Tactile-Based Insertion for Dense Box-Packing (1909.05426v1)

Abstract: We study the problem of using high-resolution tactile sensors to control the insertion of objects in a box-packing scenario. We propose a new system based on a tactile sensor GelSlim for the dense packing task. In this paper, we propose an insertion strategy that leverages tactile sensing to: 1) safely probe the box with the grasped object while monitoring incipient slip to maintain a stable grasp on the object. 2) estimate and correct for residual position uncertainties to insert the object into a designated gap without disturbing the environment. Our proposed methodology is based on two neural networks that estimate the error direction and error magnitude, from a stream of tactile imprints, acquired by two GelSlim fingers, during the insertion process. The system is trained on four objects with basic geometric shapes, which we show generalizes to four other common objects. Based on the estimated positional errors, a heuristic controller iteratively adjusts the position of the object and eventually inserts it successfully without requiring prior knowledge of the geometry of the object. The key insight is that dense tactile feedback contains useful information with respect to the contact interaction between the grasped object and its environment. We achieve high success rate and show that unknown objects can be inserted with an average of 6 attempts of the probe-correct loop. The method's ability to generalize to novel objects makes it a good fit for box packing in warehouse automation.

• The paper presents a tactile sensing methodology that uses GelSlim sensors and neural networks to estimate and correct positional errors during insertion.
• The system demonstrates robust performance by successfully inserting unknown geometric objects within an average of six attempts.
• Implications include enhanced packing efficiency in warehouse automation and a foundation for developing adaptive, tactile-based control strategies.

Analysis of "Tactile-Based Insertion for Dense Box-Packing"

The paper "Tactile-Based Insertion for Dense Box-Packing" by Siyuan Dong and Alberto Rodriguez investigates a methodology aimed at enhancing the precision and reliability of robotic box-packing tasks using high-resolution tactile sensors. Specifically, it focuses on using tactile feedback to address insertion challenges in dense packing scenarios, typically encountered in warehouse automation. This paper proposes a novel approach that leverages the tactile sensing capability of the GelSlim sensor, alongside neural networks, to improve packing efficiency by detecting and correcting for positional uncertainties during the insertion process.

Methodology Overview

The paper details an insertion strategy where tactile sensing plays a pivotal role in:

1. Probing the box with a grasped object while preventing slip to maintain a stable grasp.
2. Estimating residual positional errors and making necessary position adjustments to accurately insert the object into its designated position without causing environmental disturbances.

To achieve these tasks, the authors developed a system using two neural networks. These networks process tactile data to estimate the direction and magnitude of positioning errors. The tactile imprints gauged by the GelSlim sensor fingers during insertion provide crucial sensory feedback, processed in real-time by the neural networks. This method is designed to be versatile, trained on basic geometric-shaped objects, and capable of generalizing to other objects of common form. Importantly, the proposed heuristic controller employs these estimations to iteratively adjust the positional strategy in achieving successful insertions.

Numerical Results and Claims

The system demonstrated robust performance across a variety of objects. Unknown objects were successfully inserted within an average of six attempts, showcasing the efficacy of the probe-correct loop facilitated by the estimated errors. This method's standout feature is its ability to adapt to novel forms without prior geometric data of the target objects. The claim underlines the premise that high-resolution tactile data deliver invaluable insight into the dynamics of the interaction between grasped objects and their environments, a point thereby substantiated by the experimental results.

Implications and Future Directions

On a practical front, the implications of the research are significant for box-packing operations in warehouse automation, where maximizing packing density while minimizing errors and collateral disturbances to neighboring objects remains crucial. Tactile-based solutions, as demonstrated, offer a feasible pathway to navigate these challenges, providing a complement or alternative to vision-based systems that may suffer in precision under occlusive or complex sensory conditions.

Theoretically, this paper contributes to an expanding field of robotics research, highlighting how tactile feedback can bridge the gaps unaddressed by visual systems alone, particularly in tasks demanding high precision. Future developments in AI might expand upon these foundations, with more complex adaptive controllers potentially arising from reinforcement learning frameworks, advancing the response strategy beyond heuristic patterns. Exploring broader object typologies, integrating compliance handling, and scaling the methodology for varied industries are immediate areas warranting further exploration.

In conclusion, "Tactile-Based Insertion for Dense Box-Packing" presents a compelling strategy that enhances the robustness of packing operations by harnessing tactile sensor data. With the focus on reducing spatial uncertainty without prior object knowledge, this work lays an essential building block towards intelligent automation systems in complex environments.