ManiAgent: An Agentic Framework for General Robotic Manipulation
This addresses the problem of complex reasoning and long-horizon task planning in robotics for researchers and practitioners, representing a novel method rather than an incremental improvement.
The paper tackles the limitations of Vision-Language-Action models in robotic manipulation by introducing ManiAgent, an agentic framework that achieves an 86.8% success rate on the SimplerEnv benchmark and 95.8% on real-world pick-and-place tasks.
While Vision-Language-Action (VLA) models have demonstrated impressive capabilities in robotic manipulation, their performance in complex reasoning and long-horizon task planning is limited by data scarcity and model capacity. To address this, we introduce ManiAgent, an agentic architecture for general manipulation tasks that achieves end-to-end output from task descriptions and environmental inputs to robotic manipulation actions. In this framework, multiple agents involve inter-agent communication to perform environmental perception, sub-task decomposition and action generation, enabling efficient handling of complex manipulation scenarios. Evaluations show ManiAgent achieves an 86.8% success rate on the SimplerEnv benchmark and 95.8% on real-world pick-and-place tasks, enabling efficient data collection that yields VLA models with performance comparable to those trained on human-annotated datasets. The project webpage is available at https://yi-yang929.github.io/ManiAgent/.