Kinodynamic Rapidly-exploring Random Forest for Rearrangement-Based Nonprehensile Manipulation

面向非抓取式机器人重排操作的对象中心运动学动力学规划

Object-Centric Kinodynamic Planning for Nonprehensile Robot Rearrangement Manipulation

针对多物体非抓取重排问题，提出一种基于对象中心的运动学动力学规划统一框架，通过计算期望物体运动并利用闭环推动策略在线生成机器人动作，显著提升了动作效率与任务有效性，并提供了标准化基准测试协议。

具有嵌套非连续操作动作的对象重排

Object Rearrangement with Nested Nonprehensile Manipulation Actions

本文考虑了重排规划问题，即寻找一系列的操作动作，将多个对象从一个初始配置移动到一个给定的目标配置。重新排列是机器人的一项关键技能，以便它们能够在包含杂乱的密闭空间中有效地运行。需要重新布置的任务的示例包括将物体包装在箱子内，其中物体需要根据预定义的图案放置。在紧密的箱子中，无碰撞抓取通常是不可用的。非强制性动作，如推动和滑动，是优选的，因为它们可以使用可以容易地插入到箱中的极简的末端执行器来执行。具有非线性动作的重排是一个具有挑战性的问题，因为它需要在多个对象的组合大配置空间中对对象交互进行推理。这项工作回顾了几个现有的重排规划技术，并介绍了一个新的，利用嵌套的nonprojectile的行动，推动几个类似的对象同时沿同一路径，这消除了需要单独重新安排每个对象。仿真实验和使用一个真正的Kuka机器人手臂显示所提出的方法来解决困难的重排任务，同时减少末端执行器的轨迹的长度的能力。

Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable to reliably sort large numbers of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.

Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement

This paper considers the problem of retrieving an object from many tightly packed objects using a combination of robotic pushing and grasping actions. Object retrieval in dense clutter is an important skill for robots to operate in households and everyday environments effectively. The proposed solution, Visual Foresight Trees (VFT), intelligently rearranges the clutter surrounding a target object so that it can be grasped easily. Rearrangement with nested nonprehensile actions is challenging as it requires predicting complex object interactions in a combinatorially large configuration space of…

PuSHR: A Multirobot System for Nonprehensile Rearrangement

We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized replanning approaches become intractable even for small-sized problems whereas decentralized approaches often get stuck in deadlocks. Our key insight is that by carefully assigning pushing tasks to robots, we could reduce the complexity of the rearrangement task, enabling robust perfo…

基于深度强化学习的非线性操作重排

Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning

在桌面上重新安排对象通过non-promisile操纵是一项任务，需要熟练的互动与物理世界。通常，这是通过精确建模的对象，机器人和明确的规划环境的物理属性。相比之下，由于显式建模物理环境并不总是可行的，并且涉及各种不确定性，因此我们仅基于视觉反馈使用深度强化学习学习非随机重排策略。为此，我们用奖励对任务进行建模，并训练一个深度Q网络。我们基于势场的启发式探索策略减少了导致次优结果的冲突量，并且我们积极平衡训练集，以避免对不良示例的偏见。与统一探索和标准经验回放相比，我们的训练过程可以更快地学习和更好地执行任务。我们从模拟中展示了经验证据，表明我们的方法导致85%的成功率，表明我们的系统可以应对环境的突然变化，并将我们的性能与人类水平的性能进行比较。