We introduce a novel gradient-based approach for solving sequential tasks by dynamically adjusting the underlying myopic potential field in response to feedback and the world's regularities. This adjustment implicitly considers subgoals encoded in these regularities, enabling the solution of long sequential tasks, as demonstrated by solving the traditional planning domain of Blocks World - without any planning. Unlike conventional planning methods, our feedback-driven approach adapts to uncertain and dynamic environments, as demonstrated by one hundred real-world trials involving drawer manipulation. These experiments highlight the robustness of our method compared to planning and show how interactive perception and error recovery naturally emerge from gradient descent without explicitly implementing them. This offers a computationally efficient alternative to planning for a variety of sequential tasks, while aligning with observations on biological problem-solving strategies.

翻译：本文提出一种新颖的基于梯度的方法，通过根据反馈和世界规律动态调整底层近视势场来解决序列任务。这种调整隐式地考虑了编码在这些规律中的子目标，从而能够解决长序列任务——正如在传统规划领域积木世界中的求解所展示的那样，且无需任何规划。与传统的规划方法不同，我们的反馈驱动方法能够适应不确定和动态的环境，这一点通过涉及抽屉操作的一百次真实世界试验得到了验证。这些实验突显了我们的方法相较于规划的鲁棒性，并展示了交互式感知和错误恢复如何从梯度下降中自然涌现，而无需显式实现它们。这为各种序列任务提供了一种计算高效的规划替代方案，同时与生物问题解决策略的观察结果相一致。