Achieving human-like dexterous manipulation remains a crucial area of
research in robotics. Current research focuses on improving the success rate of
pick-and-place tasks. Compared with pick-and-place, throw-catching behavior has
the potential to increase picking speed without transporting objects to their
destination. However, dynamic dexterous manipulation poses a major challenge
for stable control due to a large number of dynamic contacts. In this paper, we
propose a Stability-Constrained Reinforcement Learning (SCRL) algorithm to
learn to catch diverse objects with dexterous hands. The SCRL algorithm
outperforms baselines by a large margin, and the learned policies show strong
zero-shot transfer performance on unseen objects. Remarkably, even though the
object in a hand facing sideward is extremely unstable due to the lack of
support from the palm, our method can still achieve a high level of success in
the most challenging task. Video demonstrations of learned behaviors and the
code can be found on the supplementary website.

我们提出了一种稳定约束强化学习算法（SCRL）来学习用灵巧的手抓取各种物体。该算法在基线算法上表现出色，并且学得的策略在未见物体上显示出强大的零样本迁移性能。值得注意的是，即使手中端面朝侧面的物体由于缺乏手掌的支持而极不稳定，我们的方法在最具挑战性的任务中仍然能够达到很高的成功率。可在补充网站上找到学习行为的视频演示和代码。

DexCatch：学习利用灵巧手方式抓取任意物体

DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands

In this paper, we present a grammar-based natural language framework for
robot programming, specifically for pick-and-place tasks. Our approach uses a
custom dictionary of action words, designed to store together words that share
meaning, allowing for easy expansion of the vocabulary by adding more action
words from a lexical database. We validate our Natural Language Robot
Programming (NLRP) framework through simulation and real-world experimentation,
using a Franka Panda robotic arm equipped with a calibrated camera-in-hand and
a microphone. Participants were asked to complete a pick-and-place task using
verbal commands, which were converted into text using Google's Speech-to-Text
API and processed through the NLRP framework to obtain joint space trajectories
for the robot. Our results indicate that our approach has a high system
usability score. The framework's dictionary can be easily extended without
relying on transfer learning or large data sets. In the future, we plan to
compare the presented framework with different approaches of human-assisted
pick-and-place tasks via a comprehensive user study.

本文提出了一种基于语法的自然语言机器人编程框架，特别用于拾取和放置任务，其方法使用自定义的动作词词典来存储共享意义的单词，通过增加更多的动作词从词汇数据库，轻松扩展词汇量。本文通过模拟和现实世界的实验验证了我们的自然语言机器人编程（NLRP）框架，使用配备了校准相机和麦克风的 Franka Panda 机器人手臂，并将参与者要求使用口头命令完成拾取和放置任务，该任务经由 Google 的语音转文本 API 转换为文本并通过 NLRP 框架处理，以获取机器人的联合空间轨迹，该方法具有高的系统可用性得分，可以轻松扩展字典而不依赖于转移学习或大型数据集，未来我们计划通过综合用户研究比较所述框架与不同人类辅助拾取和放置任务的方法

自然语言机器人编程：将自然语言处理与自主机器人抓取集成

Natural Language Robot Programming: NLP integrated with autonomous  robotic grasping

Collaborative robotics requires effective communication between a robot and a
human partner. This work proposes a set of interpretive principles for how a
robotic arm can use pointing actions to communicate task information to people
by extending existing models from the related literature. These principles are
evaluated through studies where English-speaking human subjects view animations
of simulated robots instructing pick-and-place tasks. The evaluation
distinguishes two classes of pointing actions that arise in pick-and-place
tasks: referential pointing (identifying objects) and locating pointing
(identifying locations). The study indicates that human subjects show greater
flexibility in interpreting the intent of referential pointing compared to
locating pointing, which needs to be more deliberate. The results also
demonstrate the effects of variation in the environment and task context on the
interpretation of pointing. Our corpus, experiments and design principles
advance models of context, common sense reasoning and communication in embodied
communication.

本文提出了一组解释性原则，说明了机器人手臂如何借助指向行为向人们传达任务信息，并通过实验研究评估了这些原则在拾取和放置任务中的有效性。