Diverse demonstration datasets have powered significant advances in robot
learning, but the dexterity and scale of such data can be limited by the
hardware cost, the hardware robustness, and the ease of teleoperation. We
introduce ALOHA 2, an enhanced version of ALOHA that has greater performance,
ergonomics, and robustness compared to the original design. To accelerate
research in large-scale bimanual manipulation, we open source all hardware
designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of
ALOHA 2 with system identification. See the project website at
aloha-2.github.io.

ALOHA 2 是 ALOHA 的改进版本，通过开源其硬件设计和提供详细教程以及 ALOHA 2 的 MuJoCo 模型，加速大规模双手操作研究的进程。

ALOHA 2: 强化的低成本双臂远程操作硬件

ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation

The progressive prevalence of robots in human-suited environments has given
rise to a myriad of object manipulation techniques, in which dexterity plays a
paramount role. It is well-established that humans exhibit extraordinary
dexterity when handling objects. Such dexterity seems to derive from a robust
understanding of object properties (such as weight, size, and shape), as well
as a remarkable capacity to interact with them. Hand postures commonly
demonstrate the influence of specific regions on objects that need to be
grasped, especially when objects are partially visible. In this work, we
leverage human-like object understanding by reconstructing and completing their
full geometry from partial observations, and manipulating them using a 7-DoF
anthropomorphic robot hand. Our approach has significantly improved the
grasping success rates of baselines with only partial reconstruction by nearly
30% and achieved over 150 successful grasps with three different object
categories. This demonstrates our approach's consistent ability to predict and
execute grasping postures based on the completed object shapes from various
directions and positions in real-world scenarios. Our work opens up new
possibilities for enhancing robotic applications that require precise grasping
and manipulation skills of real-world reconstructed objects.

通过从部分观测中重建和完成其全面几何结构，并利用七自由度的拟人化机器人手进行操作，我们的方法显著提高了只有部分重建的基线的抓取成功率近 30％，并以三个不同的对象类别多次实现了超过 150 次成功抓取，这表明我们的方法在真实世界的不同方向和位置基于完成的对象形状进行预测和执行抓取姿势的一贯能力，为增强需要实际重建对象的精确抓取和操作技能的机器人应用开辟了新的可能性。

具有神经对象形状完善功能的拟人抓握

Anthropomorphic Grasping with Neural Object Shape Completion

Dexterity is often seen as a cornerstone of complex manipulation. Humans are
able to perform a host of skills with their hands, from making food to
operating tools. In this paper, we investigate these challenges, especially in
the case of soft, deformable objects as well as complex, relatively
long-horizon tasks. However, learning such behaviors from scratch can be data
inefficient. To circumvent this, we propose a novel approach, DEFT (DExterous
Fine-Tuning for Hand Policies), that leverages human-driven priors, which are
executed directly in the real world. In order to improve upon these priors,
DEFT involves an efficient online optimization procedure. With the integration
of human-based learning and online fine-tuning, coupled with a soft robotic
hand, DEFT demonstrates success across various tasks, establishing a robust,
data-efficient pathway toward general dexterous manipulation. Please see our
website at this https URL for video results.

通过 DEFT（DExterous Fine-Tuning for Hand Policies）方法，结合软体机器手、基于人类驱动的经验和在线优化过程，本文探索了灵活性和复杂操作任务，并取得了成功的实验结果，展示了通向全面灵巧操作的数据高效路径。

DEFT: 灵巧微调用于现实世界手部策略

DEFT: Dexterous Fine-Tuning for Real-World Hand Policies

Achieving human-level dexterity is an important open problem in robotics.
However, tasks of dexterous hand manipulation, even at the baby level, are
challenging to solve through reinforcement learning (RL). The difficulty lies
in the high degrees of freedom and the required cooperation among heterogeneous
agents (e.g., joints of fingers). In this study, we propose the Bimanual
Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two
dexterous hands with tens of bimanual manipulation tasks and thousands of
target objects. Specifically, tasks in Bi-DexHands are designed to match
different levels of human motor skills according to cognitive science
literature. We built Bi-DexHands in the Issac Gym; this enables highly
efficient RL training, reaching 30,000+ FPS by only one single NVIDIA RTX 3090.
We provide a comprehensive benchmark for popular RL algorithms under different
settings; this includes Single-agent/Multi-agent RL, Offline RL, Multi-task RL,
and Meta RL. Our results show that the PPO type of on-policy algorithms can
master simple manipulation tasks that are equivalent up to 48-month human
babies (e.g., catching a flying object, opening a bottle), while multi-agent RL
can further help to master manipulations that require skilled bimanual
cooperation (e.g., lifting a pot, stacking blocks). Despite the success on each
single task, when it comes to acquiring multiple manipulation skills, existing
RL algorithms fail to work in most of the multi-task and the few-shot learning
settings, which calls for more substantial development from the RL community.
Our project is open sourced at this https URL.

提出了双手灵巧操纵仿真器 Bi-DexHands，可用于机器人学习多种操纵技巧，其中单一代理策略 PPO 可达到人类 48 个月婴儿的操作水平，而多代理策略可进一步帮助掌握需要灵巧双手协作的操作任务，但现有 RL 算法在多任务和少样本学习设置下仍需要更深入的研究。