PFI-TT: Robotic Dexterity for Material Handling

这一创新-技术转化伙伴关系(PFI-TT)项目的更广泛影响/商业潜力包括为我们的经济建立更有效和更强大的供应链的步骤。近年来，物资搬运行业对我们的国民经济至关重要：错综复杂的供应链在社会上随处可见。该行业的头号挑战是困难的人体工程学和重复性使得许多任务容易受伤，不适合工人。然而，自动化只是在很小程度上缓解了这个问题：机器人技术在材料搬运方面只实现了很小的市场渗透率，其中一个关键原因是缺乏与多功能和灵活的机器人材料搬运相关的关键技术。目前，该领域很大程度上缺乏能够处理灵活任务的灵活机器人机械手。开发这样的机械手既可以增进我们对运动控制任务和运动学习的科学理解，也可以对提高供应链效率产生直接影响。拟议的项目旨在开发这种技术并将其推向市场。我们建立在首席研究人员团队最近的工作基础上，他们展示了复杂的机器人操作任务，例如通过手指步态进行大型手部对象重新定位，同时确保被操作对象的安全。这一结果是通过将运动技能深度强化学习的新探索方法与PFI团队先前开发的基于光学的触觉手指相结合而实现的。该项目旨在进一步开发基于学习的外部操作方法，即机器人使用外部表面来传递所需的运动，这一策略将有助于减少对手本身的运动学要求。这将反过来使手能够安装在商业机器人手臂上，从而形成一个完整的自动化站。尽管为PFI项目提供信息的以前的工作完全依赖于触觉和本体感觉来展示灵活性，但完成任务的部署需要添加视觉传感器，从而需要学习灵活外部操作的多模式、视觉触觉控制策略的方法。PFI团队将把这项研究应用于通过灵活的包裹重新定位实现分拣系统诱导过程自动化的具体任务，这是作为我们供应链骨干的配送中心常见的任务。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project consists of steps towards a more effective and robust supply chain for our economy. Recent years have highlighted the critical importance of the material handling industry to our national economy: a snarled supply chain reverberates everywhere in society. The number one challenge in this industry is that the difficult ergonomics and repetitive nature make many tasks injury-prone and unsuitable for workers. Nevertheless, automation has alleviated this problem only to a small degree: robotics has achieved only small market penetration in material handling, and one of the key reasons is the absence of critical technology pertaining to versatile and dexterous robotic material handling. Currently, flexible robotic manipulators able to handle dexterous tasks are largely lacking from the field. Developing such manipulators would both enhance our scientific understanding of motor control tasks and motor learning and have an immediate impact in increasing supply chain efficiency.The proposed project aims to develop and translate such technology to the marketplace. We build on recent work from the principal investigator’s team, which demonstrated complex robotic manipulation tasks, such as large in-hand object reorientation with finger gaiting, while simultaneously securing the manipulated object. This result was achieved by combining novel exploration methods for deep reinforcement learning of motor skills with the optics-based tactile fingers previously developed by the PFI team. The project aims to further develop learning-based methods for extrinsic manipulation, where the robot uses external surfaces to impart the desired movement, a strategy that will help reduce the kinematic requirements on the hand itself. This will in turn enable hands to be mounted on commercial robot arms, resulting in a complete automation station. While the previous work informing the PFI project relied exclusively on tactile and proprioceptive sensing to demonstrate dexterity, deployment for complete tasks requires the addition of vision sensors, and thus methods for learning multimodal, visuotactile control policies for dexterous extrinsic manipulation. The PFI team will apply this research to the concrete task of automating the sortation system induction process with dexterous package re-orientation, a commonly found task in the distribution centers that are the backbone of our supply chain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.