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GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

Chao Tang, Dehao Huang, Wenqi Ge, Weiyu Liu, Hong Zhang

arXiv:2307.13204v3cs.RO

TL;DR

Existing task-oriented grasping methods use semantic knowledge built around predefined concept sets, which limits generalization to novel concepts. GraspGPT uses an LLM’s open-ended semantic knowledge to generalize to those concepts, and the paper reports stronger novel-concept results and real-world validation.

  • Problem

    Existing semantic knowledge for task-oriented grasping is typically built around closed-world concept sets, limiting generalization to novel concepts beyond those sets.

    Source: §I · p. 1

  • Method

    GraspGPT prompts an LLM for open-ended semantic knowledge about novel concepts and uses descriptions connecting them to related concepts from training.

    Source: §I · pp. 1–2

  • Results

    GraspGPT outperforms existing methods on novel-concept generalization in LA-TaskGrasp evaluation, with effectiveness also validated in real-world applications.

    Source: §VII · p. 8

  • Takeaways & Limitations

    The evaluation scope extends beyond the LA-TaskGrasp dataset to real-robot task-oriented grasping and manipulation.

    Source: §VII · p. 8

    The work focuses on task-oriented grasping and picking; subsequent tool motions are simplified with predefined motion primitives.

    Source: C. Discussion · p. 15

Abstract

from arXiv · show

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://sites.google.com/view/graspgpt/.

I. INTRODUCTION

Open-world robots must relate objects, tasks, and grasps across concepts beyond limited training examples, but existing semantic approaches rely on predefined concept sets. GraspGPT uses an LLM’s open-ended semantic knowledge to generalize to novel concepts, with evaluation on LA-TaskGrasp and a real-robot deployment.

I. INTRODUCTION

  • Existing semantic-knowledge methods rely on predefined concept sets and cannot handle novel concepts outside their knowledge bases, limiting open-world use.Household robots must cope with open-ended object classes and tasks.Source: §I · p. 1
  • GraspGPT prompts an LLM for descriptions of novel object-class or task concepts that connect them to related concepts seen during training.These connections support generalizing learned task-oriented grasping skills to novel concepts.Source: §I · pp. 1–2, Figure 1 · p. 1
  • Evaluation on LA-TaskGrasp reports better performance than existing TOG methods across held-out settings, with effectiveness also tested on a Kinova Gen3 arm.Source: §I · p. 2
  • The authors contribute an LLM pipeline for automatically generating concept descriptions and the language-augmented LA-TaskGrasp dataset.Source: §I · p. 2

A. Task-Oriented Grasping

Earlier TOG methods use semantic representations but remain limited by predefined concepts, while GraspGPT uses LLM knowledge to address generalization to novel concepts. The formulation supplies object and task descriptions and scores candidate grasps given point-cloud and language inputs.

A. Task-Oriented Grasping

  • GraspGPT leverages open-ended LLM semantics to generalize learned task-oriented grasping skills to novel concepts.Earlier data-driven methods learn class-task-grasp relations without external knowledge, while graph-based methods cannot directly handle concepts outside their graph.Source: A. Task-Oriented Grasping · p. 2

B. LLMs in Robotics

  • Prior LLM robotics work focuses mainly on high-level planning, whereas GraspGPT grounds LLM semantics directly in grasping actions.The passage identifies potential application to other low-level policies, including manipulation and navigation.Source: B. LLMs in Robotics · p. 2

III. PROBLEM FORMULATION

  • At inference, object classes or tasks may fall outside the training sets; GraspGPT prompts an LLM for descriptions of both concepts.The target is the posterior over task-oriented grasp poses given the object point cloud, instruction, and generated descriptions; an off-the-shelf task-agnostic sampler supplies grasp candidates.Source: §III · p. 2

IV. GRASPGPT

GraspGPT combines LLM-generated concept descriptions with point-cloud and language features to evaluate task-oriented grasps. Its LA-TaskGrasp data-generation process also creates descriptions and varied natural-language instructions.

A. Overview

  • The framework uses LLM-generated descriptions of novel concepts alongside sensor inputs to evaluate candidate-grasp compatibility.The overview separates data generation, multimodal feature representation, and task-oriented grasp evaluation.Source: A. Overview · p. 3

B. Data Generation with an LLM

  • LA-TaskGrasp uses LLM-generated descriptions for each object class and task, built from property, similarity, affordance, and relevance prompts.Multiple answers are combined into paragraphs; the authors report that a subset suffices because of redundancy, and describe each paragraph as approximately 4–6 sentences.Source: B. Data Generation with an LLM · p. 3
  • Training instructions combine object and task labels with templates, which an LLM augments to increase vocabulary and grammatical diversity.Templates are sampled with object classes and tasks during each training loop to generate instructions without human effort.Source: B. Data Generation with an LLM · p. 3

C. Multi-Modal Feature Representation

  • The object-and-grasp encoder embeds a joint point cloud formed by concatenating gripper control points with the object cloud; its output is later fused with language embeddings.The joint cloud includes a binary feature indicating whether each point belongs to the object or gripper.Source: C. Multi-Modal Feature Representation · p. 4
  • A pretrained, frozen BERT encodes task descriptions, object descriptions, and instructions as word embeddings.The language encoder is chosen to handle varied linguistic elements, including object classes, taxonomies, actions, and relations.Source: C. Multi-Modal Feature Representation · p. 4

D. Task-Oriented Grasp Evaluation

The task-oriented grasp evaluator uses a customized Transformer decoder to incorporate task and object-description context into instruction representations. It fuses the resulting language representation with shape features to score candidate grasps and trains against success labels.

