This August 2025 paper introduces ODYSSEY, a comprehensive framework for open-world mobile manipulation that integrates robotic mobility, manipulation, and real-time perception. It highlights a novel approach that uses large language models for high-level task planning and vision-language models for fine-grained action guidance, enabling robots to adaptively interact in complex environments. A significant contribution is the first comprehensive benchmark for long-horizon mobile manipulation, featuring diverse daily tasks in both indoor and outdoor settings to thoroughly evaluate embodied reasoning, planning, navigation, and manipulation capabilities. The system demonstrates strong sim-to-real transfer performance, although challenges with precise control and robust perception in real-world scenarios are identified. The paper details the coarse-to-fine task planner and a reinforcement learning-based whole-body policy, both crucial for generalizing across varied terrains and overcoming the gap between simulation and reality.
Source:
https://arxiv.org/pdf/2508.08240
