Skin and whole-body motion of large objects with torque and position controlled humanoid robots

HoRoPo的目标是考虑到配备了皮肤的类人机器人，开发一种新的运动生成科学范式。其目的是使用扭矩控制和非扭矩控制的类人机器人来操纵大型物体，并比较这两种方法的局限性，并将它们融合到统一的控制器中。科学的方向将是开发模型预测控制策略，以利用蒙皮以及扭矩和非扭矩控制。一个需要解决的科学问题是机器人上物体产生的接触数量所产生的复杂性。HoRoPo将探索和利用皮肤的潜力来进行全身操作控制。它将以德国皮肤团队(TUM-ICS)和法国团队(LAAS-Gepetto)在全身预测控制方面的工作为基础。前者在使皮肤贴片上的接触传播变得可及和可管理方面取得了一些令人印象深刻的突破。后者提出了用于计算仿人机器人全身运动的模型预测控制的新算法Crocoddyl，并使MPC适用于具有状态反馈的扭矩控制机器人。Tum-ICS已经为Reem-C，一个全尺寸的人形机器人(H1)配备了皮肤，并使用触觉反馈产生全身运动，而不是扭矩控制。五年前，LAAS-Gepetto提出了对REEM-C人形机器人的改进方案，目的是拥有一种非常强大的人形机器人，并且可以在执行器的扭矩水平上进行控制。HoRoPo的雄心是为扭矩可控机器人(TALOS)配备皮肤来操纵重物，并研究非扭矩可控仿人机器人(H-1)的类似策略。Laas-Gepetto将扩展Croccodyl的配方，以处理多个接触，并根据机器人状态在线修改可预见的行为。全身预测控制的一个困难是可能轨迹的丰富性，因为算法可能陷入不可行的局部极小值。为了应对这种情况，一个成功的方法是使用机器学习通过对问题的统计探索来学习问题的最可能的解决方案。在TUM-ICS开发的机器人皮肤原型提供了多模式传感能力，包括压力、温度和加速度。它已经被用来覆盖他们的人形H-1，用于平衡和行走控制的研究，包括一个软件框架。在HoRoPo，我们将与LAAS联手，将这一能力扩展到全身操纵。TOM-ICS将专注于具有位置或扭矩控制能力的仿人机器人的接触检测和基于多接触的控制。在HoRoPo开发的新控制方案将开辟一条人形机器人研究的新路线。这些先进的技术将产生的影响将远远超出迄今为止的类人机器人，并将增加这些机器人在多个应用领域的使用。

The goal of HoRoPo is to develop a new scientific paradigm for motion generation considering humanoid robots equipped with a skin. The aim is to manipulate large objects using torque-controlled and non-torque-controlled humanoid robots and comparing the limitations of the two approaches and to fuse them to a uniformed controller. The scientific direction will be to develop model predictive control strategies to take advantage of the skin, and the torque- and non-torque control. A scientific question to solve is the complexity arising from the number of contacts created by the object on the robot. HoRoPo will explore and exploit the potential of the skin for full-body manipulation control. It will take its foundation on the work by the German team (TUM-ICS) on the skin and the French team (LAAS-Gepetto) on whole-body predictive control. The former has realized some impressive breakthroughs in making the transmission of contact on skin patches accessible and manageable. The latter has proposed the new algorithm Crocoddyl for model predictive control for computing whole body motion for a humanoid and make MPC feasible for a torque-controlled robot with state feedback. TUM-ICS has already equipped REEM-C, a full-size humanoid robot (H1), with skin and generated whole body motion using tactile feedback without torque-control. Five years ago, LAAS-Gepetto has proposed improvements with respect to the REEM-C humanoid, in order to have a humanoid which is quite powerful and can be controlled at the torque level for the actuators. The ambition of HoRoPo is to equip a torque-controllable robot (TALOS) with skin to manipulate heavy objects and investigate a comparable strategy for non-torque-controllable humanoids (H-1). LAAS-Gepetto will extend the formulation of Croccodyl to handle multiple contacts, and modify on-line the foreseen behaviour according to the robot state. One difficulty with whole body MPC is the richness of the possible trajectory, since the algorithm can be trapped in unfeasible local minima. To cope with this, a successful approach is to use Machine Learning to learn the most likely solution for the problem through a statistical exploration of the problem. The prototype robotic skin developed at TUM-ICS provides multi-modal sensing capability, pressure, temperature, acceleration. It has already been used to cover their humanoid H-1 for research on balancing and walking control, including a software framework. In HoRoPo, we will join forces with LAAS to work on extending this capability to whole-body manipulation. TUM-ICS will focus on contacts detection and multi-contacts-based control of a humanoid robot with position or torque control capabilities. The new control schemes developed in HoRoPo will open up a new line of humanoid robotic research. Such advanced technologies will have an impact that will go far beyond humanoid robotics to date and will increase the use of these robots across multiple application domains.