay coefficient k in Eq. (5) is introduced but its value is not given; and the number of sampled responses G and the KL coefficient beta in Eq. (1) are not reported. Without these details, the reward design is not reproducible and the ablation in Figure 5 cannot be interpreted quantitatively. The authors should provide a table of hyperparameters and explicit normalization formulas.
- [Tables 1 and 2] All experimental results are reported as single runs with no error bars, confidence intervals, or significance tests. The margins in Table 1 are large, but the trajectory metrics in Table 2 are closer (e.g., ManipLVM-R1's DFD 146.82 versus Qwen2.5-VL-32B's 182.73), and the affordance IoU scores on OOD subtasks like Cut-IoU show only a 0.21 point difference between ManipLVM-R1 and RoboBrain-7B. Reporting the mean and variance over at least three seeds for the proposed method and the main baselines would allow the claimed gains to be distinguished from optimization noise.
minor comments (5)
- [Section 3.2, Affordance Perception Reward] The notation is inconsistent: the text says Raff compares the predicted box b* with the ground-truth box \hat b, while the caption of Figure 2 and Equation (3) use the opposite order (\hat b predicted, b* ground truth). This should be unified to avoid ambiguity.
- [Table 1 and Table 2] There are several typos in method names: 'Insturct' should be 'Instruct', 'LLaV A' should be 'LLaVA', and 'ShareRoBot' should be 'ShareRobot'. The duplicated Qwen2.5-VL entries in Table 1 and the inconsistent formatting of the Qwen2.5-VL-32B row in Table 2 also need cleanup.
- [Section 4.2] The description of the UMD OOD dataset says 1200 data pairs/samples are sampled with equal distribution across four categories, but the table reports per-category IoU; the authors should clarify whether the 1200 refers to images or instruction-image pairs and how the per-category test sizes are computed.
- [Section 4.6, Figure 5] The normalized performance metric on the vertical axis is defined only informally ('we normalize and negate these values'); specifying the normalization baseline (e.g., relative to initial or final values) would make the ablation curves interpretable.
- [References] The reference list includes two entries for 'Qwen2-vl' with identical author lists and titles (Wang et al., 2024a and 2024b); one should be removed or the in-text citation points should be disambiguated. The citation to 'Hershey, Olsen, 2007' also appears without the authors' initials in the text.
Circularity Check
The headline 'no human annotations' claim reduces by construction because the verifiable rewards are ground-truth boxes and trajectories; the OOD trajectory benchmark also shares Open X-Embodiment as its source with the training data.
-
self definitional
[Section 3.1, Eq. (2); Section 3.2, 'Affordance Perception Reward' and 'Trajectory Match Reward']
"checks if the ground-truth answer remains the same as the prediction o: R(q,o)= 1, if o = ground truth, 0, otherwise. / It evaluates spatial accuracy by comparing the predicted bounding box b∗ with the ground-truth box ˆb using the Intersection-over-Union (IoU) metric. / The predicted trajectory is evaluated against a ground-truth sequence T ∗ using three components: a format reward Rformat, a path similarity reward Rpath, and an endpoint distance reward Rend."
The 'verifiable' reward is defined by exact match to ground truth (Eq. 2), and the two task rewards are direct similarity functions to ground-truth boxes (IoU) and ground-truth trajectories (DFD/HD/RMSE). The headline claim that RLVR 'removes the dependence on costly annotations' and 'replaces traditional supervision' is therefore contradicted by construction: the reward is the human-annotated label. Optimizing these rewards is equivalent to fitting the training annotations; the reported affordance/trajectory outputs are driven toward the labels that are the method's input, not verified against an independent signal.
-
other
[Section 4.2, 'Out of Domain Dataset' and 'In Domain Dataset']
"For the trajectory prediction task, we designated a randomly selected subset of 500 samples from the validation data of LLARVA’s pre-training dataset, VAIT, as our OOD test set. VAIT, derived from the diverse Open X-Embodiment dataset, encompasses image-visual trace pairs across numerous robotic scenarios. / The data was curated from Open X-Embodiment (O’Neill et al., 2024) using strict quality criteria and human verification."
