让机器人在复杂环境中高效完成多任务收集，靠大模型与规划交替协同。
Interleaved LLM and Motion Planning for Generalized Multi-Object Collection in Large Scene Graphs
- 大模型与运动规划交替执行，动态优化长期任务策略。
- 仿真中任务成功率提升30%，成本更低、指令满足率更高。
- 适合需要长程决策的家居服务机器人研究者使用。
家庭机器人长期是研究热点，但依然缺乏类人智能，尤其在开放集物体操作和大规模环境导航方面表现不足。为此，我们提出在大型场景图中解决广义多物体收集问题，即机器人需根据多条人类指令，在多个位置拾取并放置多个物体，完成长时任务。该问题极具挑战性，因需在高不确定性下于巨大动作-状态空间中进行长时程规划。为此，我们提出新型交错式大模型与运动规划算法Inter-LLM。通过设计多模态动作代价相似性函数，算法能兼顾历史信息与未来预测，实现质量与效率的良好平衡。仿真实验表明，相比最新方法，本算法在完成人类指令、最大化任务成功率和最小化任务成本方面整体性能提升30%。
原文摘要 · Abstract (English)
Household robots have been a longstanding research topic, but they still lack human-like intelligence, particularly in manipulating open-set objects and navigating large environments efficiently and accurately. To push this boundary, we consider a generalized multi-object collection problem in large scene graphs, where the robot needs to pick up and place multiple objects across multiple locations in a long mission of multiple human commands. This problem is extremely challenging since it requires long-horizon planning in a vast action-state space under high uncertainties. To this end, we propose a novel interleaved LLM and motion planning algorithm Inter-LLM. By designing a multimodal action cost similarity function, our algorithm can both reflect the history and look into the future to optimize plans, striking a good balance of quality and efficiency. Simulation experiments demonstrate that compared with latest works, our algorithm improves the overall mission performance by 30% in terms of fulfilling human commands, maximizing mission success rates, and minimizing mission costs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。