Developing intelligent robots for complex manipulation tasks in household and
factory settings remains challenging due to long-horizon tasks, contact-rich
manipulation, and the need to generalize across a wide variety of object shapes
and scene layouts. While Task and Motion Planning (TAMP) offers a promising
solution, its assumptions such as kinodynamic models limit applicability in
novel contexts. Neural object descriptors (NODs) have shown promise in object
and scene generalization but face limitations in addressing broader tasks. Our
proposed TAMP-based framework, NOD-TAMP, extracts short manipulation
trajectories from a handful of human demonstrations, adapts these trajectories
using NOD features, and composes them to solve broad long-horizon tasks.
Validated in a simulation environment, NOD-TAMP effectively tackles varied
challenges and outperforms existing methods, establishing a cohesive framework
for manipulation planning. For videos and other supplemental material, see the
project website: this https URL

通过提取人类示范的短时间操纵轨迹，使用神经目标描述符进行适应性处理，并将其组合以解决广泛的长期任务，我们的提出的基于 TAMP 的 NOD-TAMP 框架有效地解决了各种挑战，并在模拟环境中胜过现有方法，为操纵规划建立了一个有机的框架。

NOD-TAMP: 多步操作规划与神经对象描述符

NOD-TAMP: Multi-Step Manipulation Planning with Neural Object  Descriptors

The IKEA Furniture Assembly Environment is one of the first benchmarks for
testing and accelerating the automation of complex manipulation tasks. The
environment is designed to advance reinforcement learning from simple toy tasks
to complex tasks requiring both long-term planning and sophisticated low-level
control. Our environment supports over 80 different furniture models, Sawyer
and Baxter robot simulation, and domain randomization. The IKEA Furniture
Assembly Environment is a testbed for methods aiming to solve complex
manipulation tasks. The environment is publicly available at
this https URL

本研究介绍了 IKEA 家具组装环境，旨在测试自动化复杂操作任务的效率。此环境支持 80 多种不同的家具模型，并公开提供，可用于解决复杂操纵任务的方法测试。

长视角复杂操作任务的 IKEA 家具组装环境

IKEA Furniture Assembly Environment for Long-Horizon Complex  Manipulation Tasks

Dexterous multi-fingered hands are extremely versatile and provide a generic
way to perform a multitude of tasks in human-centric environments. However,
effectively controlling them remains challenging due to their high
dimensionality and large number of potential contacts. Deep reinforcement
learning (DRL) provides a model-agnostic approach to control complex dynamical
systems, but has not been shown to scale to high-dimensional dexterous
manipulation. Furthermore, deployment of DRL on physical systems remains
challenging due to sample inefficiency. Consequently, the success of DRL in
robotics has thus far been limited to simpler manipulators and tasks. In this
work, we show that model-free DRL can effectively scale up to complex
manipulation tasks with a high-dimensional 24-DoF hand, and solve them from
scratch in simulated experiments. Furthermore, with the use of a small number
of human demonstrations, the sample complexity can be significantly reduced,
which enables learning with sample sizes equivalent to a few hours of robot
experience. The use of demonstrations result in policies that exhibit very
natural movements and, surprisingly, are also substantially more robust.

本研究展示了无模型深度强化学习可有效扩展到高维复杂操作任务，并通过少量人工演示显著降低样本复杂度，从而使学习具有与机器人体验几个小时相当的样本量，展示出非常自然的动作并且更加稳健。