We present Splat-MOVER, a modular robotics stack for open-vocabulary robotic
manipulation, which leverages the editability of Gaussian Splatting (GSplat)
scene representa- tions to enable multi-stage manipulation tasks. Splat-MOVER
consists of: (i) $\textit{ASK-Splat}$, a GSplat representation that distills
latent codes for language semantics and grasp affordance into the 3D scene.
ASK-Splat enables geometric, semantic, and affordance understanding of 3D
scenes, which is critical for many robotics tasks; (ii) $\textit{SEE-Splat}$, a
real-time scene-editing module using 3D semantic masking and infilling to
visualize the motions of objects that result from robot interactions in the
real-world. SEE-Splat creates a "digital twin" of the evolving environment
throughout the manipulation task; and (iii) $\textit{Grasp- Splat}$, a grasp
generation module that uses ASK-Splat and SEE-Splat to propose candidate grasps
for open-world objects. ASK-Splat is trained in real-time from RGB images in a
brief scanning phase prior to operation, while SEE-Splat and Grasp- Splat run
in real-time during operation. We demonstrate the superior performance of
Splat-MOVER in hardware experiments on a Kinova robot compared to two recent
baselines in four single-stage, open-vocabulary manipulation tasks, as well as
in four multi-stage manipulation tasks using the edited scene to reflect scene
changes due to prior manipulation stages, which is not possible with the
existing baselines. Code for this project and a link to the project page will
be made available soon.

Splat-MOVER 是一个模块化机器人软件框架，利用高斯分层表示法（GSplat）的可编辑性，实现多阶段机器人操作任务。

Splat-MOVER: 多阶段开放词汇机器人操纵通过可编辑高斯 Splating

$\textbf{Splat-MOVER}$: Multi-Stage, Open-Vocabulary Robotic  Manipulation via Editable Gaussian Splatting

Imitation learning has shown great potential for enabling robots to acquire
complex manipulation behaviors. However, these algorithms suffer from high
sample complexity in long-horizon tasks, where compounding errors accumulate
over the task horizons. We present PRIME (PRimitive-based IMitation with data
Efficiency), a behavior primitive-based framework designed for improving the
data efficiency of imitation learning. PRIME scaffolds robot tasks by
decomposing task demonstrations into primitive sequences, followed by learning
a high-level control policy to sequence primitives through imitation learning.
Our experiments demonstrate that PRIME achieves a significant performance
improvement in multi-stage manipulation tasks, with 10-34% higher success rates
in simulation over state-of-the-art baselines and 20-48% on physical hardware.

PRIME 是一种以行为基元为基础的框架，旨在提高模仿学习的数据效率，在多阶段操作任务中取得了显著的性能提升。