Task-Driven Perception and Manipulation for Constrained Placement of Unknown Objects
Task-Driven Perception and Manipulation for Constrained Placement of Unknown Objects
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DOI：
10.1109/lra.2020.3006816
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发表时间：
2020-06
影响因子：
5.2
通讯作者：
Chaitanya Mitash;Rahul Shome;Bowen Wen;Abdeslam Boularias;Kostas E. Bekris
中科院分区：
文献类型：
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作者：
Chaitanya Mitash;Rahul Shome;Bowen Wen;Abdeslam Boularias;Kostas E. Bekris
最近的进展，在机器人操作处理的情况下，以前未知的对象在相对简单的任务，如垃圾箱采摘。现有的方法，更多的约束问题，但是，如故意放置在一个紧张的地区，更严格地依赖于形状信息，以实现安全执行。这项工作涉及到拾取和约束放置的对象，而无需访问几何模型。目标是挑选一个物体并将其安全地放置在所需的目标区域内，而不会发生任何碰撞，同时最大限度地减少完成任务所需的时间和传感操作。为此目的，提出了一种算法框架，它执行操作规划同时在保守和乐观的估计对象的体积。保守估计确保操纵是安全的，而乐观估计指导基于传感器的操纵过程时，不能找到保守估计的解决方案。为了保持这些估计并在操作期间动态更新它们，对象由简单的体积表示表示，其存储被占用和不可见的体素的集合。所提出的方法的有效性证明了开发一个机器人系统，从桌面上挑选一个以前看不见的对象，并将其放置在一个受约束的空间。该系统包括一个双臂机械手与异构末端执行器，并利用手的重新把握策略。现实世界的实验表明，简单的选择，感觉和放置的替代品往往无法解决选择和约束的放置问题。然而，所提出的管道，实现了超过95%的成功率和更快的执行时间，通过多个物理实验进行评估。
Recent progress in robotic manipulation has dealt with the case of previously unknown objects in the context of relatively simple tasks, such as bin-picking. Existing methods for more constrained problems, however, such as deliberate placement in a tight region, depend more critically on shape information to achieve safe execution. This work deals with pick-and-constrained placement of objects without access to geometric models. The objective is to pick an object and place it safely inside a desired goal region without any collisions, while minimizing the time and the sensing operations required to complete the task. An algorithmic framework is proposed for this purpose, which performs manipulation planning simultaneously over a conservative and an optimistic estimate of the object’s volume. The conservative estimate ensures that the manipulation is safe while the optimistic estimate guides the sensor-based manipulation process when no solution can be found for the conservative estimate. To maintain these estimates and dynamically update them during manipulation, objects are represented by a simple volumetric representation, which stores sets of occupied and unseen voxels. The effectiveness of the proposed approach is demonstrated by developing a robotic system that picks a previously unseen object from a table-top and places it in a constrained space. The system comprises of a dual-arm manipulator with heterogeneous end-effectors and leverages hand-offs as a re-grasping strategy. Real-world experiments show that straightforward pick-sense-and-place alternatives frequently fail to solve pick-and-constrained placement problems. The proposed pipeline, however, achieves more than 95% success rate and faster execution times as evaluated over multiple physical experiments.