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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors
Haifeng Huang, Xinyi Chen, Yilun Chen, Hao Li, Xiaoshen Han, Zehan Wang, Tai Wang, Jiangmiao Pang, Zhou Zhao
TL;DR
Robotic policies often generalize poorly to novel settings, while existing intermediate representations trade spatial precision against scalability. RoboGround uses grounding masks and a diverse simulated-data pipeline to guide manipulation policies. Experiments show substantially enhanced policy generalization and value from the proposed dataset.
Problem
Robot policies remain difficult to generalize to novel settings, while existing intermediate representations are either spatially coarse or resource-intensive.
Method
RoboGround uses grounding masks generated by a grounded vision-language model and integrates them into a policy network, alongside automated diverse-data generation.
Results
Experiments show that grounding masks substantially enhance robot-policy generalization and demonstrate the value of the proposed dataset.
Takeaways & Limitations
Grounding masks provide an intermediate representation that combines spatial guidance with broad generalization potential for robotic manipulation.
Takeaways & Limitations
The reported evaluation notes that pose-prediction networks such as AnyGrasp could potentially help address a challenge, despite the method's high contact rate.
Abstract
from arXiv · showhide
Recent advancements in robotic manipulation have highlighted the potential of intermediate representations for improving policy generalization. In this work, we explore grounding masks as an effective intermediate representation, balancing two key advantages: (1) effective spatial guidance that specifies target objects and placement areas while also conveying information about object shape and size, and (2) broad generalization potential driven by large-scale vision-language models pretrained on diverse grounding datasets. We introduce RoboGround, a grounding-aware robotic manipulation system that leverages grounding masks as an intermediate representation to guide policy networks in object manipulation tasks. To further explore and enhance generalization, we propose an automated pipeline for generating large-scale, simulated data with a diverse set of objects and instructions. Extensive experiments show the value of our dataset and the effectiveness of grounding masks as intermediate guidance, significantly enhancing the generalization abilities of robot policies.
1. Introduction
RoboGround addresses limited policy generalization by using grounding masks as spatially informative intermediate representations and by generating diverse, complex simulated manipulation data. Experiments report that grounding masks enhance policy generalization and that the dataset is valuable.
- Existing manipulation policies struggle to generalize to novel settings without extensive datasets and additional fine-tuning.
- Language instructions are accessible but spatially coarse, while goal images and point flows provide precision at greater data and computational cost.
- Grounding masks specify target objects and placement areas while conveying object shape and size, combining spatial guidance with broad generalization potential.
- RoboGround generates 24K demonstrations and 112K instructions across 3,526 objects and 176 categories, increasing scene complexity with distractors.
- Grounding masks as intermediate guidance substantially enhance robot-policy generalization, while the proposed dataset provides measurable value.
2. Related Work
Prior robot-policy research explores intermediate guidance including language, trajectories, point flows, goal images, sketches, and object-centric representations. These approaches differ in spatial detail and practical evaluation requirements.
- Intermediate guidance strategies include language instructions, 2D trajectories, point flows, goal images, goal sketches, and object-centric representations.
3. Data Generation
RoboGround builds an automated simulation-data pipeline to increase object variety, instruction diversity, and scene complexity. It generates appearance, spatial, and commonsense instructions using attribute extraction, similarity-based selection, rules, and GPT-4.
- Existing language-conditioned simulation datasets often lack object, environment, instruction, or complex-scene diversity needed for robust policy training and generalization evaluation.
- Appearance-Based Instructions: Object attributes are extracted as keywords and descriptive phrases, supporting object filtering and appearance-similarity computation.
- The pipeline produces 24K demonstrations and 112K instructions covering 176 object categories and 3,526 unique objects.
- Appearance-Based Instructions: Distractors are selected from contextually relevant objects to require precise distinctions between targets and surrounding objects.
- Spatial and Commonsense Instructions: Spatial instructions use rule-based generation, whereas commonsense instructions use GPT-4-generated daily scenarios grounded in multiple views.
4. Method
RoboGround uses a grounded vision-language model to convert instructions and images into target and placement masks, then injects those masks into a language-conditioned policy. The masks guide both visual input and patch-level attention for manipulation.
- Grounded Vision-Language Model: The grounded vision-language model processes image observations and instructions to generate binary masks for target objects and placement areas.
- Grounded Vision-Language Model: Separate segmentation tokens produce target-object and placement-area masks that are passed to the grounded policy network.
- Grounded Policy Network: The policy concatenates image channels with grounding masks before visual encoding, adding mask information to the policy input.
- Grounded Policy Network: The policy combines visual, language, robot-state, and action-token features across a history of observations before predicting the next action.
- Grounded Policy Network: Mask-guided object and placement queries attend to corresponding patch features, producing grounded visual features for the policy.
- Grounded Vision-Language Model: The grounded VLM is fine-tuned on instruction-following data generated from the simulation pipeline to improve target and placement-area identification.
