Tool and robot control jointly design improve powder weighing accuracy
Tool-Policy Co-Design for Powder Weighing in Laboratory Automation
Robotics
Summary
Weighing powders accurately is hard for robots because powders behave in complex ways and tools are normally designed for people. The researchers created a method that designs the tool itself and how the robot controls it at the same time. This method tries many tool shapes and control settings to find the best combo that works well for different powders. Their approach reduced weighing errors by almost half compared to using a regular tool, even for powders the system hadn’t seen before.
What this means in practice
- •For laboratory automation engineers: Improve accuracy of robotic powder dispensing by jointly optimizing the dispensing tool design and robot control policies for diverse materials.
- •For robotic system integrators: Design custom end-effectors and control strategies for robots to handle complex materials with varying flow properties more reliably.
Authors
Nikola Radulov, Xin Yang, Kevin S. Luck, Gabriella Pizzuto
Abstract
Autonomous powder weighing is one of many bottlenecks in laboratory automation due to the complex, non-linear dynamics of heterogeneous materials. Robot chemists performing this task utilise standard tools shaped for the dexterity of human hands, whose fixed geometry sets the dynamics that the control policy needs to regulate. This work introduces a tool-policy co-design framework that concurrently optimises the morphology of a dispensing tool and its control policy for use by robots in chemistry laboratories, formulated as a bi-level optimisation that minimises dispensing error over a target distribution of powder flowabilities. The outer loop varies tool-design parameters such as tool depth, width and rim spike topology using Bayesian optimisation and hyperband, while an inner loop optimises a control policy for each candidate morphology. We also introduce a geometric similarity metric that warm-starts policy training from cached policies of structurally similar designs, exploring 28% more configurations under the same compute budget. The proposed framework is evaluated on a robotic powder weighing task across seven materials with distinct physical dynamics in a flowability-informed robot-material simulation framework. Experimental results demonstrate that our co-designed tool morphology reduces real-world weighing errors by 45% relative to a standard tool, including on previously unseen materials. These results demonstrate our method can adapt both the control policy and the physical tool to the dynamics of the target material, bringing a new paradigm for material manipulation to the field of laboratory automation.