D. Task-Oriented Grasp Evaluation

  • The task-oriented grasp evaluator is a customized Transformer decoder that translates novel concepts through their descriptions to connect them with related concepts seen during training.Its two decoder layers incorporate object-class knowledge and task knowledge into instruction word embeddings.Source: D. Task-Oriented Grasp Evaluation · p. 4
  • Cross-attention uses description features to contextualize instruction embeddings, with the evaluator producing a success score for each candidate grasp.The resulting language representation is mean-pooled, concatenated with the shape embedding, and passed to an MLP with sigmoid activation.Source: D. Task-Oriented Grasp Evaluation · pp. 4–5
  • Training minimizes binary cross-entropy between predicted scores and labels that mark successful grasp poses as one and unsuccessful poses as zero.The MLP has three fully connected layers with batch normalization, ReLU activation, and dropout.Source: D. Task-Oriented Grasp Evaluation · p. 5

A. Perception Experiments

The perception experiments compare GraspGPT with three baselines on LA-TaskGrasp, using held-out object-class and task settings. Real-robot experiments test the grasping pipeline and tool-manipulation skills.

A. Perception Experiments

  • LA-TaskGrasp evaluation compares GraspGPT with Random, SGN, and GCNGrasp on held-out object-class and task settings.The dataset extends TaskGrasp, which contains 250K task-oriented grasp annotations across 56 tasks, 75 object classes, and 191 instances; the evaluation reports class, task, and instance mAP.Source: A. Perception Experiments · p. 5

B. Real-Robot Experiments

  • The real-robot platform uses a 7 DoF Kinova Gen3 arm, parallel-jaw gripper, and eye-in-hand Intel RealSense D435 camera.The setup segments single-view point clouds with SAM and generates 50 grasp candidates with Contact-GraspNet; the single-view setting is chosen as more practical for real-world applications.Source: B. Real-Robot Experiments · p. 5
  • The physical pipeline separates perception, planning, and action, and combines GraspGPT with pouring, handover, and scooping motion primitives.A trial succeeds when the robot grasps the test object according to the language instruction and lifts it stably.Source: B. Real-Robot Experiments · p. 5

C. Implementation Details

GraspGPT is evaluated against GCNGrasp in open- and closed-world perception settings, with quantitative results reported in Table II. The implementation uses GPT-3 and BERT-Base, and Fig. 3 visualizes confidence-ranked grasp predictions.

C. Implementation Details

  • Training uses Adam with 0.0001 weight decay, an initial learning rate of 0.0001, 50 epochs, batch size 32, and 4096-point inputs.Experiments run on a desktop PC with one Nvidia RTX 3090 GPU.Source: C. Implementation Details · p. 5
  • GraspGPT uses the OpenAI GPT-3 text-davinci-003 model and Google pre-trained BERT-Base as its language encoder.The framework can incorporate other current LLMs or an ensemble, which the authors leave for future work.Source: C. Implementation Details · p. 5
  • Fig. 3 shows top-five grasp predictions for both held-out settings and self-collected objects, with green indicating higher confidence.The self-collected test objects have no ground-truth annotations.Source: Figure 3 · p. 6

A. Results of Perception Experiments

  • GraspGPT outperforms GCNGrasp by 21.84%, 29.69%, and 39.17% on held-out tasks, and by 6.78%, 5.43%, and 5.26% on held-out classes.The three values for each setting correspond to the reported metrics; GCNGrasp falls behind Random by 1.58%, 11.03%, and 19.20% on held-out tasks.Source: Table II · p. 6, A. Results of Perception Experiments · p. 7
  • GraspGPT achieves comparable performance with GCNGrasp in closed-world generalization, despite not assuming access to all concepts and their relations.It is comparable on held-out-task instance and class mAP but trails by 3.77% on task mAP; it outperforms GCNGrasp on two metrics for held-out classes.Source: Table II · p. 6, A. Results of Perception Experiments · p. 7

B. Results of Real-Robot Experiments

Real-robot trials assess task-oriented grasping and manipulation, while ablations examine semantic descriptions and encoder size. The conclusion reports zero-shot generalization to novel concepts and real-world validation.

