Bimanual manipulation is challenging due to precise spatial and temporal
coordination required between two arms. While there exist several real-world
bimanual systems, there is a lack of simulated benchmarks with a large task
diversity for systematically studying bimanual capabilities across a wide range
of tabletop tasks. This paper addresses the gap by extending RLBench to
bimanual manipulation. We open-source our code and benchmark comprising 13 new
tasks with 23 unique task variations, each requiring a high degree of
coordination and adaptability. To kickstart the benchmark, we extended several
state-of-the art methods to bimanual manipulation and also present a
language-conditioned behavioral cloning agent -- PerAct2, which enables the
learning and execution of bimanual 6-DoF manipulation tasks. Our novel network
architecture efficiently integrates language processing with action prediction,
allowing robots to understand and perform complex bimanual tasks in response to
user-specified goals. Project website with code is available at:
this http URL

扩展 RLBench 以进行双手操作的模拟基准测试，提供 13 项新任务和 23 个独特的任务变体，其中每个任务要求高度的协调和适应性。通过引入多种先进方法和 PerAct2 语言条件行为克隆代理，实现语言处理与动作预测的高效集成，使机器人能够理解和执行用户指定目标的复杂双手任务。

PerAct2: 一个面向双手操作任务的感知器执行框架

PerAct2: A Perceiver Actor Framework for Bimanual Manipulation Tasks

In the same way that the computer vision (CV) and natural language processing
(NLP) communities have developed self-supervised methods, reinforcement
learning (RL) can be cast as a self-supervised problem: learning to reach any
goal, without requiring human-specified rewards or labels. However, actually
building a self-supervised foundation for RL faces some important challenges.
Building on prior contrastive approaches to this RL problem, we conduct careful
ablation experiments and discover that a shallow and wide architecture,
combined with careful weight initialization and data augmentation, can
significantly boost the performance of these contrastive RL approaches on
challenging simulated benchmarks. Additionally, we demonstrate that, with these
design decisions, contrastive approaches can solve real-world robotic
manipulation tasks, with tasks being specified by a single goal image provided
after training.

通过对比强化学习问题的先前对比方法，我们发现，结合谨慎的权重初始化和数据增强，使用浅而宽的架构可以显著提高这些对比强化学习方法在挑战性的模拟基准测试中的性能，并且可以通过这些设计决策解决实际的机器人操作任务。

稳定对比强化学习：离线目标达成技术

Stabilizing Contrastive RL: Techniques for Offline Goal Reaching

Rearranging and manipulating deformable objects such as cables, fabrics, and
bags is a long-standing challenge in robotic manipulation. The complex dynamics
and high-dimensional configuration spaces of deformables, compared to rigid
objects, make manipulation difficult not only for multi-step planning, but even
for goal specification. Goals cannot be as easily specified as rigid object
poses, and may involve complex relative spatial relations such as "place the
item inside the bag". In this work, we develop a suite of simulated benchmarks
with 1D, 2D, and 3D deformable structures, including tasks that involve
image-based goal-conditioning and multi-step deformable manipulation. We
propose embedding goal-conditioning into Transporter Networks, a recently
proposed model architecture for learning robotic manipulation that rearranges
deep features to infer displacements that can represent pick and place actions.
We demonstrate that goal-conditioned Transporter Networks enable agents to
manipulate deformable structures into flexibly specified configurations without
test-time visual anchors for target locations. We also significantly extend
prior results using Transporter Networks for manipulating deformable objects by
testing on tasks with 2D and 3D deformables. Supplementary material is
available at this https URL

本文介绍了一种将目标限制引入运输网络中的方法，利用该方法结合具有多阶段柔性物体操作任务而设计的一套模拟基准，成功实现了机器人对柔性物体的多样姿态操作。