Learning to Design 3D Printable Adaptations on Everyday Objects for Robot Manipulation

Advancements in robot learning for object ma- nipulation have shown promising results, yet certain everyday objects remain challenging for robots to effectively interact with. This discrepancy arises from the fact that human-designed objects are optimized for human use rather than robot manip- ulation. To address this gap, we propose a framework to auto- matically design 3D printable adaptations that can be attached to hard-to-use objects, thus improving “robot ergonomics”. Our learning-based framework formulates the adaptation design and control as a dual Markov decision process and is able to improve robot-object interactions for various robot end effectors and objects. We further validate our designs in the real world with a Franka Panda robot. Please see the supplementary video and https://object-adaptation.github.io for additional visualizations.