ShareRobot (training) and VAIT (OOD test) are both derived from Open X-Embodiment. The paper does not demonstrate disjointness in episodes, embodiments, scenes, or viewpoints between the 50% ShareRobot training split and the 500 VAIT samples. Therefore the reported OOD trajectory scores (DFD 146.82, HD 140.52, RMSE 108.64) may reflect near-domain regression rather than out-of-distribution generalization; the 'OOD' label is assigned rather than established, partially reducing the generalization claim to an evaluation on the same underlying source distribution.
full rationale
The in-domain empirical comparison is largely self-contained and could be valid: RL with dense rewards can outperform SFT on held-out ShareRobot data, and that result is not circular. However, the central advertised contribution—replacing human supervision with verifiable rewards—is undermined by the paper's own definitions: Eq. (2) equates the reward with ground-truth matching, and Raff/Rtrajectory are IoU and distance functions against human-verified GT annotations from ShareRobot. This is a self-definitional reduction of the no-annotation claim, not merely a wording issue. Separately, the OOD trajectory test is drawn from VAIT, which is, like ShareRobot, 'derived from the diverse Open X-Embodiment dataset'; without an explicit disjointness analysis, the OOD generalization numbers may be partly in-domain. The RoboBrain self-citation (shared author Yuheng Ji) is used for the dataset and baseline but is not the main circularity. Overall, the in-domain affordance gain (31.0 vs 12.69 IoU) retains independent content, so the paper is not wholly circular, but the flagship claims of annotation-free learning and OOD generalization are partially forced by construction.
Assumptions & free parameters
free parameters (3)
- k (decay coefficient in endpoint reward) =
not reported
- Distance-to-score normalization parameters for DFD/HD/RMSE =
not reported
- Number of sampled responses G and KL coefficient beta =
not reported
assumptions (4)
- standard math GRPO policy update with KL constraint is a valid optimizer for these reward functions
- domain assumption IoU and trajectory distances are sufficient proxies for manipulation task success
- domain assumption Ground-truth affordance boxes and trajectories in ShareRobot are accurate and complete
- domain assumption VAIT and UMD are out-of-domain relative to ShareRobot
Cite this review
Pith. "Pith review of ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models." pith.science (2026). https://pith.science/paper/PGOMFKOH
@misc{pith2026250516517,
author = {Pith},
title = {Pith review of: ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models},
year = {2026},
howpublished = {\url{https://pith.science/paper/PGOMFKOH}},
note = {Machine review of arXiv:2505.16517}
}
read the original abstract
Large Vision-Language Models (LVLMs) have recently advanced robotic manipulation by leveraging vision for scene perception and language for instruction following. However, existing methods rely heavily on costly human-annotated training datasets, which limits their generalization and causes them to struggle in out-of-domain (OOD) scenarios, reducing real-world adaptability. To address these challenges, we propose ManipLVM-R1, a novel reinforcement learning framework that replaces traditional supervision with Reinforcement Learning using Verifiable Rewards (RLVR). By directly optimizing for task-aligned outcomes, our method enhances generalization and physical reasoning while removing the dependence on costly annotations. Specifically, we design two rule-based reward functions targeting key robotic manipulation subtasks: an Affordance Perception Reward to enhance localization of interaction regions, and a Trajectory Match Reward to ensure the physical plausibility of action paths. These rewards provide immediate feedback and impose spatial-logical constraints, encouraging the model to go beyond shallow pattern matching and instead learn deeper, more systematic reasoning about physical interactions.
Figures
Figures from the paper (1 more)
Forward citations
Cited by 4 Pith papers
-
Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation
A hierarchical RL-MPC framework with a 'contact intention' interface achieves data-efficient, robust non-prehensile manipulation that transfers zero-shot to a real robot.
-
PresentAgent: Multimodal Agent for Presentation Video Generation
PresentAgent chains LLM segmentation, slide rendering, TTS, and ffmpeg to turn documents into narrated presentation videos, but the human-level claim rests on five documents and an unvalidated VLM judge.
-
Nav-R1: Reasoning and Navigation in Embodied Scenes
Nav-R1 uses a 110K synthetic CoT dataset, GRPO with three rewards, and a fast-in-slow system to set new SOTA on R2R-CE, RxR-CE, and HM3D-OVON.
-
LaViPlan : Language-Guided Visual Path Planning with RLVR
LaViPlan uses RLVR with GRPO and ADE/FDE rewards to fine-tune a 2B VLM for trajectory prediction, improving ADE/FDE on ROADWork and a normalized safety score on CODA-LM over supervised fine-tuning.
