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The impact of tactile sensor configurations on grasp learning efficiency -- a comparative evaluation in simulation

Eszter Birtalan, Miklós Koller

TL;DR

This work tackles how tactile sensor density and layout on a robotic hand influence grasp-learning efficiency. It leverages two distinct simulation platforms, PyBullet with PPO on an MPL hand and MuJoCo with DDPG+HER on a Shadow Hand, to evaluate six sensor configurations on a grasp-and-lift task, using bootstrapped IQM and converged metrics for robust comparison. Across both setups, tactile information generally aids learning, but the degree of benefit depends on layout and density, with Configuration 2 showing the strongest, most consistent performance and indicating that high-density coverage is not always necessary. The findings offer practical guidance for designing prosthetic hands, suggesting that thoughtfully distributed, lower-density sensor arrays can achieve near-optimal learning performance while preserving space for additional modalities.

Abstract

Tactile sensors are breaking into the field of robotics to provide direct information related to contact surfaces, including contact events, slip events and even texture identification. These events are especially important for robotic hand designs, including prosthetics, as they can greatly improve grasp stability. Most presently published robotic hand designs, however, implement them in vastly different densities and layouts on the hand surface, often reserving the majority of the available space. We used simulations to evaluate 6 different tactile sensor configurations with different densities and layouts, based on their impact on reinforcement learning. Our two-setup system allows for robust results that are not dependent on the use of a given physics simulator, robotic hand model or machine learning algorithm. Our results show setup-specific, as well as generalized effects across the 6 sensorized simulations, and we identify one configuration as consistently yielding the best performance across both setups. These results could help future research aimed at robotic hand designs, including prostheses.

The impact of tactile sensor configurations on grasp learning efficiency -- a comparative evaluation in simulation

TL;DR

This work tackles how tactile sensor density and layout on a robotic hand influence grasp-learning efficiency. It leverages two distinct simulation platforms, PyBullet with PPO on an MPL hand and MuJoCo with DDPG+HER on a Shadow Hand, to evaluate six sensor configurations on a grasp-and-lift task, using bootstrapped IQM and converged metrics for robust comparison. Across both setups, tactile information generally aids learning, but the degree of benefit depends on layout and density, with Configuration 2 showing the strongest, most consistent performance and indicating that high-density coverage is not always necessary. The findings offer practical guidance for designing prosthetic hands, suggesting that thoughtfully distributed, lower-density sensor arrays can achieve near-optimal learning performance while preserving space for additional modalities.

Abstract

Tactile sensors are breaking into the field of robotics to provide direct information related to contact surfaces, including contact events, slip events and even texture identification. These events are especially important for robotic hand designs, including prosthetics, as they can greatly improve grasp stability. Most presently published robotic hand designs, however, implement them in vastly different densities and layouts on the hand surface, often reserving the majority of the available space. We used simulations to evaluate 6 different tactile sensor configurations with different densities and layouts, based on their impact on reinforcement learning. Our two-setup system allows for robust results that are not dependent on the use of a given physics simulator, robotic hand model or machine learning algorithm. Our results show setup-specific, as well as generalized effects across the 6 sensorized simulations, and we identify one configuration as consistently yielding the best performance across both setups. These results could help future research aimed at robotic hand designs, including prostheses.
Paper Structure (10 sections, 1 equation, 6 figures, 2 tables)

This paper contains 10 sections, 1 equation, 6 figures, 2 tables.

Table of Contents

  1. Results
  2. PyBullet results
  3. MuJoCo results
  4. Discussion
  5. Methods
  6. PyBullet setup
  7. Reinforcement learning algorithm
  8. MuJoCo setup
  9. Reinforcement learning algorithm
  10. Figure legends

Figures (6)

  • Figure 1: Schematic overview of the methodology used to evaluate tactile sensor configurations. The same configurations have been implemented on two 3D hand models using different physics simulators. The effects of the different sensor layouts were evaluated using two reinforcement learning algorithms to ensure the robustness of the results and compared against configurations, setups and control groups.
  • Figure 2: Visual representation of the sensor configurations implemented in both setups. A-F: Sensor configurations corresponding to Configurations 1-6, respectively. The number and layout of the sensors shown on the index finger are repeated across the other digits.
  • Figure 3: Sample-efficiency curves showing success rates as a function of epochs in the PyBullet setup. A-F: Sample-efficiency curves corresponding to Configurations 1-6 (shown on diagrams in the right hand corner of each panel), respectively. Thick lines show the IQM scores (bootstrap replication 50000), with the confidence intervals represented as shaded areas. Yellow is used to show results from the sensorized group, while blue is used for the controls. Training occurred for 500000 timesteps, roughly generating 500 epochs. Both main and control groups represent the result of 10 seeds.
  • Figure 4: Visual representation of the converged success rates in the PyBullet setup. A-B: The Median, IQM and Mean of the converged success rates (bootstrap replication 1000) for each Configuration in the sensorized and control groups, respectively. Confidence intervals are represented as shaded areas. Each configuration was run with 10 seeds; the exact values represented here can be found in Table \ref{['tab:table1']}.
  • Figure 5: Sample-efficiency curves showing success rates as a function of epochs in the MuJoCo setup. A-F: Sample-efficiency curves corresponding to Configurations 1-6 (shown on diagrams in the right hand corner of each panel), respectively. Thick lines show the IQM scores (bootstrap replication 50000), with the confidence intervals represented as shaded areas. Yellow is used to show results from the sensorized group, while blue is used for the controls. Training occurred for 5000000 timesteps, generating 500 epochs. Both main and control groups represent the result of 10 seeds.
  • ...and 1 more figures