Contact-Guided Robot-to-Human Object Handover: Maximizing Visibility and Reachability of Human Preferred Contacts

Core Concepts

ContactHandover, a robot-to-human handover system that predicts human contact points on objects and uses this information to guide the robot's grasp selection and object delivery, in order to maximize the visibility and reachability of human preferred contact areas during handover.

Abstract

ContactHandover is a two-phase robot-to-human object handover system that aims to maximize the visibility and reachability of human preferred contact areas on the object being handed over.

In the first phase, the grasping phase, ContactHandover predicts both 6-DoF robot grasp poses and a 3D affordance map of human contact points on the object. It then re-ranks the robot grasp poses by penalizing those that block human contact points, and executes the highest ranking grasp.

In the second phase, the delivery phase, ContactHandover computes the robot end effector pose that maximizes human contact points close to the human receiver while minimizing the human arm joint torques and displacements.

The authors evaluate ContactHandover on 27 diverse household objects and show that it achieves better visibility and reachability of human contacts compared to several baselines. Key findings include:

• Grasp re-ranking to avoid occluding human contacts improves the availability of human preferred contact areas during handover.
• Estimating the handover orientation to align human contacts with the receiver further increases the reachability of these contacts.
• Clustering human contacts is useful for objects with bimodal contact distributions, allowing the robot to focus on orienting the dominant contact cluster towards the human.
• ContactHandover can generalize to unseen object shapes and classes by leveraging the learned human contact preferences.

The authors propose two computational metrics, visibility and reachability, to quantitatively evaluate the quality of a handover in terms of aligning with human preferences. ContactHandover outperforms several ablations on these metrics, demonstrating its effectiveness in enabling natural and ergonomic robot-to-human object handovers.

Stats

The robot should choose a stable grasp on the head of the hammer and leave enough room on the handle of the hammer for the receiver.
The robot should orient the handle of the hammer to the receiver instead of the head.

Quotes

"A successful handover requires the robot to maintain a stable grasp on the object while making sure the human receives the object in a natural and easy-to-use manner."
"The robot should deliver the object in a way that most natural grasping areas are visible and reachable from the receiver."

Key Insights Distilled From

ContactHandover

by Zixi Wang,Ze... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01402.pdf

Deeper Inquiries

How could ContactHandover be extended to handle dynamic handover scenarios where the human's position or pose changes during the handover process

To handle dynamic handover scenarios where the human's position or pose changes during the handover process, ContactHandover could incorporate real-time tracking of the human's movements. This could involve using sensors like depth cameras or motion capture systems to continuously monitor the human's position and pose. By integrating this tracking data into the system, ContactHandover can adjust the robot's handover strategy in response to any changes in the human's location or orientation. Additionally, ContactHandover could implement predictive algorithms that anticipate potential changes in the human's pose based on historical data or movement patterns, enabling the robot to proactively adapt its handover approach.

What other modalities beyond vision, such as force/tactile sensing, could be leveraged to further improve the robot's understanding of human grasp preferences

Beyond vision, ContactHandover could leverage force and tactile sensing modalities to enhance the robot's understanding of human grasp preferences. By integrating force sensors in the robot's gripper or end effector, ContactHandover can detect the amount of force exerted by the human during the handover process. This information can help the robot adjust its grasp strength and posture to ensure a comfortable and secure handover. Tactile sensors on the robot's fingers or palm can provide feedback on the contact forces and pressure distribution during the handover, enabling ContactHandover to optimize the grasp configuration based on tactile feedback. By combining vision with force and tactile sensing, ContactHandover can create a more comprehensive and adaptive handover system that takes into account both visual cues and physical interactions.

How might the human contact prediction model be improved to better capture the influence of object function and task context on grasp affordances

To improve the human contact prediction model in ContactHandover to better capture the influence of object function and task context on grasp affordances, several enhancements can be considered. Firstly, incorporating object semantics and contextual information into the prediction model can help account for the functional aspects of the object. By analyzing object properties, such as shape, material, and intended use, the model can better predict human contact points based on the object's purpose and design. Additionally, integrating task-specific cues and constraints into the prediction process can enhance the model's ability to anticipate how humans interact with objects in different scenarios. By training the model on a diverse range of tasks and contexts, ContactHandover can learn to adapt its predictions based on the specific task requirements and environmental conditions, leading to more accurate and context-aware grasp affordance estimations.