Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer (2410.23701v1)

Abstract: This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative models for multi-finger grasping at scale, reliable real-world dexterous grasping remains challenging, with most methods degrading when deployed on hardware. An alternate strategy is to use discriminative grasp evaluation models for grasp selection and refinement, conditioned on real-world sensor measurements. This paradigm has produced state-of-the-art results for vision-based parallel-jaw grasping, but remains unproven in the multi-finger setting. In this work, we find that existing datasets and methods have been insufficient for training discriminitive models for multi-finger grasping. To train grasp evaluators at scale, datasets must provide on the order of millions of grasps, including both positive and negative examples, with corresponding visual data resembling measurements at inference time. To that end, we release a new, open-source dataset of 3.5M grasps on 4.3K objects annotated with RGB images, point clouds, and trained NeRFs. Leveraging this dataset, we train vision-based grasp evaluators that outperform both analytic and generative modeling-based baselines on extensive simulated and real-world trials across a diverse range of objects. We show via numerous ablations that the key factor for performance is indeed the evaluator, and that its quality degrades as the dataset shrinks, demonstrating the importance of our new dataset. Project website at: https://sites.google.com/view/get-a-grip-dataset.

• The paper introduces a novel sim-to-real transfer framework that leverages a 3.5M grasp dataset to train robust discriminative evaluators.
• It employs real-world sensor inputs in a vision-based evaluation pipeline to outperform traditional analytic and learning-based grasp models.
• The findings emphasize the critical role of large-scale data and evaluator design in advancing reliable multi-finger robotic manipulation.

Multi-Finger Grasp Evaluation and Sim-to-Real Transfer in Robotics

This paper introduces a significant advancement in robotic multi-finger grasping by proposing a robust framework for sim-to-real transfer using discriminative grasp evaluators. The authors bridge the prevalent gap between simulation and real-world performance in dexterous grasping, a known challenge in robotic manipulation.

Contributions of the Paper

1. Large-Scale Grasp Dataset: The authors have released a comprehensive dataset that includes 3.5 million grasp attempts across 4,300 unique objects. This dataset stands out by offering both positive and negative grasp examples, annotated with realistic perceptual data such as RGB images and trained NeRF models. The sheer scale of the dataset facilitates the training of robust discriminative models that generalize well from simulation to real-world applications.
2. Evaluation Pipeline: The paper focuses on discriminative grasp evaluation conditioned on real-world sensor inputs, a relatively under-exploited approach in multi-finger setups compared to parallel-jaw grasping. This pipeline is optimized for performance by training with data that reflect real-world measurement conditions, particularly emphasizing the structure of grasp failures and successes.
3. Outperformance of Baselines: The paper demonstrates that their vision-based grasp evaluators outperform both traditional physics-based analytic models and recent learning-based generative models. The results are substantiated with empirical evaluations conducted in both simulation environments and physical trials, confirming the models' robustness across a diverse object set.
4. Implications of Evaluator Reliance: Through comprehensive ablation studies, the authors highlight the pivotal role of evaluators in the grasping process. They note significant performance degradation when datasets are insufficiently large, reinforcing the importance of training scale for effective discriminative grasp evaluation.

Practical and Theoretical Implications

The proposed methodology enhances the understanding of the symbiotic relationship between dataset attributes and model performance in robotic grasping. This work reaffirms the critical role of large datasets in algorithm training, analogous to trends observed in other fields of AI. Practically, the presented system can be adapted for diverse applications ranging from anthropomorphic manipulation in service robotics to autonomous field robots handling irregular objects.

Theoretical implications include new insights into the efficacy of data-driven approaches over traditional models reliant on precise geometric information, which are often unreliable due to real-world sensor noise. The paper suggests a promising direction for future research in refining evaluator-based methods and integrating multi-modal sensor data to augment the grasp evaluation process.

Future Directions

Future research directions could involve extending dataset diversity to include non-rigid and articulated objects, addressing the complexities of grasping in cluttered or occluded environments. Additionally, there is potential to explore enhanced representation learning methods that further improve grasp prediction accuracy without the extensive data collection phase. Developing faster evaluation models to meet real-time requirements in more challenging manipulation tasks could also be crucial.

This paper contributes valuable knowledge and resources to the field of robotic manipulation, particularly in overcoming barriers to achieving reliable sim-to-real transfer in complex grasping scenarios.