English(EN) IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation

机器人框架IMPACT提升强制操控和泛化能力

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-09 13:00

研究人员开发了IMPACT，一个用于机器人操控的新框架，可提高需要强制交互的任务的性能。该系统将任务规划与内部模型预测控制分离，使机器人能够更好地处理不同重量的物体并执行富含接触的任务。实验表明，与以前的方法相比，IMPACT实现了更高的成功率、更好的泛化能力以及更高的安全性和能源效率。 AI

影响增强了机器人在现实世界操控任务中的能力，可能带来更通用、更高效的自动化。

排序理由该集群包含一篇详细介绍机器人新研究框架的学术论文。

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arXiv cs.CV TIER_1 English(EN) · Jiawei Gao, Chaoqi Liu, Peilin Wu, Haonan Chen, Yilun Du · 2026-06-10 04:00

IMPACT：为强制性机器人操作学习内部模型预测控制

arXiv:2606.10818v1 Announce Type: cross Abstract: Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wipin…
arXiv cs.CV TIER_1 English(EN) · Yilun Du · 2026-06-09 13:00

IMPACT：为强制性机器人操作学习内部模型预测控制

Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically em…