| title | ComBodied Agents: a New Paradigm of Human-Centric Agentic AI |
|---|---|
| authors | Qianggang Ding, Xingyao Wang, Rui Feng, Zhibin Wang, Feixiang Wang, Kelong Mao, Hao Sun, Zhiyao Luo, Jiankai Tang, Lei Li, Jiadong Guo, Minheng Ni, Weicong Lin, Chenxi Yang, Hongxiang Gao, Zhenghua Chen, Yang Bai, Min Wu, Jun Cheng, Huazhu Fu, Dacheng Tao, Bang Liu |
| date | 2026-08-11 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.10915 |
| upvotes | 94 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| primer | World models |
| abstract | After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person forgot, is confused, has side effects, or deliberately refused, nor what support is appropriate. This reveals a structural gap in Agentic AI: Digital Agents primarily transform software states, while Embodied Agents transform physical states; neither makes a person's evolving state and agency the primary object of modeling, intervention, and evaluation. We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time, using software tools, sensors, wearables, robots, and human services as action channels rather than end goals. We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop: event-based multimodal perception reconstructs meaningful personal events; longitudinal, correctable memory provides temporal context; Personal World Models estimate future personal states and outcomes under alternative decisions and interventions; and an admissible intervention policy selects proportionate support under consent, uncertainty, safety, reversibility, and user control. Feedback from the person and environment updates the loop. Rather than requiring an exhaustive Human Digital Twin, the framework uses purpose-bounded, uncertainty-aware, user-correctable representations. We organize the design space by human-state targets, relational contexts, and agent roles, and propose scenario-centered evaluation, agency-preservation metrics, benchmark requirements, edge-native personal models, and governance directions. Combodied Agents shift Agentic AI from external task completion toward sustained human benefit. |
cite
@misc{ding2026combodied,
title={ComBodied Agents: a New Paradigm of Human-Centric Agentic AI},
author={Qianggang Ding and Xingyao Wang and Rui Feng and Zhibin Wang and Feixiang Wang and Kelong Mao and Hao Sun and Zhiyao Luo and Jiankai Tang and Lei Li and Jiadong Guo and Minheng Ni and Weicong Lin and Chenxi Yang and Hongxiang Gao and Zhenghua Chen and Yang Bai and Min Wu and Jun Cheng and Huazhu Fu and Dacheng Tao and Bang Liu},
year={2026},
eprint={2608.10915},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.10915}
}