Nazari Kiyanoush, Mandil Willow, Santello Marco, Park Seongjun, Ghalamzan-E Amir
School of Computer Science and LIAT, University of Lincoln, Lincoln, UK.
Cambridge Research Laboratory, Toshiba Europe, Cambridge, UK.
Nat Mach Intell. 2025;7(7):1119-1128. doi: 10.1038/s42256-025-01062-2. Epub 2025 Jul 22.
Ensuring a stable grasp during robotic manipulation is essential for dexterous and reliable performance. Traditionally, slip control has relied on grip force modulation. Here we show that trajectory modulation provides an effective alternative for slip prevention in certain robotic manipulation tasks. We develop and compare a slip control policy based on trajectory modulation with a conventional grip-force-based approach. Our results demonstrate that trajectory modulation can significantly outperform grip force control in specific scenarios, highlighting its potential as a robust slip control strategy. Furthermore, we show that, similar to humans, incorporating a data-driven action-conditioned forward model within a model predictive control framework is key to optimizing trajectory modulation for slip prevention. These findings introduce a predictive control framework leveraging trajectory adaptation, offering a new perspective on slip mitigation. This approach enhances grasp stability in dynamic and unstructured environments, improving the adaptability of robotic systems across various applications.
在机器人操作过程中确保稳定抓握对于灵活可靠的性能至关重要。传统上，防滑控制依赖于握力调制。在此我们表明，轨迹调制为某些机器人操作任务中的防滑提供了一种有效的替代方法。我们开发并比较了基于轨迹调制的防滑控制策略与传统的基于握力的方法。我们的结果表明，在特定场景下轨迹调制能显著优于握力控制，凸显了其作为一种强大防滑控制策略的潜力。此外，我们表明，与人类类似，在模型预测控制框架内纳入数据驱动的动作条件前向模型是优化用于防滑的轨迹调制的关键。这些发现引入了一个利用轨迹自适应的预测控制框架，为防滑减轻提供了新视角。这种方法增强了动态和非结构化环境中的抓握稳定性，提高了机器人系统在各种应用中的适应性。
