[Paper Review] Orientation Attentive Robot Grasp Synthesis.

Nikolaos Gkanatsios, Georgia Chalvatzaki+2 more|arXiv (Cornell University)|Jun 9, 2020

Robot Manipulation and Learning29 references4 citations

TL;DR

This paper proposes ORANGE, a novel grasp synthesis framework that addresses orientation ambiguity in robotic grasping by using a binarized angular representation to co-identify multiple plausible grasping orientations. By jointly solving discrete bin classification and continuous regression, ORANGE achieves state-of-the-art 94.71% success rate on the Jacquard dataset, outperforming existing unimodal and multimodal methods.

ABSTRACT

Physical neighborhoods of grasping points in common objects may offer a wide variety of plausible grasping configurations. For a fixed center of a simple spherical object, for example, there is an infinite number of valid grasping orientations. Such structures create ambiguous and discontinuous grasp maps that confuse neural regressors. We perform a thorough investigation of the challenging Jacquard dataset to show that the existing pixel-wise learning approaches are prone to box overlaps of drastically different orientations. We then introduce a novel augmented map representation that partitions the angle space into bins to allow for the co-occurrence of such orientations and observe larger accuracy margins on the ground truth grasp map reconstructions. On top of that, we build the ORientation AtteNtive Grasp synthEsis (ORANGE) framework that jointly solves a bin classification problem and a real-value regression. The grasp synthesis is attentively supervised by combining discrete and continuous estimations into a single map. We provide experimental evidence by appending ORANGE to two existing unimodal architectures and boost their performance to state-of-the-art levels on Jacquard, specifically 94.71\%, over all related works, even multimodal. Code is available at \url{this https URL}.

Motivation & Objective

• To address the challenge of orientation ambiguity in robotic grasp synthesis, where multiple valid grasping orientations exist for a single point.
• To overcome the limitations of pixel-wise learning methods that suffer from orientation box overlaps and discontinuous grasp maps.
• To design a unified representation that enables co-occurrence of diverse grasping orientations in a single map.
• To develop a joint learning framework that combines discrete classification and continuous regression for improved grasp prediction.
• To achieve state-of-the-art performance on the Jacquard benchmark, surpassing existing unimodal and multimodal approaches.

Proposed method

• Introduce an augmented angular map representation that partitions the full 360-degree orientation space into discrete bins to resolve orientation ambiguity.
• Construct a hybrid supervision signal by combining discrete bin classification and continuous regression of grasp parameters into a single attention-based map.
• Design the ORANGE framework to jointly optimize for bin assignment and precise grasp parameter regression using a shared feature encoder.
• Apply the framework as a plug-in to existing unimodal architectures, enabling performance gains without architectural overhaul.
• Use attention mechanisms to dynamically weigh contributions from discrete and continuous predictions, improving localization accuracy.
• Train the model end-to-end on the Jacquard dataset with data augmentation to enhance generalization across object shapes and orientations.

Experimental results

Research questions

• RQ1 How can orientation ambiguity in robotic grasp synthesis be effectively modeled to avoid discontinuities in grasp maps?
• RQ2 Can a unified representation that supports co-occurrence of multiple orientations improve grasp prediction accuracy?
• RQ3 Does combining discrete bin classification with continuous regression yield better performance than either approach alone?
• RQ4 To what extent can ORANGE boost the performance of existing unimodal grasp networks on benchmark datasets?
• RQ5 Is the proposed method capable of achieving state-of-the-art results on the Jacquard dataset compared to multimodal approaches?

Key findings

• The proposed angular binning representation successfully resolves orientation ambiguity by allowing multiple plausible orientations to coexist in the same prediction map.
• The joint learning framework of ORANGE achieves a 94.71% success rate on the Jacquard dataset, setting a new state-of-the-art performance level.
• Appending ORANGE to existing unimodal architectures leads to significant performance gains, surpassing both unimodal and multimodal baselines.
• The method demonstrates robustness to orientation discontinuities that commonly mislead standard pixel-wise regression models.
• The attention-based fusion of discrete and continuous predictions enhances localization precision and generalization across diverse object shapes.