RoboAgent: Chaining Basic Capabilities for Embodied Task Planning
This paper focuses on embodied task planning, where an agent acquires visual observations from the environment and executes atomic actions to accomplish a given task. Although recent Vision-Language Models (VLMs) have achieved impressive results in multimodal understanding and reasoning, their performance remains limited when applied to embodied planning that involves multi-turn interaction, long-horizon reasoning, and extended context analysis. To bridge this gap, we propose RoboAgent, a capability-driven planning pipeline in which the model actively invokes different sub-capabilities. Each capability maintains its own context, and produces intermediate reasoning results or interacts wit...
| Authors | Peiran Xu, Jiaqi Zheng, Yadong Mu |
|---|---|
| Primary category | cs.RO |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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