EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-fingered Robot Hands (2206.15159v1)

Abstract: Autonomous grasping of novel objects that are previously unseen to a robot is an ongoing challenge in robotic manipulation. In the last decades, many approaches have been presented to address this problem for specific robot hands. The UniGrasp framework, introduced recently, has the ability to generalize to different types of robotic grippers; however, this method does not work on grippers with closed-loop constraints and is data-inefficient when applied to robot hands with multigrasp configurations. In this paper, we present EfficientGrasp, a generalized grasp synthesis and gripper control method that is independent of gripper model specifications. EfficientGrasp utilizes a gripper workspace feature rather than UniGrasp's gripper attribute inputs. This reduces memory use by 81.7% during training and makes it possible to generalize to more types of grippers, such as grippers with closed-loop constraints. The effectiveness of EfficientGrasp is evaluated by conducting object grasping experiments both in simulation and real-world; results show that the proposed method also outperforms UniGrasp when considering only grippers without closed-loop constraints. In these cases, EfficientGrasp shows 9.85% higher accuracy in generating contact points and 3.10% higher grasping success rate in simulation. The real-world experiments are conducted with a gripper with closed-loop constraints, which UniGrasp fails to handle while EfficientGrasp achieves a success rate of 83.3%. The main causes of grasping failures of the proposed method are analyzed, highlighting ways of enhancing grasp performance.

EfficientGrasp: Advancements in Data-Efficient Grasp Synthesis for Multi-fingered Robot Hands

The paper "EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-fingered Robot Hands" presents a novel approach to address autonomy in robotic grasping, particularly focusing on the challenge of grasping unknown novel objects. This work introduces EfficientGrasp, a data-efficient framework that surpasses prior models by improving generalization across different types of robot grippers, including those with closed-loop constraints which were previously unsupported.

Context and Motivation

Autonomous robotic grasping remains a pivotal area in robotics, primarily due to its implications for automation and interaction with diverse, unstructured environments. Prior frameworks generally necessitated extensive training on specific gripper models, limiting their applicability across heterogeneous robotic systems. Notably, the UniGrasp framework offered a generalized model yet faced limitations in terms of data efficiency and applicability to grippers with certain kinematic constraints.

Methodology

EfficientGrasp refines the capabilities of autonomous grasping by emphasizing data efficiency. The approach deviates from model-specific features and instead uses a gripper workspace feature, reducing memory usage significantly by 81.7% during training. The method employs a point cloud-based deep learning model—specifically, a Point Set Selection Network (PSSN)—to determine contact points on objects, leveraging object features and gripper workspace features extracted using PointNet.

This innovative feature handling enables EfficientGrasp to generalize effectively to newer gripper types, including those with complex, closed-loop kinematic structures like the RUTH hand. Furthermore, the paper introduces the use of model-free reinforcement learning (RL), specifically a soft actor-critic algorithm, to resolve inverse kinematics challenges without relying on predefined kinematic models, thereby extending the framework's applicability.

Results and Analysis

Simulation experiments using PyBullet highlight EfficientGrasp's superiority over prior approaches, presenting a 9.85% improvement in contact point accuracy and a 3.10% increase in grasping success rate devoid of closed-loop constraints, culminating in an 86.3% success rate. Real-world validation further underscores its robustness, especially with grippers that UniGrasp cannot accommodate, achieving an 83.3% success rate with the RUTH gripper. The robustness of grasp synthesis is quantitatively measured using a Grasp Quality Score (GQS), affirming the generation of near-optimal force closure grasps.

The efficiency of EfficientGrasp stems not only from reduced computational resources but also from its capability to adapt its grasp strategy across varied mechanical gripper architectures. This positions EfficientGrasp as a compelling advancement within grasping research, facilitating effective deployment in both theoretical and pragmatic contexts.

Practical and Theoretical Implications

The research delineates several pathways for future investigation and development. Practically, EfficientGrasp offers a scalable solution for industries employing robotics with diverse multi-fingered hands, enhancing manipulation capabilities in logistics, manufacturing, and service industries. Theoretical implications include exploring the influences of varying object mass distributions on discovered contact points, potentially integrating object dynamics more comprehensively into grasp planning algorithms.

The paper stimulates further exploration into refining reinforcement learning architectures for trajectory planning and force-closure grasps, which could lead to even more adaptable manipulation strategies in dynamic environments.

In conclusion, EfficientGrasp represents a significant stride in adaptive, efficient robotic grasping technology, promising wide-ranging enhancements in the dexterity and flexibility of robotic systems. It efficiently bridges the gap between theoretical generalization capabilities and the practical demands of multi-fingered robotic manipulation, setting a new benchmark for future advancements in autonomous grasp synthesis.