和谐移动操作
Harmonic Mobile Manipulation
AI总结：
针对导航与操作分离难以完成开门等协调任务的问题，提出端到端方法 HarmonicMM，联合优化两类能力，并通过仿真、真实公寓部署和新基准验证其泛化与实用潜力。
摘要：
近期机器人技术的进展已使机器人能够独立在复杂场景中导航或操作多样物体。然而，在开门等需要协调行为的许多家庭任务中，机器人仍然无能为力。将导航与操作分解处理虽对某些任务有效，但在需要协调动作的场景中会失败。为应对这一挑战，我们提出 HarmonicMM，这是一种同时优化导航和操作的端到端学习方法，在日常任务中相较现有技术表现出显著提升。该方法在仿真和真实世界环境中均得到验证，并能无需额外调参即可适应全新的未知环境。我们的贡献包括一个面向移动操作的新基准，以及仅凭 RGB 视觉观测在真实未知公寓中的成功部署，展示了在日常生活中实际室内机器人部署的潜力。更多结果见项目网站：https://rchalyang.github.io/HarmonicMM/
英文摘要：
Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently. However, robots are still impotent in many household tasks requiring coordinated behaviors such as opening doors. The factorization of navigation and manipulation, while effective for some tasks, fails in scenarios requiring coordinated actions. To address this challenge, we introduce, HarmonicMM, an end-to-end learning method that optimizes both navigation and manipulation, showing notable improvement over existing techniques in everyday tasks. This approach is validated in simulated and real-world environments and adapts to novel unseen settings without additional tuning. Our contributions include a new benchmark for mobile manipulation and the successful deployment with only RGB visual observation in a real unseen apartment, demonstrating the potential for practical indoor robot deployment in daily life. More results are on our project site: https://rchalyang.github.io/HarmonicMM/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
