Reactive Human-to-Robot Handovers of Arbitrary Objects (2011.08961v2)

Abstract: Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and rigidity. In this paper, we present a vision-based system that enables reactive human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally-consistent grasp generation to ensure reactivity and motion smoothness. Our system is robust to different object positions and orientations, and can grasp both rigid and non-rigid objects. We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects. More results and videos can be found at https://sites.google.com/nvidia.com/handovers-of-arbitrary-objects.

• The paper introduces a vision-based system that leverages closed-loop motion planning and real-time grasp generation to manage dynamic handovers.
• It employs a segmentation model to accurately distinguish between human hand and object, adapting grasp strategies through symbolic planning.
• Experimental evaluations with varied object types demonstrate the system’s robustness and its potential in assistive and industrial robotics.

Overview of "Reactive Human-to-Robot Handovers of Arbitrary Objects"

The research paper presents a novel vision-based system aimed at facilitating human-to-robot handovers involving arbitrary objects. This work addresses a critical gap within the domain of robotics, where the manipulation and transfer of objects from humans to robots is constrained by the diversity of object appearances, sizes, and deformability.

System Design and Approach

At the core of this system is a sophisticated combination of closed-loop motion planning and real-time, temporally consistent grasp generation. By leveraging advancements in robot perception and grasping technology, the paper introduces a system capable of accommodating the dynamic nature of human environments, which often involve objects that are unknown to the robot prior to the handover. The approach effectively handles various challenges intrinsic to human-robot handovers. Unlike static object grasping, human-to-robot handovers require the robot to interact with objects potentially in motion, partially obscured by the human’s hand, and with approach paths constrained by the human's pose.

Methodological Contributions

The paper details several key contributions, including:

1. Hand and Object Segmentation: A segmentation model is employed to accurately distinguish between the hand and the object using RGB images, facilitating object grasping from partial view data acquired from the depth camera.
2. Grasp Generation: The extension of 6-DOF GraspNet enables the system to generate temporally consistent grasp points over time, ensuring reactivity and stability during handover operations. This is crucial for dealing with potential movements of the object after the robot has begun to interact with it.
3. Motion Planning and Grasp Selection: The paper demonstrates a reactive task model that selects appropriate grasp strategies dynamically using symbolic planning. This model accounts for motion adaptations based on real-time feedback, ensuring a smooth and continuous interaction between the human and robot.

Experimental Evaluation

Comprehensive experiments are described focusing on the robustness and adaptability of the system:

• Systematic Evaluation: Tests were conducted with three objects of varying shapes and sizes, and in different orientations, to assess performance in a controlled environment. Success was primarily determined by grasp success rate and time.
• User Study: A user paper with six participants focused on handovers involving a diverse assortment of 26 household objects, and examined both pre-defined and freeform object presentations. Results indicated high adaptability and reliability in object handover tasks, although challenges in speed and grasping smaller or more complexly shaped objects were noted.

Implications and Future Directions

The implications of this research are substantial for fields requiring human-robot interactions, such as elderly care, assistive technologies, and industrial automation. The system’s generalizability to arbitrary objects expands its utility beyond conventional robotic applications, suggesting potential for adaptation to myriad customized environments. Future enhancements could explore optimizing computational efficiency to allow faster robotic responses and incorporating more robust perception algorithms to reduce segmentation errors, particularly in complex or cluttered environments.

Conclusion

The paper advances the state-of-the-art in robotic handover systems by significantly expanding the repertoire of objects that can be handled and making real-time adaptations possible. It addresses fundamental problems in human-robot cooperation, which is pivotal for creating seamless, integrated robotic assistants in diverse applications. The data-driven methodological framework and comprehensive evaluation encourage further research into real-time adaptive systems that support intricate human-robot interactions.