BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization

TL;DR

This paper introduces BODex, a highly efficient bilevel optimization system for robotic grasp synthesis that generates large-scale datasets and benchmarks, significantly improving grasp success rates in simulation and real-world tests.

Contribution

The authors develop a scalable, GPU-accelerated grasp synthesis method using bilevel optimization, enabling rapid dataset creation and benchmarking for dexterous robotic grasping.

Findings

Synthesized over 49 grasps per second on a single GPU.

Achieved over 75% success rate in simulation for multiple robotic hands.

Improved learning model success rate from 40% to 80% compared to previous datasets.

Abstract

Robotic dexterous grasping is important for interacting with the environment. To unleash the potential of data-driven models for dexterous grasping, a large-scale, high-quality dataset is essential. While gradient-based optimization offers a promising way for constructing such datasets, previous works suffer from limitations, such as inefficiency, strong assumptions in the grasp quality energy, or limited object sets for experiments. Moreover, the lack of a standard benchmark for comparing different methods and datasets hinders progress in this field. To address these challenges, we develop a highly efficient synthesis system and a comprehensive benchmark with MuJoCo for dexterous grasping. We formulate grasp synthesis as a bilevel optimization problem, combining a novel lower-level quadratic programming (QP) with an upper-level gradient descent process. By leveraging recent advances in CUDA-accelerated robotic libraries and GPU-based QP solvers, our system can parallelize thousands of grasps and synthesize over 49 grasps per second on a single 3090 GPU. Our synthesized grasps for Shadow, Allegro, and Leap hands all achieve a success rate above 75% in simulation, with a penetration depth under 1 mm, outperforming existing baselines on nearly all metrics. Compared to the previous large-scale dataset, DexGraspNet, our dataset significantly improves the performance of learning models, with a success rate from around 40% to 80% in simulation. Real-world testing of the trained model on the Shadow Hand achieves an 81% success rate across 20 diverse objects. The codes and datasets are released on our project page: https://pku-epic.github.io/BODex.