用触觉传感提升机器人抓取的稳定性，发现简单触觉信号更利于学习。
The Role of Tactile Sensing for Learning Reach and Grasp
- 结合强化学习与不同触觉输入，对比抓取效果
- 在视觉不完美时，触觉特征显著改善学习结果
- 复杂触觉信号反而增加训练难度，适合追求鲁棒性的研究者
稳定可靠的机器人抓取对当前及未来的机器人应用至关重要。近期工作通过大规模数据集和监督学习提升了对称抓取的速度与精度，但因规划周期长，易受感知与校准误差影响。为获得更鲁棒、响应更快的抓取动作，结合强化学习与触觉感知是潜在方向。然而，尚无系统性评估力觉触觉感知复杂度如何影响抓取任务的学习行为。本文采用两种无模型强化学习方法，对比多种触觉与环境配置下的对称抓取表现。结果表明，在视觉感知不理想情况下，多种触觉特征可提升学习效果；而复杂触觉输入则会加剧训练难度。
原文摘要 · Abstract (English)
Stable and robust robotic grasping is essential for current and future robot applications. In recent works, the use of large datasets and supervised learning has enhanced speed and precision in antipodal grasping. However, these methods struggle with perception and calibration errors due to large planning horizons. To obtain more robust and reactive grasping motions, leveraging reinforcement learning combined with tactile sensing is a promising direction. Yet, there is no systematic evaluation of how the complexity of force-based tactile sensing affects the learning behavior for grasping tasks. This paper compares various tactile and environmental setups using two model-free reinforcement learning approaches for antipodal grasping. Our findings suggest that under imperfect visual perception, various tactile features improve learning outcomes, while complex tactile inputs complicate training.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。