Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning (2311.09555v1)

Abstract: Object grasping is an important ability required for various robot tasks. In particular, tasks that require precise force adjustments during operation, such as grasping an unknown object or using a grasped tool, are difficult for humans to program in advance. Recently, AI-based algorithms that can imitate human force skills have been actively explored as a solution. In particular, bilateral control-based imitation learning achieves human-level motion speeds with environmental adaptability, only requiring human demonstration and without programming. However, owing to hardware limitations, its grasping performance remains limited, and tasks that involves grasping various objects are yet to be achieved. Here, we developed a cross-structure hand to grasp various objects. We experimentally demonstrated that the integration of bilateral control-based imitation learning and the cross-structure hand is effective for grasping various objects and harnessing tools.

• The paper introduces a novel cross-structure hand design that secures diverse objects with a simple yet effective underactuated mechanism.
• It employs bilateral control-based imitation learning to mimic human force dynamics, achieving a 95% success rate in grasping varied objects.
• The method demonstrates practical applications in tasks like pick-and-place and letter-writing, outperforming conventional force control strategies.

Overview of Soft and Rigid Object Grasping Using a Cross-Structure Hand with Bilateral Control-Based Imitation Learning

The paper "Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning" proposes a novel methodology aimed at enhancing the grasping capabilities of robots. It introduces the integration of bilateral control-based imitation learning with a specially designed cross-structure hand, allowing robots to achieve fine dexterity in manipulating both rigid and soft objects.

Methodology

This research focuses on addressing the longstanding challenge in robotics of dynamically adjusting grasping forces without predefined programming for unknown objects. Traditional approaches have often struggled with the flexibility required for such varied tasks. The authors propose a solution involving a unique cross-structure gripper that allows a wide range of objects to be grasped through a single degree of freedom. This innovative design enables the robot hand to close completely, providing more secure and precise control over thin objects by leveraging a mechanism that allows fingers to cross each other.

A central component of the methodology is the implementation of bilateral control-based imitation learning. This approach allows the robot to learn and mimic human-like force adjustments through demonstration, bypassing the need for explicit coding of each task or environmental adaptation. The method exploits the concept of 4-channel bilateral control, separating human-applied forces and environmental reaction forces, thereby facilitating efficient data collection and high-level mimicry of human force dynamics.

Experimental Validation

The experimental framework encompassed two primary tasks to test the capability of the system: a pick-and-place operation involving diverse object textures and shapes, and a letter-writing task requiring precision with tool usage. The paper reports high success rates in these tasks, particularly when the force control capabilities of the gripper were utilized, with the grasping of various unknown objects achieving a 95% success rate. In the letter-writing task, which demanded rigid grasping and delicate maneuvering, the method significantly outperformed baseline approaches that did not leverage force control.

Contributions and Implications

This research asserts several significant contributions:

1. Cross-Structure Hand Design: The design of a gripper with a simple underactuated mechanism that skillfully combines rigid body construction with a flexible grasping path to accommodate various object sizes and rigidity levels.
2. Bilateral Control-Based Learning: Advancing the field of imitation learning by utilizing bilateral control to capture nuanced human force applications, enhancing the adaptability and efficiency of robots in real-world tasks.

The practical implications of this work extend to domains requiring complex robotic manipulation, such as logistics, manufacturing, and healthcare, where the ability to handle unpredictable objects is critical. Theoretically, it presents a compelling case for evolving imitation learning frameworks, highlighting the potential of using human-like adaptability in robotic systems.

Future Directions

While promising, this work prompts further exploration into enhanced models, potentially integrating visual data for more context-aware manipulation. The authors suggest that integrating large-scale LLMs could refine the system’s decision-making and task planning capabilities. There is potential for further research into refining the learning algorithms to decrease reliance on vast amounts of demonstration data, thereby increasing deployment efficiency.

Overall, this paper conveys a robust approach to enhancing robotic grasping capabilities leveraging a novel combination of control theory and learning algorithms, offering significant advancements in the field of autonomous robotics.