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Value of Assistance for Grasping

Mohammad Masarwy, Yuval Goshen, David Dovrat, Sarah Keren

TL;DR

Value of Assistance for Grasping introduces a principled framework to quantify how a sensing action can improve grasp success under pose uncertainty. It defines $U^{VOA}_{x}(\beta_h,\beta_a)$ to capture the expected gain from a helper-provided observation, accounting for how $\hat{o}$ updates the actor's belief and shifts grasp choice. The authors instantiate VOA in a two-agent robotic setup using lidar and depth sensors, and validate the approach in both simulation and real-world experiments, including analyses of the predicted sensor function $\hat{\mathcal{O}}$ and various belief-update strategies. Results show VOA can reliably identify informative observations and improve grasp outcomes in most cases, though some IoU/contour-based similarity methods struggle with small, noisy objects; future work will address optimization, longer-horizon tasks, and multi-agent VOA applications.

Abstract

In multiple realistic settings, a robot is tasked with grasping an object without knowing its exact pose and relies on a probabilistic estimation of the pose to decide how to attempt the grasp. We support settings in which it is possible to provide the robot with an observation of the object before a grasp is attempted but this possibility is limited and there is a need to decide which sensing action would be most beneficial. We support this decision by offering a novel Value of Assistance (VOA) measure for assessing the expected effect a specific observation will have on the robot's ability to complete its task. We evaluate our suggested measure in simulated and real-world collaborative grasping settings.

Value of Assistance for Grasping

TL;DR

Value of Assistance for Grasping introduces a principled framework to quantify how a sensing action can improve grasp success under pose uncertainty. It defines to capture the expected gain from a helper-provided observation, accounting for how updates the actor's belief and shifts grasp choice. The authors instantiate VOA in a two-agent robotic setup using lidar and depth sensors, and validate the approach in both simulation and real-world experiments, including analyses of the predicted sensor function and various belief-update strategies. Results show VOA can reliably identify informative observations and improve grasp outcomes in most cases, though some IoU/contour-based similarity methods struggle with small, noisy objects; future work will address optimization, longer-horizon tasks, and multi-agent VOA applications.

Abstract

In multiple realistic settings, a robot is tasked with grasping an object without knowing its exact pose and relies on a probabilistic estimation of the pose to decide how to attempt the grasp. We support settings in which it is possible to provide the robot with an observation of the object before a grasp is attempted but this possibility is limited and there is a need to decide which sensing action would be most beneficial. We support this decision by offering a novel Value of Assistance (VOA) measure for assessing the expected effect a specific observation will have on the robot's ability to complete its task. We evaluate our suggested measure in simulated and real-world collaborative grasping settings.
Paper Structure (13 sections, 16 equations, 11 figures, 2 tables, 1 algorithm)

This paper contains 13 sections, 16 equations, 11 figures, 2 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Preliminaries
  3. Value of Assistance (VOA) for Grasping
  4. Empirical Evaluation
  5. Experimental Setting
  6. Evaluating the Predicted Sensor Function $\hat{\mathcal{O}}$
  7. Assessing VOA
  8. CONCLUSION
  9. Pose Belief
  10. Finding maximal VOA
  11. Observation Similarity Scores
  12. Evaluating Grasp Score
  13. Evaluating grasp score

Figures (11)

  • Figure 1: Collaborative Grasping Example.
  • Figure 2: Example stable poses.
  • Figure 3: Example grasp configurations from which the actor can attempt to grasp the object - each configuration is associated with a score, i.e., probability of success.
  • Figure 4: Example sensor configurations. Each column represents the RGB image [top] lidar reading [middle] and depth image [bottom] for a sensor configuration-object pose pair.
  • Figure 5: Evaluation objects
  • ...and 6 more figures

Theorems & Definitions (6)

  • Definition 1: Grasp Score
  • Definition 2: Pose Belief
  • Definition 3: Expected Grasp Score
  • Definition 4: Sensor Function
  • Definition 5: Belief Update
  • Definition 6: Value of Assistance (VOA) for Grasping