FetchBench: A Simulation Benchmark for Robot Fetching

Beining Han, Meenal Parakh, Derek Geng, Jack A Defay, Gan Luyang, Jia Deng

Keywords: Grasping; Benchmark; Imitation Learning

Abstract: Fetching, which includes approaching, grasping, and retrieving, is a critical challenge for robot manipulation tasks. Existing methods primarily focus on table-top scenarios, which do not adequately capture the complexities of environments where both grasping and planning are essential. To address this gap, we propose a new benchmark FetchBench, featuring diverse procedural scenes that integrate both grasping and motion planning challenges. Additionally, FetchBench includes a data generation pipeline that collects successful fetch trajectories for use in imitation learning methods. We implement multiple baselines from the traditional sense-plan-act pipeline to end-to-end behavior models. Our empirical analysis reveals that these methods achieve a maximum success rate of only 20%, indicating substantial room for improvement. Additionally, we identify key bottlenecks within the sense-plan-act pipeline and make recommendations based on the systematic analysis.

Code: https://github.com/princeton-vl/FetchBench-CORL2024

Student Paper: yes

Submission Number: 79