Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Generating Future Observations to Estimate Grasp Success in Cluttered Environments

Daniel Fernandes Gomes, Wenxuan Mou, Paolo Paoletti, Shan Luo

TL;DR

This work tackles grasp success estimation in cluttered environments by contrasting a model-free end-to-end predictor with a model-based pipeline that forecasts a future observation $\hat{I}_d$ prior to grasping and uses it to determine success. Using a robot-arm bin-picking setup, the authors collect $24{,}364$ grasps with autonomous annotations to evaluate methods on pre-grasp, during-grasp, and post-grasp data. The model-free approach achieves up to $72\%$ accuracy, while the model-based approach with hallucinated future views reaches $82\%$ on validation, a substantial improvement that highlights the value of future-view prediction for manipulation tasks. These results suggest that self-supervised predictive models can improve data efficiency and robustness in grasping under clutter, with future work aiming to enhance generated observations via diffusion/transformer techniques and to integrate optical tactile sensing for Sim2Real transfer.

Abstract

End-to-end self-supervised models have been proposed for estimating the success of future candidate grasps and video predictive models for generating future observations. However, none have yet studied these two strategies side-by-side for addressing the aforementioned grasping problem. We investigate and compare a model-free approach, to estimate the success of a candidate grasp, against a model-based alternative that exploits a self-supervised learnt predictive model that generates a future observation of the gripper about to grasp an object. Our experiments demonstrate that despite the end-to-end model-free model obtaining a best accuracy of 72%, the proposed model-based pipeline yields a significantly higher accuracy of 82%.

Generating Future Observations to Estimate Grasp Success in Cluttered Environments

TL;DR

This work tackles grasp success estimation in cluttered environments by contrasting a model-free end-to-end predictor with a model-based pipeline that forecasts a future observation prior to grasping and uses it to determine success. Using a robot-arm bin-picking setup, the authors collect grasps with autonomous annotations to evaluate methods on pre-grasp, during-grasp, and post-grasp data. The model-free approach achieves up to accuracy, while the model-based approach with hallucinated future views reaches on validation, a substantial improvement that highlights the value of future-view prediction for manipulation tasks. These results suggest that self-supervised predictive models can improve data efficiency and robustness in grasping under clutter, with future work aiming to enhance generated observations via diffusion/transformer techniques and to integrate optical tactile sensing for Sim2Real transfer.

Abstract

End-to-end self-supervised models have been proposed for estimating the success of future candidate grasps and video predictive models for generating future observations. However, none have yet studied these two strategies side-by-side for addressing the aforementioned grasping problem. We investigate and compare a model-free approach, to estimate the success of a candidate grasp, against a model-based alternative that exploits a self-supervised learnt predictive model that generates a future observation of the gripper about to grasp an object. Our experiments demonstrate that despite the end-to-end model-free model obtaining a best accuracy of 72%, the proposed model-based pipeline yields a significantly higher accuracy of 82%.
Paper Structure (6 sections, 3 figures, 1 table)

This paper contains 6 sections, 3 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. EXPERIMENTS
  3. Challenges with model-free grasp success prediction
  4. Predicting success when the grasp occurs
  5. Predicting grasp success from hallucinated observations
  6. Conclusions and Future Work

Figures (3)

  • Figure 1: A robot arm randomly grasping various plastic objects from a bin, autonomously annotated using the gripper feedback.
  • Figure 2: The predictive model generates the during observation, $\hat{I}_{d}$, given the grasp command (gripper position, orientation and aperture) and before observation, $I_{b}$. Then, the grasp success estimator, classifies this candidate grasp into successful or failure.
  • Figure 3: During observations generated by the predictive model, given the before observation and arm configuration. While the background sharpness is highly due to the skip connection, as proposed in VisualForesight, we note the model capacity of omiting the gripper in before.