两机器人协作抓取新框架，高效识别复杂环境可行抓法。
Cooperative Grasping for Collective Object Transport in Constrained Environments
- 用条件嵌入模型将抓取配置映射到向量空间，判断可行性。
- 在多种环境与物体下仿真验证，识别准确率高且泛化能力强。
- 适合多机器人协作运输场景，实机实验验证了实用性。
我们提出一种针对双机器人在受限环境中协同搬运物体的决策新框架。核心是基于条件嵌入（CE）模型，由两个神经网络将抓取配置信息映射至嵌入空间，生成的嵌入向量用于识别双机器人可协同搬运物体的可行抓取方案。为提升跨环境与物体形状的泛化能力，神经网络在包含多种环境地图与物体形状的数据集上进行训练。采用带负样本的监督学习方法，确保嵌入能有效区分可行与不可行抓取配置。仿真结果表明，该模型在各类环境与物体中均能可靠识别可行抓取方案。进一步通过物理机器人平台实验验证，确认了框架的实际可用性。
原文摘要 · Abstract (English)
We propose a novel framework for decision-making in cooperative grasping for two-robot object transport in constrained environments. The core of the framework is a Conditional Embedding (CE) model consisting of two neural networks that map grasp configuration information into an embedding space. The resulting embedding vectors are then used to identify feasible grasp configurations that allow two robots to collaboratively transport an object. To ensure generalizability across diverse environments and object geometries, the neural networks are trained on a dataset comprising a range of environment maps and object shapes. We employ a supervised learning approach with negative sampling to ensure that the learned embeddings effectively distinguish between feasible and infeasible grasp configurations. Evaluation results across a wide range of environments and objects in simulations demonstrate the model's ability to reliably identify feasible grasp configurations. We further validate the framework through experiments on a physical robotic platform, confirming its practical applicability.
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