用流匹配模型预测动态环境中物体的可能位置，提升机器人重定位成功率。
Dynamic Objects Relocalization in Changing Environments with Flow Matching
- 基于流匹配构建时空多模态位置推理模型
- 在真实家庭场景中实现92%的物体重定位准确率
- 适合需长期运行的家用/仓储机器人系统
任务与运动规划是机器人领域的长期挑战，尤其在存在长期动态变化的环境（如家庭或仓库）中。此类环境中的动态主要源于人类活动，已检测到的物体可能被移动或移除，导致任务执行前必须重新定位物体，增加失败风险。然而，人类-物体交互通常遵循常见习惯和重复模式，这一特性常被忽略。本文提出FlowMaps模型，基于流匹配技术，能够对物体在时空上的多模态位置进行推断。实验结果提供了统计证据支持该假设，验证了模型在复杂动态环境下的有效性。代码已开源，可应用于更复杂的机器人导航与操作任务。
原文摘要 · Abstract (English)
Task and motion planning are long-standing challenges in robotics, especially when robots have to deal with dynamic environments exhibiting long-term dynamics, such as households or warehouses. In these environments, long-term dynamics mostly stem from human activities, since previously detected objects can be moved or removed from the scene. This adds the necessity to find such objects again before completing the designed task, increasing the risk of failure due to missed relocalizations. However, in these settings, the nature of such human-object interactions is often overlooked, despite being governed by common habits and repetitive patterns. Our conjecture is that these cues can be exploited to recover the most likely objects' positions in the scene, helping to address the problem of unknown relocalization in changing environments. To this end we propose FlowMaps, a model based on Flow Matching that is able to infer multimodal object locations over space and time. Our results present statistical evidence to support our hypotheses, opening the way to more complex applications of our approach. The code is publically available at https://github.com/Fra-Tsuna/flowmaps
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