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When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection

Shaochen Wang, Zhangli Zhou, Zhen Kan

arXiv:2202.11911v3cs.ROcs.AIcs.CV

TL;DR

Visual grasp detection must account for both local object details and long-range relationships, challenges that the paper associates with CNN-based feature extraction. TF-Grasp applies transformer attention in a hierarchical encoder-decoder for grasp prediction. It reports improved results over CNN-based models, including 97.99% accuracy with depth and RGB data on Cornell, and promising generalization to unseen objects on a real robot.

  • Problem

    CNN-based grasp detection is described as prone to losing long-term dependencies, although grasp configurations are continuous and depend on features across objects and scenes.

    Source: §I · p. 1, A. Grasp Detection · p. 2

  • Method

    TF-Grasp uses transformer attention in an encoder-decoder framework to combine local and global features for pixel-level grasp prediction.

    Source: §I · p. 1, §III · pp. 3–4

  • Results

    97.99% accuracy is reported for TF-Grasp using depth and RGB data on Cornell, and the paper reports outperforming original CNN-based models.

    Source: B. Experimental Results and Analysis · p. 5, §V · p. 8

  • Takeaways & Limitations

    TF-Grasp works on a real robotic system and shows promising generalization to unseen objects.

    Source: §I · p. 2

    The current approach is limited to the parallel gripper.

    Source: §V · p. 8

Abstract

from arXiv · show

In this paper, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well suitable for visual grasping tasks. The first key design is that we adopt the local window attention to capture local contextual information and detailed features of graspable objects. Then, we apply the cross window attention to model the long-term dependencies between distant pixels. Object knowledge, environmental configuration, and relationships between different visual entities are aggregated for subsequent grasp detection. The second key design is that we build a hierarchical encoder-decoder architecture with skip-connections, delivering shallow features from encoder to decoder to enable a multi-scale feature fusion. Due to the powerful attention mechanism, the TF-Grasp can simultaneously obtain the local information (i.e., the contours of objects), and model long-term connections such as the relationships between distinct visual concepts in clutter. Extensive computational experiments demonstrate that the TF-Grasp achieves superior results versus state-of-art grasping convolutional models and attain a higher accuracy of 97.99% and 94.6% on Cornell and Jacquard grasping datasets, respectively. Real-world experiments using a 7DoF Franka Emika Panda robot also demonstrate its capability of grasping unseen objects in a variety of scenarios. The code and pre-trained models will be available at https://github.com/WangShaoSUN/grasp-transformer

I. INTRODUCTION

The introduction motivates grasp detection that combines local object geometry with long-range relationships, particularly in clutter. TF-Grasp adapts transformer attention to grasp prediction and is reported to perform well on benchmark datasets and real-robot trials.

I. INTRODUCTION

  • Grasp detection in clutter requires both partial geometric information and whole-object appearance, including spatial relationships among objects.Shape, position variation, and occlusion are identified as relevant challenges.Source: §I · p. 1
  • CNN-based grasp detectors emphasize local information, while stacking convolutional layers to enlarge receptive fields can reduce resolution and lose global details.The introduction contrasts this with the need to model long-distance relationships in grasping scenes.Source: §I · p. 1
  • TF-Grasp combines attention-based local and global feature learning with an encoder-decoder framework for visual grasp detection.The introduction describes the decoder as using long-range spatial dependencies to construct grasp poses.Source: §I · pp. 1–2
  • The authors report that TF-Grasp works on a real robotic system and shows promising generalization to unseen objects.The network generates parallel-gripper grasp poses in a single forward pass.Source: §I · p. 2
  • The paper presents vision transformers as an early approach to grasp detection and redesigns ViT to fuse local and global features for robotic sensing.The authors describe their work as one of the first attempts to apply vision transformers to grasp detection.Source: §I · p. 2
  • 97.99% accuracy is reported for TF-Grasp using depth and RGB data, alongside improved performance over state-of-the-art methods on popular grasping datasets.The introduction also reports appropriate poses for known or unknown objects in single or cluttered environments.Source: §I · p. 2

II. RELATED WORK

Related work traces grasp detection from geometry-driven methods requiring object models to deep-learning approaches, including CNN-based systems. It identifies long-term dependency loss in CNN feature extraction as a challenge for continuous grasp configurations.