Reference graph
Works this paper leans on
-
[1]
Abouelenin Abdelrahman, Ashfaq Atabak, Atkinson Adam, Awadalla Hany, Bach Nguyen, Bao Jianmin, Benhaim Alon, Cai Martin, Chaudhary Vishrav, Chen Congcong, others . Phi-4-mini technical report: Compact yet powerful multimodal language models via mixture-of-loras // arXiv preprint arXiv:2503.01743
-
[3]
3292–3310. Liu Chris Yuhao, Zeng Liang, Liu Jiacai, Yan Rui, He Jujie, Wang Chaojie, Yan Shuicheng, Liu Yang, Zhou Yahui. Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs // arXiv preprint arXiv:2410.18451. 2024a. Liu Haotian, Li Chunyuan, Li Yuheng, Lee Yong Jae. Improved baselines with visual instruction tuning // Proceedings of the IEEE/CVF Co...
-
[7]
5880–5886. Guo Daya, Yang Dejian, Zhang Haowei, Song Junxiao, Zhang Ruoyu, Xu Runxin, Zhu Qihao, Ma Shirong, Wang Peiyi, Bi Xiao, others. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning // arXiv preprint arXiv:2501.12948
-
[8]
MedINST: Meta Dataset of Biomedical Instructions
Han Wenhan, Fang Meng, Zhang Zihan, Yin Yu, Song Zirui, Chen Ling, Pechenizkiy Mykola, Chen Qingyu. MedINST: Meta Dataset of Biomedical Instructions // arXiv preprint arXiv:2410.13458
-
[9]
379–389. Hershey John R, Olsen Peder A. Approximating the Kullback Leibler divergence between Gaussian mixture models // 2007 IEEE International Conference on Acoustics, Speech and Signal Processing- ICASSP’07
work page 2007
-
[11]
850–863. Ji Yuheng, Tan Huajie, Shi Jiayu, Hao Xiaoshuai, Zhang Yuan, Zhang Hengyuan, Wang Pengwei, Zhao Mengdi, Mu Yao, An Pengju, others. Robobrain: A unified brain model for robotic manipulation from abstract to concrete // arXiv preprint arXiv:2502.21257
-
[12]
OpenVLA: An Open-Source Vision-Language-Action Model // arXiv preprint arXiv:2406.09246
Kim Moo Jin, Pertsch Karl, Karamcheti Siddharth, Xiao Ted, Balakrishna Ashwin, Nair Suraj, Rafailov Rafael, Foster Ethan, Lam Grace, Sanketi Pannag, others. OpenVLA: An Open-Source Vision-Language-Action Model // arXiv preprint arXiv:2406.09246
-
[13]
Lambert Nathan, Morrison Jacob, Pyatkin Valentina, Huang Shengyi, Ivison Hamish, Brahman Faeze, Miranda Lester James V , Liu Alisa, Dziri Nouha, Lyu Shane, others. T \" ULU 3: Pushing Frontiers in Open Language Model Post-Training // arXiv preprint arXiv:2411.15124
Show all 32 references
-
[14]
Llava-onevision: Easy visual task transfer // arXiv preprint arXiv:2408.03326
Li Bo, Zhang Yuanhan, Guo Dong, Zhang Renrui, Li Feng, Zhang Hao, Zhang Kaichen, Li Yanwei, Liu Ziwei, Li Chunyuan. Llava-onevision: Easy visual task transfer // arXiv preprint arXiv:2408.03326. 2024a. Li Chenliang, Xu Haiyang, Tian Junfeng, Wang Wei, Yan Ming, Bi Bin, Ye Jiab...
-
[21]
Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0 // 2024 IEEE International Conference on Robotics and Automation (ICRA)
O’Neill Abby, Rehman Abdul, Maddukuri Abhiram, Gupta Abhishek, Padalkar Abhishek, Lee Abra- ham, Pooley Acorn, Gupta Agrim, Mandlekar Ajay, Jain Ajinkya, others. Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0 // 2024 IEEE Intern...
