外观
论文arXiv 2610.2282712 天前
DefFlow：使用 3D Point Flows 的目标条件可变形物体操作
原文链接：DefFlow: Goal-Conditioned Deformable Object Manipulation using 3D Point Flows（arXiv 2610.22827 · 发布日期：2026-10-16）
DefFlow: Goal-Conditioned Deformable Object Manipulation using 3D Point Flows
DefFlow通过联合预测机器人与可变形物体的目标条件3D点流，实现面向多种3D目标形态的操作，并在仿真设置和真实机器人上验证。
核心要点
- 目标条件可变形物体操作需在多样初始与目标形态下推断抓取、释放等行为和连续动作。
- DefFlow从示范中学习，联合预测机器人与可变形物体的目标条件3D点流，而非仅预测机器人流。
- 在绳索、颗粒介质和布料操作设置中，DefFlow在成功率和目标距离指标上优于基线。
- 框架还使用真实点云观测和未见过的目标形态部署到真实机器人上。
验证范围与局限：验证范围包括绳索、颗粒介质、布料操作设置，以及一台真实机器人上的未见目标形态。
英文摘要 (Abstract)
Manipulating deformable objects toward desired 3D configurations is a fundamental capability for robots operating in unstructured physical environments. However, as the range of possible initial and target configurations grows, goal-conditioned deformable manipulation requires a robot to infer increasingly diverse manipulation behaviors in addition to precise continuous actions. We introduce DefFlow, a framework for goal-conditioned manipulation of deformable objects toward a broad range of 3D configurations. DefFlow learns from demonstrations by jointly predicting the goal-conditioned 3D point flow of the robot and deformable object. We show that this formulation better captures both the manipulation decisions, such as when and where to grasp, release, or re-grasp, and the precise continuous motions required for goal reaching, rather than predicting only the robot flow. Across rope, granular-media, and cloth manipulation settings with diverse initial and target configurations, DefFlow substantially outperforms goal-conditioned action-prediction and robot-flow-only baselines on success rates and goal-distance metrics. We further demonstrate deployment of our framework on a real robot using real point-cloud observations and unseen goal configurations.
作者：Gaurav Singh, Sergio Orozco, Duong, BS, Ryan, Mete Tuluhan Akbulut, George Konidaris, Srinath Sridhar
来源
本页事实对应的公开页面，以原文为准。