II. RELATED WORK

  • The related-work section reviews advances in robotic grasping and transformer applications.Its grasp-detection discussion includes CNN use of depth and RGB information to improve performance.Source: §II · p. 2

A. Grasp Detection

  • Grasp detection research progressed from geometry-driven contour analysis, which assumes available object models, to deep-learning and CNN-based approaches.The section describes two-stage candidate evaluation, pose regression, multimodal inputs, and fully convolutional real-time detection among these approaches.Source: A. Grasp Detection · p. 2
  • Continuous grasp configurations create a feature-extraction challenge, and the section identifies lost long-term dependencies as a major drawback of CNN-based grasp detection.The number of possible grasp options is described as infinite.Source: A. Grasp Detection · p. 2

B. Transformer

Transformers are presented as a way to model global information, while vision applications face challenges from differences between visual signals and word tokens and from pixel-level computational demands. Prior vision transformers motivate the paper's end-to-end encoder-decoder approach to grasp prediction.

B. Transformer

  • Vision transformers have been applied to image classification, detection, and segmentation, but visual signals differ from word tokens and pixel-level inputs can be computationally costly.The section describes ViT's non-overlapping image patches and Swin-Transformer's local attention with shifted windows.Source: B. Transformer · p. 2
  • The paper identifies grasp detection as an unaddressed application for aggregating information across entire inputs and proposes end-to-end transformer encoder-decoder prediction.It reports higher grasp success than state-of-the-art CNN counterparts.Source: B. Transformer · p. 2
  • The TF-Grasp overview shows an end-effector camera image as input and a pixel-level grasp representation as output.The figure caption states this input-output path.Source: Figure 1 · p. 3

III. METHOD

TF-Grasp uses a hierarchical encoder-decoder with local and shifted-window attention to combine local visual detail with long-range context. Skip-connections fuse features across stages for pixel-level grasp prediction.

III. METHOD

  • The grasp is represented by pixel-level quality, width, and orientation maps, enabling the model to predict grasp position, angle, and width.The confidence map identifies grasp success at each pixel, while the other maps provide gripper width and orientation.Source: §III · p. 3
  • TF-Grasp balances local and global scene information with window attention and shifted-window attention in a cascaded encoder-decoder for pixel-level grasp prediction.The encoder learns contextual representations; the decoder uses extracted features to predict grasps.Source: §III · p. 3
  • Local window attention extracts features within non-overlapping regions, while shifted windows enable cross-window interactions.Patch merging between blocks reduces feature-map resolution and builds a hierarchical representation.Source: §III · p. 4
  • Three parallel decoder heads produce full-image heatmaps for grasp confidence, gripper angle, and gripper width.The model learns these maps by minimizing distances between predictions and ground truth.Source: §III · p. 4
  • Skip-connections concatenate encoder features into corresponding decoder stages to fuse multi-scale features for grasp posture prediction.The fused features are processed by attention blocks during decoding.Source: §III · p. 4
  • Pixel-level grasp prediction selects the best posture in one forward pass without generating multiple grasp candidates.This representation is described as saving computation expense.Source: §III · p. 4

IV. EXPERIMENTS

The experiments evaluate TF-Grasp on two grasping datasets and a real Franka Panda manipulator. They ask whether transformer-based grasp detection outperforms CNN-based models and visualize learned attention maps.

IV. EXPERIMENTS

  • Experiments evaluate TF-Grasp on two popular grasping datasets and a real Franka Panda robotic manipulator.The section frames the evaluation around performance against CNN-based models and what may explain any advantage.Source: §IV · pp. 4–5
  • The experiments ask whether transformer-based grasp detection is better than CNN-based models and what makes it outperform them.Source: §IV · pp. 4–5
  • The attention heatmaps are presented as showing that the transformer learns concepts beneficial for grasping.Source: Figure 3 · p. 5

A. Datasets and Experiment Setup

Experiments use the Cornell and Jacquard grasping datasets, with grasp correctness assessed by angle and overlap criteria. TF-Grasp is trained on normalized 224×224 images and produces three pixel-wise output maps.

