Abstract
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.
Abstract (translated)
我们提出了一种基于梯度的方法，通过动态调整底层近视势场来解决序列任务，并根据反馈和世界的规律性进行响应。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，例如传统的Blocks World规划领域——无需任何计划即可完成。与传统规划方法不同，我们的基于反馈的方法适应不确定性和动态环境，在一百次涉及抽屉操作的真实世界试验中得到了验证。这些实验突显了我们方法相对于规划的稳健性，并展示了交互感知和错误恢复如何自然而然地从梯度下降中出现，而无需明确实现它们。这为各种序列任务提供了计算效率更高的替代方案，并与生物解决问题策略中的观察结果相一致。
URL
https://arxiv.org/abs/2503.01732
