The increasing demand for accelerated scientific discovery, driven by global challenges, highlights the need for advanced AI-driven robotics. Deploying robotic chemists in human-centric labs is key for the next horizon of autonomous discovery, as complex tasks still demand the dexterity of human scientists. Robotic manipulation in this context is uniquely challenged by handling diverse chemicals (granular, powdery, or viscous liquids), under varying lab conditions. For example, humans use spatulas for scraping materials from vial walls. Automating this process is challenging because it goes beyond simple robotic insertion tasks and traditional lab automation, requiring the execution of fine-granular movements within a constrained environment (the sample vial). Our work proposes an adaptive control framework to address this, relying on a low-level Cartesian impedance controller for stable and compliant physical interaction and a high-level reinforcement learning agent that learns to dynamically adjust interaction forces at the end-effector. The agent is guided by perception feedback, which provides the material's location. We first created a task-representative simulation environment with a Franka Research 3 robot, a scraping tool, and a sample vial containing heterogeneous materials. To facilitate the learning of an adaptive policy and model diverse characteristics, the sample is modelled as a collection of spheres, where each sphere is assigned a unique dislodgement force threshold, which is procedurally generated using Perlin noise. We train an agent to autonomously learn and adapt the optimal contact wrench for a sample scraping task in simulation and then successfully transfer this policy to a real robotic setup. Our method was evaluated across five different material setups, outperforming a fixed-wrench baseline by an average of 10.9%.

翻译：全球性挑战推动了对加速科学发现的日益增长的需求，这突显了先进人工智能驱动机器人技术的必要性。在以人为本的实验室中部署机器人化学家是实现自主发现下一个关键领域，因为复杂任务仍然需要人类科学家的灵巧性。在此背景下的机器人操作面临独特挑战，即需要在变化的实验室条件下处理多样化的化学品（颗粒状、粉末状或粘性液体）。例如，人类使用刮铲从样品瓶壁上刮取材料。自动化这一过程具有挑战性，因为它超越了简单的机器人插入任务和传统的实验室自动化，需要在受限环境（样品瓶）内执行精细粒度的运动。我们的工作提出了一个自适应控制框架来解决这一问题，该框架依赖于一个用于稳定且柔顺物理交互的低层笛卡尔阻抗控制器，以及一个学习动态调整末端执行器交互力的高层强化学习智能体。该智能体由感知反馈引导，感知反馈提供材料的位置信息。我们首先使用Franka Research 3机器人、一个刮取工具和一个包含异质材料的样品瓶，创建了一个具有任务代表性的仿真环境。为了促进自适应策略的学习并模拟多样化的材料特性，样品被建模为一个球体集合，其中每个球体被分配一个独特的脱离力阈值，该阈值是使用Perlin噪声程序化生成的。我们训练了一个智能体在仿真中自主学习和适应样本刮取任务的最佳接触力旋量，然后成功地将该策略迁移到真实的机器人设置中。我们的方法在五种不同的材料设置中进行了评估，平均性能优于固定力旋量基线10.9%。