Draft: A Parametric Tool for Robot Design Exploration

2026-09-29

AI Summary

This study addresses the high costs and scarcity of empirical data in the co-design of robot morphology and control by proposing Draft, a parametric generation framework. Draft compiles arbitrary serial-chain trees into simulation-ready models without CAD dependencies. By fitting hardware trends from real-world measurements of 114 actuators and 49 robots, it anchors the design space and enables efficient exploration through a two-stage reinforcement learning curriculum. Experimental evaluations demonstrate that digital twins constructed for four physical robots achieve a mass error of only 1.10×. Furthermore, the framework successfully assesses design trade-offs among three quadrupedal robots, validating both the effectiveness and engineering feasibility of the proposed approach.

Abstract

Robot performance is often limited by the cost of iterating on morphology and control together, since every computer-aided design (CAD) change has to be carried into a simulation-ready model before control work begins. Co-design methods attempt to close this gap, but each uses a model generator written for a single platform or lack the use of real-world data to suggest that designs are plausible. We present Draft, a parametric generation tool whose generalized engine compiles any parametric tree of serial chains into a simulation-ready MJCF model, without CAD. It allows engineers to explore design tradeoffs through easily adjustable models and evaluate how changes influence controller performance. Draft grounds the free parameters of each design using trends fitted to a survey of $114$ actuators and $49$ published robot descriptions, so that a generated robot is anchored to real-world hardware. We validate those trends wholistically by building twins of four off-the-shelf robots, whose masses agree to $1.10\times$ geometric mean fold error. Finally, we demonstrate how Draft exposes design tradeoffs by evaluating three quadrupeds through a two-stage reinforcement learning curriculum.

Problem

• robot design
• co-design
• morphology
• control
• parametric generation

Innovation

• Parametric Robot Design
• Co-design
• Reinforcement Learning
• Simulation-ready Model
• Digital Twin