B. Results of Real-Robot Experiments

  • 86.00% and 91.00% perception-stage success rates fell to 71.00% and 77.00% at the action stage across the two held-out settings.The reported causes were marginal grasp candidates, incorrect GraspGPT evaluation, and motion-planning failure.Source: B. Results of Real-Robot Experiments · p. 7
  • 75.00%, 85.00%, and 90.00% success rates were achieved in three task-oriented grasping tasks, while manipulation success decreased especially for pouring and scooping.GraspGPT generated tool grasp poses; rule-based heuristics selected operating direction and target effect points, but the system could not adaptively model the relative pose between tool and target.Source: B. Results of Real-Robot Experiments · p. 7

C. Ablation Study

  • For held-out tasks, the full model outperformed all three semantic-knowledge ablations; task knowledge mattered more for novel tasks, while class knowledge mattered more for novel classes.Object-class descriptions alone slightly outperformed the full model in the held-out-class setting; the authors note that extra task knowledge may sometimes be conflicting and identify knowledge filtering and selection as future work.Source: C. Ablation Study · p. 7
  • BERT-Base outperformed the two smaller encoders on held-out-task mAP, but the gaps were insignificant.The authors argue the three encoders had similar knowledge-understanding capability because they were pretrained on a large text corpus.Source: C. Ablation Study · pp. 7–8, Table V · p. 8

VII. CONCLUSION

  • The conclusion reports that LA-TaskGrasp evaluation showed superiority over existing methods on novel-concept generalization, with effectiveness also validated in real-world grasping and manipulation.GraspGPT uses open-end LLM semantic knowledge rather than a predefined concept set or knowledge base.Source: §VII · p. 8

APPENDIX I

The LA-TaskGrasp appendix describes language prompts and examples for object classes and tasks, alongside dataset examples and instruction templates.

A. Language Augmented TaskGrasp (LA-TaskGrasp) Dataset

  • LA-TaskGrasp includes 18 examples, each with 6 DoF task-oriented grasp poses, a language instruction, and object-class and task-description paragraphs.The appendix also lists 53 language-instruction templates: 11 derived from prior work and 42 generated through LLM data augmentation.Source: A. Language Augmented TaskGrasp… · p. 9

1) Object Class Description Prompts: •

3) Task Description Prompts: •

4) Task Description Examples:

This appendix material identifies task-description examples, language-instruction templates, dataset examples, additional experimental setup, and training hyperparameters.

4) Task Description Examples:

  • The task-description examples are Sweep, Screw, and Slice.They are presented as examples within the LA-TaskGrasp dataset appendix.Source: Table VII · p. 10

5) Language Instruction Templates:

  • The cited passage is the heading for Table VIII, titled “Language Instruction Templates”; it does not state the number of templates or their origins.Source: Table VIII · p. 11

6) Dataset Examples:

  • Each illustrated dataset example includes 6 DoF grasp poses, a language instruction, object-class and task-description paragraphs, and compatibility scores colored green when higher.The figure caption says only part of the description paragraphs are shown.Source: Figure 5 · p. 12

B. Additional Experimental Setup

  • The appendix identifies a section covering additional setup for perception and real-robot experiments.The supplied passage does not specify the setup details.Source: B. Additional Experimental Setup · p. 13

2) Real-Robot Experiment:

The appendix depicts the real-robot setup and test objects, then discusses limitations involving language knowledge, tool manipulation, grasp sampling, and affordance learning.

2) Real-Robot Experiment:

  • The real-robot setup uses a Kinova Gen3 arm with a Robotiq parallel-jaw gripper and an Intel RealSense D435 camera with eye-in-hand calibration.The appendix also depicts laboratory- and YCB-dataset test objects and a pipeline with intermediate results.Source: Figure 6 · p. 14, Figure 7 · p. 14, Figure 8 · p. 14

C. Discussion

  • Unprocessed LLM descriptions can contain imprecise or false commonsense, including prompt mismatches and descriptions that do not match an instance’s properties.The proposed remedies are checking whether text matches prompt intent and using visual inputs with a multimodal model for instance-specific descriptions.Source: C. Discussion · p. 15
  • The work focuses on task-oriented grasping or picking; subsequent tool motion is simplified with predefined primitives, and rule-based effect points do not model tool–target relative pose.The authors aim to extend the framework to task-oriented pick and place using grasping and placement knowledge from an LLM.Source: C. Discussion · p. 15
  • The two-stage sampler evaluates geometry-only, uniformly sampled grasps and assumes their stability, making inference inefficient and leaving marginal poses vulnerable to perturbations.Future work aims to integrate sampling and task-oriented evaluation in an end-to-end architecture that considers stability and task compatibility together.Source: C. Discussion · p. 15
  • Future work proposes joint affordance learning, which could help identify task-relevant grasp regions and use task-oriented grasp supervision as weak supervision for affordance recognition.The authors also identify possible applications in task-driven object retrieval or selection and semantic scene understanding.Source: C. Discussion · p. 15

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