让机器人同时执行多个任务，还能动态调整优先级。
Value Iteration for Learning Concurrently Executable Robotic Control Tasks
- 基于价值函数独立性定义新成本函数，实现任务间低干扰协同。
- 通过拟合值迭代高效逼近成本函数，支持多任务并发控制。
- 适用于多机器人、机械臂等冗余系统，适合需要动态任务调度场景。
许多现代机器人系统（如多机器人系统和机械臂）具有冗余特性，可同时执行多个任务。本文提出一种基于强化学习的新方法，训练冗余机器人并行执行多个任务。与典型多目标强化学习不同，该方法允许学习到的任务以可能随时间变化的优先级堆叠方式组合执行。为此，我们首先定义了价值函数之间的任务独立性概念，并据此设计了一种成本函数，使基于近似价值函数的策略在完成自身控制目标的同时，对高优先级任务的干扰最小化。该方法可训练出一组可并行执行的控制策略。我们还引入一种拟合值迭代算法，高效地学习近似该成本函数。在多个场景和机器人系统上验证了方法的有效性。
原文摘要 · Abstract (English)
Many modern robotic systems such as multi-robot systems and manipulators exhibit redundancy, a property owing to which they are capable of executing multiple tasks. This work proposes a novel method, based on the Reinforcement Learning (RL) paradigm, to train redundant robots to be able to execute multiple tasks concurrently. Our approach differs from typical multi-objective RL methods insofar as the learned tasks can be combined and executed in possibly time-varying prioritized stacks. We do so by first defining a notion of task independence between learned value functions. We then use our definition of task independence to propose a cost functional that encourages a policy, based on an approximated value function, to accomplish its control objective while minimally interfering with the execution of higher priority tasks. This allows us to train a set of control policies that can be executed simultaneously. We also introduce a version of fitted value iteration to learn to approximate our proposed cost functional efficiently. We demonstrate our approach on several scenarios and robotic systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。