arXiv:2606. 27826v3 Announce Type: replace Abstract: Embodied agents driven by multimodal large language models (MLLMs) can often complete everyday tasks from visual observations, but goal achievement does not establish whether they proactively respect unstated social norms.
By Shiyun Zhao, Xinwei Song, Tianyu Guo, Xiaomeng Gao, Mingyuan Liu, Xu Han, Yuanyuan Zhang, Zhenliang Zhang, Xue Feng, Bo Dai
The paper "Inferring the Unspoken: Aligning Embodied Agents with Implicit Preferences" addresses the challenge of natural-language instructions that omit details needed for embodied action. It introduces the Preference-based Planning (PbP) benchmark, comprising 5,000 evaluation groups and 290 preferences across three levels, to systematically evaluate agents’ ability to infer latent user preferences from a few demonstrations. The authors propose the two-stage Inferring the Unspoken (InTU) framework, which first verbalizes inferred preferences from multimodal demonstrations and then generates action plans conditioned on that explicit representation, showing that explicit verbalization improves alignment and robustness compared to direct end-to-end planning.
By Manjie Xu, Xinyi Yang, Wei Liang, Chi Zhang, Yixin Zhu
The paper introduces FISER, a framework that explicitly infers human goals and intentions before planning actions for AI agents to follow natural language instructions in collaborative embodied tasks. It employs Transformer-based models and is evaluated on the HandMeThat benchmark, outperforming end-to-end approaches and strong baselines such as Chain of Thought prompting. FISER achieves state‑of‑the‑art performance on this embodied social reasoning task.
By Yanming Wan, Yue Wu, Yiping Wang, Jiayuan Mao, Natasha Jaques
arXiv:2609.08602v1 Announce Type: new
Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequenc...
By Tianyi Ma, Parisa Kordjamshidi
Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequences. However, fluent plans are not always executab...
arXiv:2603.26741v2 Announce Type: replace-cross
Abstract: Goal-conditioned visual navigation has been a long-standing testbed for embodied AI. We study a natural language-conditioned variant, languag...
By Yifei Dong, Fengyi Wu, Yilong Dai, Lingdong Kong, Guangyu Chen, Yetong Sha, Qiyu Hu, Feng Liu, Siyu Huang, Qi Dai, Zhi-Qi Cheng