2024
-
[22]
12 Peng Yingzhe, Zhang Gongrui, Zhang Miaosen, You Zhiyuan, Liu Jie, Zhu Qipeng, Yang Kai, Xu Xingzhong, Geng Xin, Yang Xu
6892–6903. 12 Peng Yingzhe, Zhang Gongrui, Zhang Miaosen, You Zhiyuan, Liu Jie, Zhu Qipeng, Yang Kai, Xu Xingzhong, Geng Xin, Yang Xu. Lmm-r1: Empowering 3b lmms with strong reasoning abilities through two-stage rule-based rl // arXiv preprint arXiv:2503.07536
-
[23]
Vlm-r1: A stable and generalizable r1-style large vision-language model // arXiv preprint arXiv:2504.07615
Shen Haozhan, Liu Peng, Li Jingcheng, Fang Chunxin, Ma Yibo, Liao Jiajia, Shen Qiaoli, Zhang Zilun, Zhao Kangjia, Zhang Qianqian, others . Vlm-r1: A stable and generalizable r1-style large vision-language model // arXiv preprint arXiv:2504.07615
-
[24]
Mmac-copilot: Multi-modal agent collaboration operating system copilot // arXiv e-prints
Song Zirui, Li Yaohang, Fang Meng, Chen Zhenhao, Shi Zecheng, Huang Yuan . Mmac-copilot: Multi-modal agent collaboration operating system copilot // arXiv e-prints. 2024a. arXiv–2404. Song Zirui, Ouyang Guangxian, Fang Meng, Na Hongbin, Shi Zijing, Chen Zhenhao, Fu Yujie, Zhan...
-
[25]
Gemma 3 technical report // arXiv preprint arXiv:2503.19786
Team Gemma, Kamath Aishwarya, Ferret Johan, Pathak Shreya, Vieillard Nino, Merhej Ramona, Perrin Sarah, Matejovicova Tatiana, Ramé Alexandre, Rivière Morgane, others . Gemma 3 technical report // arXiv preprint arXiv:2503.19786. 2025a. Team Kimi, Du Angang, Gao Bofei, Xing Bow...
-
[26]
Wang Chenxi, Gu Tianle, Wei Zhongyu, Gao Lang, Song Zirui, Chen Xiuying
3891–3902. Wang Chenxi, Gu Tianle, Wei Zhongyu, Gao Lang, Song Zirui, Chen Xiuying. Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia // arXiv preprint arXiv:2503.01714. 2025a. Wang Peng, Bai Shuai, Tan Sinan, Wang Shijie, Fan Zhihao, Bai Jinze, Chen Keqin, L...
-
[27]
Wang Yanbo, Ye Jiayi, Wu Siyuan, Gao Chujie, Huang Yue, Chen Xiuying, Zhao Yue, Zhang Xiangliang. TRUSTEV AL: A Dynamic Evaluation Toolkit on Trustworthiness of Generative Foundation Models // Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Ass...
2025
-
[28]
Zang Yuhang, Dong Xiaoyi, Zhang Pan, Cao Yuhang, Liu Ziyu, Ding Shengyuan, Wu Shenxi, Ma Yubo, Duan Haodong, Zhang Wenwei, others
204–213. Zang Yuhang, Dong Xiaoyi, Zhang Pan, Cao Yuhang, Liu Ziyu, Ding Shengyuan, Wu Shenxi, Ma Yubo, Duan Haodong, Zhang Wenwei, others. A Simple Yet Effective Multi-Modal Reward Model // arXiv preprint arXiv:2501.12368
-
[29]
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks // arXiv preprint arXiv:2503.21696
Zhang Wenqi, Wang Mengna, Liu Gangao, Huixin Xu, Jiang Yiwei, Shen Yongliang, Hou Guiyang, Zheng Zhe, Zhang Hang, Li Xin, others . Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks // arXiv preprint arXiv:2503.21696
-
[30]
Embodied-R: Collaborative Framework for Activating Em- bodied Spatial Reasoning in Foundation Models via Reinforcement Learning // arXiv preprint arXiv:2504.12680
Zhao Baining, Wang Ziyou, Fang Jianjie, Gao Chen, Man Fanhang, Cui Jinqiang, Wang Xin, Chen Xinlei, Li Yong, Zhu Wenwu. Embodied-R: Collaborative Framework for Activating Em- bodied Spatial Reasoning in Foundation Models via Reinforcement Learning // arXiv preprint arXiv:2504.12680
-
[31]
Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection // arXiv preprint arXiv:2412.04455
Zhou Enshen, Su Qi, Chi Cheng, Zhang Zhizheng, Wang Zhongyuan, Huang Tiejun, Sheng Lu, Wang He. Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection // arXiv preprint arXiv:2412.04455
-
[32]
The current framework focuses on affordance perception and 2D trajectory prediction
A Limitation While ManipLVM-R1 demonstrates significant advancements in robotic manipulation through rein- forcement learning with verifiable rewards, two limitations should be acknowledged. The current framework focuses on affordance perception and 2D trajectory prediction. R...
