Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Language-Conditioned Open-Vocabulary Mobile Manipulation with Pretrained Models
Authors: Shen Tan, Dong Zhou, Xiangyu Shao, Junqiao Wang, Guanghui Sun
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments simulated in complex household environments show strong zero-shot generalization and multi-task learning abilities of LOVMM. Moreover, our approach can also generalize to multiple tabletop manipulation tasks and achieve better success rates compared to other state-of-the-art methods. |
| Researcher Affiliation | Academia | Shen Tan , Dong Zhou , Xiangyu Shao , Junqiao Wang , Guanghui Sun Harbin Institute of Technology EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed method and architecture using text, mathematical equations, and figures (Figure 2 and Figure 3), but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present any code-like formatted steps. |
| Open Source Code | Yes | 1The source code, dataset, and supplementary material are available at: https://github.com/shentan-shiina/LOVMM. |
| Open Datasets | Yes | 1The source code, dataset, and supplementary material are available at: https://github.com/shentan-shiina/LOVMM. |
| Dataset Splits | Yes | The models are trained for 600K steps across all seen tasks using n = 1, 10, 100 expert demonstrations in multi-task settings following CLIPort benchmark [Shridhar et al., 2022]. Then we evaluate the models on 100 seen tasks and use the best validation model to test on 100 unseen tasks. |
| Hardware Specification | Yes | All models are trained on 4 NVIDIA RTX 4090 GPUs. |
| Software Dependencies | No | The paper mentions several models and frameworks used (e.g., GPT-4, VLMaps, CLIP, Transporter network, LSeg) but does not specify the version numbers of any underlying software libraries, programming languages, or specific ancillary tools used for implementation. |
| Experiment Setup | Yes | The models are trained for 600K steps across all seen tasks using n = 1, 10, 100 expert demonstrations in multi-task settings following CLIPort benchmark [Shridhar et al., 2022]. The model is trained with cross-entropy loss for 2D manipulation, and a Huber loss for 3D manipulation. We use c = 64, k = 36 and d = 3, and d = 24 for feature channel dimensions. |