arXiv cs.AI· Laurens Engwegen, Max Weltevrede, Caroline Horsch, Daan Brinks, Wendelin B\"ohmer·· 4 小时前AI 评分32
基于共享模块化循环的上下文 MDP 方法实现通用形态机器人控制
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
AI 导读
一项 arXiv 研究提出基于 Transformer 架构、采用共享模块化循环的通用机器人控制器，利用上下文 MDP 中模块化交互恢复部分缺失的上下文特征。研究在 MuJoCo 大规模机器人集合和四种环境中评估，结果显示对具有未见动力学、运动学和拓扑结构的机器人在零样本泛化上取得显著提升。
正文 · 原文
Abstract:A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utilizing contextual information about the properties of individual robots and exploiting their modular structure in the architecture of deep reinforcement learning agents. When the robots have highly dissimilar morphologies, however, this becomes a challenging problem, especially when the agent must generalize to new, unseen robots. In this paper, we consider the fact that contextual features are often only partially available, but that they can be recovered through modular interactions. This can allow for better multi-robot control and generalization to contexts that are not seen during training. To this extent, we implement a transformer-based architecture with shared modular recurrence and evaluate its (generalization) performance on a large set of MuJoCo robots. The results show a substantial improvement in zero-shot generalization performance on robots with unseen dynamics, kinematics, and topologies, in four different environments.
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2506.08630 [cs.AI] |
| (or arXiv:2506.08630v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2506.08630 arXiv-issued DOI via DataCite |
Submission history
From: Laurens Engwegen [view email]
[v1]
Tue, 10 Jun 2025 09:44:30 UTC (1,693 KB)
[v2]
Sun, 7 Sep 2025 10:03:07 UTC (1,690 KB)
[v3]
Wed, 8 Jul 2026 09:45:39 UTC (2,768 KB)
[v4]
Fri, 30 Oct 2026 17:07:06 UTC (2,985 KB)
来源：arXiv cs.AI · arxiv.org