2022
-
[36]
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation // arXiv preprint arXiv:2405.17418
Liu Jiaming, Li Chenxuan, Wang Guanqun, Lee Lily, Zhou Kaichen, Chen Sixiang, Xiong Chuyan, Ge Jiaxin, Zhang Renrui, Zhang Shanghang. Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation // arXiv preprint arXiv:2405.17418. 2024d. Liu Jiaming, Liu Me...
-
[229]
2023a. 540–562. (Proceedings of Machine Learning Research). Huang Wenlong, Xia Fei, Xiao Ted, Chan Harris, Liang Jacky, Florence Pete, Zeng Andy, Tompson Jonathan, Mordatch Igor, Chebotar Yevgen, others . Inner Monologue: Embodied Reasoning through Planning with Language Model...
-
[1994]
Rlafford: End-to-end affordance learning for robotic manipulation // 2023 IEEE International Conference on Robotics and Automation (ICRA)
10 Geng Yiran, An Boshi, Geng Haoran, Chen Yuanpei, Yang Yaodong, Dong Hao. Rlafford: End-to-end affordance learning for robotic manipulation // 2023 IEEE International Conference on Robotics and Automation (ICRA)
2023
-
[2007]
Nazeri Mohammad Hossein, Bohlouli Mahdi
69–84. Nazeri Mohammad Hossein, Bohlouli Mahdi. Exploring reflective limitation of behavior cloning in autonomous vehicles // 2021 IEEE International Conference on Data Mining (ICDM)
2021
-
[2015]
Azzolini Alisson, Brandon Hannah, Chattopadhyay Prithvijit, Chen Huayu, Chu Jinju, Cui Yin, Diamond Jenna, Ding Yifan, Ferroni Francesco, Govindaraju Rama, others
2425–2433. Azzolini Alisson, Brandon Hannah, Chattopadhyay Prithvijit, Chen Huayu, Chu Jinju, Cui Yin, Diamond Jenna, Ding Yifan, Ferroni Francesco, Govindaraju Rama, others. Cosmos-reason1: From physical common sense to embodied reasoning // arXiv preprint arXiv:2503.15558
-
[2017]
Niu Dantong, Sharma Yuvan, Biamby Giscard, Quenum Jerome, Bai Yutong, Shi Baifeng, Darrell Trevor, Herzig Roei
5908–5915. Niu Dantong, Sharma Yuvan, Biamby Giscard, Quenum Jerome, Bai Yutong, Shi Baifeng, Darrell Trevor, Herzig Roei. LLARV A: Vision-Action Instruction Tuning Enhances Robot Learning // arXiv preprint arXiv:2406.11815
-
[2021]
Nguyen Anh, Kanoulas Dimitrios, Caldwell Darwin G, Tsagarakis Nikos G
1252–1257. Nguyen Anh, Kanoulas Dimitrios, Caldwell Darwin G, Tsagarakis Nikos G. Object-based affordances detection with convolutional neural networks and dense conditional random fields // 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2017
-
[2022]
11 Li Junnan, Li Dongxu, Savarese Silvio, Hoi Steven
7241–7259. 11 Li Junnan, Li Dongxu, Savarese Silvio, Hoi Steven . Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large language models // ICML. 2023a. 19730–19742. Li Xiaoqi, Zhang Mingxu, Geng Yiran, Geng Haoran, Long Yuxing, Shen Yan, Zhang...
-
[2023]
Rt-h: Action Hierarchies Using Language // arXiv preprint arXiv:2403.01823
Belkhale Suneel, Ding Tianli, Xiao Ted, Sermanet Pierre, Vuong Quon, Tompson Jonathan, Chebotar Yevgen, Dwibedi Debidatta, Sadigh Dorsa. Rt-h: Action Hierarchies Using Language // arXiv preprint arXiv:2403.01823
-
[2024]
Rt-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control // arXiv preprint arXiv:2307.15818
Brohan Anthony, Brown Noah, Carbajal Justice, Chebotar Yevgen, Chen Xi, Choromanski Krzysztof, Ding Tianli, Driess Danny, Dubey Avinava, Finn Chelsea, others. Rt-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control // arXiv preprint arXiv:2307.15818. 2023...
-
[2025]
Qwen technical report // arXiv preprint arXiv:2309.16609
Bai Jinze, Bai Shuai, Chu Yunfei, Cui Zeyu, Dang Kai, Deng Xiaodong, Fan Yang, Ge Wenbin, Han Yu, Huang Fei, others. Qwen technical report // arXiv preprint arXiv:2309.16609
Reviewed August 7, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.