EMPOWER：具备在线接地与执行能力的具身多角色开放词汇规划
EMPOWER: Embodied Multi-role Open-vocabulary Planning with Online Grounding and Execution
AI总结：
EMPOWER框架利用预训练基础模型和多角色机制，解决机器人在现实环境中开放词汇在线接地与规划问题，在TIAGo机器人上六种场景中平均成功率达0.73。
摘要：
机器人在现实环境中的任务规划面临重大挑战。这些挑战源于三个主要问题：难以识别实现目标所需的接地步骤序列；高层动作与低层命令之间缺乏标准化映射；以及在机器人硬件资源有限的情况下保持低计算开销的困难。我们提出EMPOWER，这是一个为具身智能体设计的开放词汇在线接地与规划框架，旨在解决上述问题。通过利用高效的预训练基础模型和多角色机制，EMPOWER在接地规划与执行方面展现出显著改进。定量结果凸显了我们方法的有效性，在使用TIAGo机器人的六种不同现实场景中取得了0.73的平均成功率。
英文摘要：
Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of steps to achieve a goal; the lack of a standardized mapping between high-level actions and low-level commands; and the challenge of maintaining low computational overhead given the limited resources of robotic hardware. We introduce EMPOWER, a framework designed for open-vocabulary online grounding and planning for embodied agents aimed at addressing these issues. By leveraging efficient pre-trained foundation models and a multi-role mechanism, EMPOWER demonstrates notable improvements in grounded planning and execution. Quantitative results highlight the effectiveness of our approach, achieving an average success rate of 0.73 across six different real-life scenarios using a TIAGo robot.
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。
