AVO：多指操作中接触模式切换的摊销价值优化
AVO: Amortized Value Optimization for Contact Mode Switching in Multi-Finger Manipulation
AI总结：
针对多指操作中接触模式切换的轨迹优化子任务独立求解导致性能受限和计算昂贵的问题，提出摊销价值优化（AVO），通过学习价值函数预测未来任务性能并融入优化成本，衔接子任务并加速优化，在螺丝刀抓取转动任务中验证了减半预算下的性能提升。
摘要：
灵巧操作任务通常需要在不同接触模式之间切换，例如滚动、滑动、粘滞或非接触模式。当将灵巧操作任务表述为轨迹优化问题时，一种常见的方法是将这些任务分解为每个接触模式的子任务，并分别独立求解。独立优化每个子任务可能会限制性能，因为在没有未来子任务信息的情况下优化接触点、接触力或其他变量，可能会使系统陷入一种难以在后续子任务中取得进展的状态。此外，优化这些子任务的计算成本非常高。为解决这些挑战，我们提出了摊销价值优化（AVO），该方法引入了一个学习到的价值函数，用于预测未来的总任务性能。通过在每个规划步骤中将该价值函数纳入轨迹优化的成本中，价值函数的梯度引导优化器朝向能够最小化未来子任务成本的系统状态。这有效地衔接了独立优化的子任务，并通过减少所需的在线计算量来加速优化。我们在仿真和真实世界实验中，在螺丝刀抓取和转动任务上验证了AVO，并表明即使计算预算减少50%，与不使用价值函数的轨迹优化相比，性能也有所提升。
英文摘要：
Dexterous manipulation tasks often require switching between different contact modes, such as rolling, sliding, sticking, or non-contact contact modes. When formulating dexterous manipulation tasks as a trajectory optimization problem, a common approach is to decompose these tasks into sub-tasks for each contact mode, which are each solved independently. Optimizing each sub-task independently can limit performance, as optimizing contact points, contact forces, or other variables without information about future sub-tasks can place the system in a state from which it is challenging to make progress on subsequent sub-tasks. Further, optimizing these sub-tasks is very computationally expensive. To address these challenges, we propose Amortized Value Optimization (AVO), which introduces a learned value function that predicts the total future task performance. By incorporating this value function into the cost of the trajectory optimization at each planning step, the value function gradients guide the optimizer toward states that minimize the cost in future sub-tasks. This effectively bridges separately optimized sub-tasks, and accelerates the optimization by reducing the amount of online computation needed. We validate AVO on a screwdriver grasping and turning task in both simulation and real world experiments, and show improved performance even with 50% less computational budget compared to trajectory optimization without the value function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