5. Experiments
RoboGround is evaluated on simulated manipulation tasks, unseen objects and classes, and ablations of its data, mask integration, representations, and grounded VLM. Across these experiments, grounding masks and diverse training data improve generalization, while grasping remains a relative challenge.
- Main Results: The evaluation compares methods trained on RoboCasa’s 66K demonstrations and RoboGround’s 24K demonstrations with 112K diverse instructions.Pick-and-place evaluation uses 400 test samples per instruction type and fundamental skill.
- Main Results: RoboGround consistently outperforms the baseline models across tasks, while language-free ACT performs poorly and global language features remain limited on challenging instructions.The baselines include ACT, BC-Transformer, and GR-1.
- Main Results: Grounding-mask guidance substantially improves challenging-task performance, although contact rates remain higher than success rates, indicating grasping is a remaining weakness.The authors associate the grasping difficulty with the diverse object set and suggest pose-prediction networks as a possible remedy.
- Zero-shot Evaluation: Mask information improves zero-shot generalization to both unseen instances and unseen classes while preserving strong performance on seen tasks.In more challenging scenarios, mask guidance achieves approximately 100% relative improvement over non-mask baselines.
- Ablation Study: Models trained only on original simple data fit the Easy set but struggle on challenging sets, whereas new data and mask guidance improve generalization.Combining original and new data further enhances performance, supporting the value of scaling the dataset.
- Ablation Study: Both channel concatenation and the grounded perceiver incorporate mask information effectively, with the grounded perceiver exploiting mask features more comprehensively.Among grounding representations, masks produce the best performance compared with lower-dimensional point or bounding-box representations.
Supplementary Material
The supplementary materials direct readers to implementation details, data details, grounded perceiver details, and discussions of limitations and future work.
- Appendix A provides implementation details, while Appendix B presents data details.
- Appendix C explains the grounded perceiver in detail.
- Appendix D discusses limitations and future work.
A. Implementation Details
The implementation fine-tunes a grounded vision-language model with simulated grounded conversations and trains a grounded policy network from scratch on simulation data; instruction embeddings are used to assess diversity.
- Grounded VLM: GLaMM is fine-tuned from a publicly available checkpoint pretrained on the Grounding-anything Dataset, which contains 7.5M unique concepts spanning 810M regions.
- Grounded VLM: The grounded VLM training adds 112K simulated-data QA pairs to an existing 277K grounded conversation dataset.
- Grounded Policy Network: The grounded policy network re-implements GR-1 without its image prediction head and is trained from scratch using simulation data.
B. Data Details
The data pipeline expands simulated manipulation beyond the original task setup through diverse instructions, kitchen-object filtering, attribute-based descriptions, and more complex task scenes.
- Instruction Diversity: Generated instructions show greater diversity than original data, with lower mean similarity and higher variance in BERT embedding comparisons.
- Prompts for Data Generation: GPT-4 filters kitchen-related objects using valid object types and attributes generated during earlier pipeline stages.
- Prompts for Data Generation: GPT-4 generates object key attributes as distinct descriptive words and also produces detailed descriptive phrases.
- Simulation Tasks: The simulation adopts 22 RoboCasa atomic tasks grouped into Pick and Place, Open/Close, Press, and Turn/Twist.
- Simulation Tasks: Pick-and-place data adds complex scenes and instructions that vary appearance, spatial relationships, and other task conditions.
C. Details of Grounded Perceiver
The grounded perceiver combines global, object, and placement queries with image patch features, then modifies attention using target masks to focus queries on relevant regions.
- Query Construction: The grounded perceiver uses 9 global, 9 target-object, and 9 target-placement query tokens, concatenated into Q ∈ R27×d.
- Attention Inputs: The 14×14 image patch features are concatenated with query features to form keys and values for attention computation.
- Task and Data Split: The data split covers four task types, with more complex scenes and instructions created for pick-and-place tasks.
- Mask-Guided Attention: The attention layer incorporates target-object and placement masks by replacing masked-region attention values with the current matrix’s highest attention value.
- Mask-Guided Attention: Mask-guided attention makes object and placement query tokens focus more effectively on their corresponding masked areas.
D. Limitation and Future Work
The paper identifies limitations in grasp reliability, placement-area diversity, and the separation between mask prediction and action prediction, while outlining corresponding directions for future work.
- Limitations: Object picking remains unreliable because the model often contacts target objects without successfully grasping them.The authors attribute this partly to the diversity of thousands of objects and note that grounding masks provide limited grasping-precision support.
- Limitations: Future data generation should expand target placement-area diversity, not only target-object diversity.This would require additional human demonstrations for new placement scenes and automated augmentation methods such as MimicGen.
- Limitations: The current architecture separates grounded VLM mask prediction from policy-network action prediction.The authors identify end-to-end and slow-fast architectures as promising but challenging directions for more robust and longer-horizon policies.
- Future Work: The findings are intended to motivate further work on intermediate representations and more diverse robot-manipulation scenes and instructions.This future-work direction connects representation design with dataset-generation practices.