发表机构
Autonomous Systems Lab; ETH Zurich; Robotics Systems Lab（自主系统实验室; 苏黎世联邦理工学院; 机器人系统实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于记忆与演示的持续学习框架，用于单视图6自由度抓取合成，在未见物体上在线提升性能，经1500余次试验验证，50次尝试后成功率超90%。
AI 中文摘要
当前大多数抓取合成系统在离线状态下训练，并在部署期间保持固定。虽然当部署条件与训练数据相似时效果良好，但当机器人遇到从未见过的条件（如不熟悉的物体）时，性能可能会下降。在这项工作中，我们提出了一种针对杂乱场景中平行颚夹持器的单视图6自由度抓取合成的持续学习框架。我们的方法不是微调一个大型参数模型，而是通过在学习到的嵌入空间中的记忆进行适应：抓取结果更新未来的抓取分数，而可选的用户演示被召回并转移到新场景中作为额外的候选抓取。我们在仿真和包含超过1500次抓取试验的广泛真实世界实验中评估了我们的方法。我们表明，即使在适应之前，我们的方法也能匹配现有6自由度抓取基线的性能，在训练期间缺失或代表性不足的类别中的未见物体上在线改进，并支持具有有限遗忘的长时域持续学习。在真实世界实验中，我们的方法在仅50次在线抓取尝试后，在几个具有挑战性的物体类别上达到了超过90%的成功率。视频和代码见此https URL。
英文摘要
Most current grasp synthesis systems are trained offline and remain fixed during deployment. While this works well when deployment conditions resemble the training data, performance can degrade when robots encounter conditions they have not seen before, such as unfamiliar objects. In this work, we present a continual-learning framework for single-view 6-DoF grasp synthesis for a parallel-jaw gripper in cluttered scenes. Rather than finetuning a large parametric model, our method adapts through memory in a learned embedding space: grasp outcomes update future grasp scores, while optional user demonstrations are recalled and transferred to new scenes as additional candidate grasps. We evaluate our method in simulation and in extensive real-world experiments comprising over 1500 grasp trials. We show that our method matches the performance of existing 6-DoF grasping baselines even before adaptation, improves online on unseen objects from categories absent or underrepresented during training, and supports long-horizon continual learning with limited forgetting. In real-world experiments, our method reaches over 90\% success rates on several challenging object categories after only 50 online grasp attempts. Videos and code at https://giuschio.github.io/cl_grasping/.
CommentsAccepted to CoRL 2026
