论文标题
通过视觉技能和前提模型执行反应性的长范围任务
Reactive Long Horizon Task Execution via Visual Skill and Precondition Models
论文作者
论文摘要
零拍摄的机器人任务的执行对于允许机器人在人类环境中执行各种任务的执行很重要，但是收集训练现实世界中端到端策略所需的数据量通常是不可行的。我们描述了一种用于模拟训练的方法，该方法可以使用在模拟中学习的模型来完成看不见的机器人任务，从而使简单任务计划者的接地组件。我们在模拟中学习了一个参数化技能的库，以及一组基于预测的先决条件和终止条件。我们探索了一个堆叠任务，因为它具有清晰的结构，必须将多个技能束缚在一起，但是我们的方法适用于广泛的其他问题和域，并且可以在没有微调的情况下从模拟转移到现实世界。该系统能够识别失败并从感知输入中完成长马任务，这对于现实世界执行至关重要。我们在模拟和现实世界中评估了我们提出的方法，与天真的基准相比，成功率从91.6％增加到了模拟的91.6％增加到98％，在现实世界中的成功率从10％增加到80％。有关包括现实世界和模拟在内的实验视频，请参见：https：//www.youtube.com/playlist?list=pl-od0xhungelfqmpngykgfzarstfpoxqx
Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world is often infeasible. We describe an approach for sim-to-real training that can accomplish unseen robotic tasks using models learned in simulation to ground components of a simple task planner. We learn a library of parameterized skills, along with a set of predicates-based preconditions and termination conditions, entirely in simulation. We explore a block-stacking task because it has a clear structure, where multiple skills must be chained together, but our methods are applicable to a wide range of other problems and domains, and can transfer from simulation to the real-world with no fine tuning. The system is able to recognize failures and accomplish long-horizon tasks from perceptual input, which is critical for real-world execution. We evaluate our proposed approach in both simulation and in the real-world, showing an increase in success rate from 91.6% to 98% in simulation and from 10% to 80% success rate in the real-world as compared with naive baselines. For experiment videos including both real-world and simulation, see: https://www.youtube.com/playlist?list=PL-oD0xHUngeLfQmpngYkGFZarstfPOXqX
