物流场景中基于视觉观测的机器人顺序拣选任务的无监督奖励塑形
Unsupervised Reward Shaping for a Robotic Sequential Picking Task from Visual Observations in a Logistics Scenario
AI总结：
针对物流卸货的顺序拣选任务，本文提出一种基于专家观测的无监督奖励塑形算法，以降低强化学习对监督的依赖并提升其性能。
摘要：
我们关注一个物流行业中典型的卸货问题，并将其建模为顺序拾取与放置任务。在这类任务中，现代机器学习技术已被证明优于经典系统，因为它们更能适应随机性，并且能够更好地应对较大的不确定性。更具体地说，监督学习和模仿学习在这方面取得了突出的成果，但其缺点是需要某种形式的监督，而这种监督并非在所有场景下都能获得。另一方面，强化学习（RL）所需的监督形式要温和得多，但由于其效率低下，仍然难以实际应用。在本文中，我们提出了一种新颖的基于专家观测的无监督奖励塑形算法，并从理论上给出了动机。该算法放宽了智能体所需的监督水平，并致力于提高强化学习在我们任务中的性能。
英文摘要：
We focus on an unloading problem, typical of the logistics sector, modeled as a sequential pick-and-place task. In this type of task, modern machine learning techniques have shown to work better than classic systems since they are more adaptable to stochasticity and better able to cope with large uncertainties. More specifically, supervised and imitation learning have achieved outstanding results in this regard, with the shortcoming of requiring some form of supervision which is not always obtainable for all settings. On the other hand, reinforcement learning (RL) requires much milder form of supervision but still remains impracticable due to its inefficiency. In this paper, we propose and theoretically motivate a novel Unsupervised Reward Shaping algorithm from expert's observations which relaxes the level of supervision required by the agent and works on improving RL performance in our task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
