MoDeSuite: Robot Learning Task Suite for Benchmarking Mobile Manipulation with Deformable Objects
Yuying Zhang, Kevin Sebastian Luck, Francesco Verdoja, Ville Kyrki, Joni Pajarinen

TL;DR
MoDeSuite is a new benchmark suite for evaluating mobile manipulation algorithms on deformable objects, addressing a significant gap in robot learning research and enabling progress towards real-world applications.
Contribution
Introduction of MoDeSuite, the first standardized benchmark for mobile manipulation with deformable objects, including tasks, evaluation protocols, and demonstration of sim-to-real transfer.
Findings
State-of-the-art algorithms face challenges on MoDeSuite tasks.
Trained policies successfully transferred to real-world robot.
Benchmark facilitates future research in deformable object manipulation.
Abstract
Mobile manipulation is a critical capability for robots operating in diverse, real-world environments. However, manipulating deformable objects and materials remains a major challenge for existing robot learning algorithms. While various benchmarks have been proposed to evaluate manipulation strategies with rigid objects, there is still a notable lack of standardized benchmarks that address mobile manipulation tasks involving deformable objects. To address this gap, we introduce MoDeSuite, the first Mobile Manipulation Deformable Object task suite, designed specifically for robot learning. MoDeSuite consists of eight distinct mobile manipulation tasks covering both elastic objects and deformable objects, each presenting a unique challenge inspired by real-world robot applications. Success in these tasks requires effective collaboration between the robot's base and manipulator, as well…
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