InteractAgent: Agentic Human Motion Interaction with Memory-Augmented Reflection
Long-horizon human-scene interaction requires coordinated spatial reasoning, physical feasibility, and the ability to recover from compounding execution errors. Text-driven motion models are largely scene-agnostic and lack closed-loop correction, while geometry-conditioned approaches depend on paired scene-motion training or explicit 3D preprocessing that limits generalization to novel layouts and open-vocabulary goals. We present InteractAgent, an agentic multimodal LLM system for long-horizon human-scene interaction that plans, executes, observes, and reflects in a closed loop, using a single MLLM as both planner and evaluator and a frozen MotionLLM as the executor. InteractAgent integr...
| Authors | Senhe Zhang, Tao Zhang, Yujia Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 October 2026 |
| Last updated at source | 20 October 2026 |
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