论文标题
学习与物理风险的风险意识重新排列
Learning to Rearrange with Physics-Inspired Risk Awareness
论文作者
论文摘要
现实世界中的应用程序需要在物理世界中运行的机器人，除了完成任务外，还要意识到潜在风险。很大一部分风险行为是由于与负担无知的物体相互作用而产生的。为了防止代理做出不安全的决策，我们建议通过强化学习在室内环境中对质量和摩擦等物理特性执行任务来训练机器人代理。我们通过一种新颖的物理风格的奖励功能来实现这一目标，该功能鼓励代理商学习辨别不同质量和摩擦系数的政策。我们介绍了两项新颖且具有挑战性的室内重排任务 - 可变的摩擦推动任务和可变的质量推动任务 - 允许评估在交易绩效和物理启发的风险方面学到的政策。我们的结果表明，通过对拟议的奖励进行装备，代理商可以学习选择推动目标或目标轨迹的政策，以最低的物理成本，可以进一步利用这作为预防措施，以限制在安全性批评环境中的行为。
Real-world applications require a robot operating in the physical world with awareness of potential risks besides accomplishing the task. A large part of risky behaviors arises from interacting with objects in ignorance of affordance. To prevent the agent from making unsafe decisions, we propose to train a robotic agent by reinforcement learning to execute tasks with an awareness of physical properties such as mass and friction in an indoor environment. We achieve this through a novel physics-inspired reward function that encourages the agent to learn a policy discerning different masses and friction coefficients. We introduce two novel and challenging indoor rearrangement tasks -- the variable friction pushing task and the variable mass pushing task -- that allow evaluation of the learned policies in trading off performance and physics-inspired risk. Our results demonstrate that by equipping with the proposed reward, the agent is able to learn policies choosing the pushing targets or goal-reaching trajectories with minimum physical cost, which can be further utilized as a precaution to constrain the agent's behavior in a safety-critic environment.