A. Datasets and Experiment Setup

  • Cornell contains 885 images, while Jacquard contains over 50k images spanning 11k object categories and over 1 million grasp labels.Cornell images are 640 × 480; the paper uses augmentation to reduce overfitting on this relatively small dataset.Source: A. Datasets and Experiment Setup · p. 5
  • A predicted grasp is considered correct when its angle discrepancy from ground truth is within 30° and its Jaccard index, defined in Eq. (8), is greater than 0.25.Source: A. Datasets and Experiment Setup · p. 5
  • TF-Grasp maps a normalized 224×224 input image to three pixel-wise maps and is trained with mean squared error and AdamW.The encoder and decoder each contain four Swin-attention blocks; the batch size is 64 and the patch size is 4.Source: A. Datasets and Experiment Setup · p. 5
  • The grasp angle is represented by sin(2Θ) and cos(2Θ), and the implementation runs in PyTorch on Ubuntu 18.04 with an Intel Core i9 CPU and NVIDIA 3090 GPU.The reported angle is recovered from the two components using arctan.Source: A. Datasets and Experiment Setup · p. 5

B. Experimental Results and Analysis

TF-Grasp reports higher accuracy than conventional CNN models on Cornell and Jacquard, including 97.99% with RGB and depth on Cornell and 94.6% on Jacquard. Its comparisons also cover unseen objects and qualitative grasp predictions in multi-object scenes.

B. Experimental Results and Analysis

  • 97.99% accuracy with RGB and depth on Cornell and 94.6% on Jacquard; TF-Grasp is reported as higher than conventional CNN models on both datasets.The Cornell result uses five-fold cross-validation, and the Jacquard comparison is summarized in Table II.Source: B. Experimental Results and Analysis · pp. 5–6, Table II · p. 6
  • On selected unseen Cornell objects, the transformer outperforms original convolutions and is marginally better or on-par with the residual network.The comparison uses an object-wise split; each object subset is evaluated five times.Source: B. Experimental Results and Analysis · p. 6
  • In multi-object examples, CNN predictions often miss suitable gripper angle or width, while transformer heatmaps identify graspable areas and favor easy-to-grasp regions.The authors attribute the contrast to attention capturing global and detailed object features, whereas stacked CNN layers can lose position and shape information.Source: B. Experimental Results and Analysis · p. 6

C. Visualization Analysis

The visualization analysis highlights attention to graspable object contours and reports successful localization of novel objects in clutter. It also presents comparisons of model accuracy and CNN- versus transformer-based grasping visualizations.

C. Visualization Analysis

  • Fig. 5 compares model accuracy as feature extractors on selected objects.Source: Figure 5 · p. 6
  • Attention heatmaps highlight object edges and contours as graspable regions while the model disregards irrelevant details.For chairs, the method assigns higher grasp quality to their edges.Source: C. Visualization Analysis · p. 6
  • Fig. 4 presents a visualization comparison of CNN- and transformer-based grasping models.Source: Figure 4 · p. 7
  • TF-Grasp is reported to locate graspable objects in clutter and work well on objects unseen during training.The passage describes a pick-and-place task on the Franka manipulator.Source: C. Visualization Analysis · p. 7
  • The visualization results are described as producing more general and robust predictions that contribute to detection accuracy.Source: C. Visualization Analysis · p. 7

D. Ablation Studies

The ablation compares transformer grasp detection with and without skip-connections on Cornell and Jacquard. Skip-connections perform better across all input modes, and the authors associate multi-scale fusion with improved grasp prediction quality.

D. Ablation Studies

  • Skip-connections outperform their absence across all input modes on the Cornell and Jacquard grasping datasets.Source: D. Ablation Studies · p. 7
  • The transformer attention mechanism builds inter-relationships within each layer and incorporates global features.Source: D. Ablation Studies · p. 7
  • Skip-connections globally fuse multi-scale representations, with refinement and contextual features contributing to final grasp prediction quality.Source: D. Ablation Studies · p. 7

E. Grasping in Real World Scenarios

In physical trials with a Franka Panda robot and RealSense D435 camera, TF-Grasp succeeded in 152 of 165 grasp attempts. The system processes depth images with missing pixels before producing a grasp posture for execution.

E. Grasping in Real World Scenarios

V. DISCUSSION AND CONCLUSION

The discussion argues that transformer-based grasp detection captures global dependencies and outperforms original CNN-based models. Its current scope is parallel grippers, with future work aimed at other gripper types.

V. DISCUSSION AND CONCLUSION

  • The proposed approach outperforms original CNN-based models, while the discussion emphasizes transformers' capacity to capture global dependencies.Source: §V · p. 8
  • The current approach is limited to parallel grippers; future work will target other grippers, including five-finger dexterous hands.Source: §V · p. 8
  • The authors describe attention propagation as a way to represent context better than CNN-based counterparts.Source: §V · p. 8

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