无需规划即可完成复杂任务，靠动态梯度调整实现鲁棒控制
No Plan but Everything Under Control: Robustly Solving Sequential Tasks with Dynamically Composed Gradient Descent
- 通过反馈实时调整梯度场，隐式利用环境规律设定子目标
- 在100次真实抽屉操作实验中表现稳定，优于传统规划方法
- 适合需要自适应与容错的机器人任务，尤其契合生物智能策略
我们提出一种基于梯度的新方法，通过动态调整受反馈和世界规律影响的短视势场来解决序列任务。该调整隐式利用了环境中编码的子目标信息，使系统能够在不进行显式规划的情况下完成长序列任务，如经典块世界问题。与传统规划方法不同，该反馈驱动的方法能适应不确定和动态环境，在一百次真实世界的抽屉操作实验中展现出优异鲁棒性。实验表明，交互式感知与错误恢复可自然从梯度下降中涌现，无需显式建模。该方法为多种序列任务提供了计算高效的替代方案，同时与生物问题解决策略相符。
原文摘要 · Abstract (English)
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.
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