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Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, Yuke Zhu
TL;DR
The paper addresses 6-DoF grasp detection in clutter from incomplete and noisy perception, where geometry and grasp affordance must be inferred jointly. It introduces GIGA, which uses shared implicit representations and multi-task learning for grasp prediction and 3D reconstruction. In clutter-removal experiments, GIGA achieves higher reported grasp success rates than VGN, while the authors identify limitations in grasp-distribution modeling and explicit collision-free planning.
Problem
6-DoF grasp detection in clutter requires predicting grasp poses from partial point clouds, while data-driven grasping can have limited generalization within training domains.
Method
GIGA jointly learns grasp affordance and 3D reconstruction with structured implicit neural representations, using shared feature grids and simulated self-supervised grasp trials.
Results
87.9% and 69.2% grasp success rates on packed and pile scenes exceeded VGN’s 74.5% and 60.7%, respectively.
Takeaways & Limitations
Jointly exploiting affordance and geometry improves 6-DoF grasp detection, especially under large occlusion, while grasp supervision also improves reconstruction in graspable regions.
Takeaways & Limitations
The method assumes one ground-truth grasp pose per grasp center and does not explicitly reason about collision-free paths to grasps.
Abstract
from arXiv · showhide
Grasp detection in clutter requires the robot to reason about the 3D scene from incomplete and noisy perception. In this work, we draw insight that 3D reconstruction and grasp learning are two intimately connected tasks, both of which require a fine-grained understanding of local geometry details. We thus propose to utilize the synergies between grasp affordance and 3D reconstruction through multi-task learning of a shared representation. Our model takes advantage of deep implicit functions, a continuous and memory-efficient representation, to enable differentiable training of both tasks. We train the model on self-supervised grasp trials data in simulation. Evaluation is conducted on a clutter removal task, where the robot clears cluttered objects by grasping them one at a time. The experimental results in simulation and on the real robot have demonstrated that the use of implicit neural representations and joint learning of grasp affordance and 3D reconstruction have led to state-of-the-art grasping results. Our method outperforms baselines by over 10% in terms of grasp success rate. Additional results and videos can be found at https://sites.google.com/view/rpl-giga2021
I. INTRODUCTION
The paper frames 6-DoF grasp detection in clutter as reasoning over geometry and affordance from partial observations. GIGA jointly learns both through shared implicit representations, achieving improved clutter-removal performance in simulation and on real hardware.
- Problem: 6-DoF grasp detection from partial point clouds requires predicting candidate grasp poses for decluttering cluttered objects.The task involves estimating 3D position and orientation from an onboard depth camera.
- Motivation: Geometry-centric methods often require complete object models, while end-to-end grasp learning can have limited generalization within training domains.The paper motivates combining geometry reasoning with data-driven grasp prediction.
- Approach: GIGA uses a shared scene representation for grasp prediction and 3D reconstruction, representing grasp affordance as success likelihood and grasp parameters over the scene.The shared representation is intended to encode both 3D geometry and grasp affordance information.
- Approach: The model uses structured implicit neural representations with feature grids and jointly estimates grasp quality, orientation, gripper width, and binary occupancy.Features are queried at 3D coordinates and decoded for both affordance and geometry.
- Results: 87.9% and 69.2% grasp success rates on packed and pile scenes exceeded VGN’s 74.5% and 60.7%, respectively.Experiments covered clutter removal in simulation and on physical hardware.
II. RELATED WORK
Prior grasping research includes analytical geometry-based methods, data-driven prediction, and geometry-aware learning. Deep implicit functions provide continuous, differentiable, high-resolution representations that support the paper’s geometry-reasoning approach.
- Geometry-centric grasping: Analytical grasping methods optimize constraints from complete object geometry and physics, but such models are often unavailable in unstructured scenes.3D reconstruction has been used as a bridge from raw perception to model-driven grasping.
- Geometry-aware grasping: Geometry-aware methods combine grasp prediction with 3D geometry reasoning, including occupancy prediction from partial observations.This line of work connects reconstructed geometry with grasp-quality estimation.
- Implicit representations: Deep implicit functions represent surfaces continuously and smoothly at high resolution instead of using explicit voxels, point clouds, or meshes.Their continuous formulation supports memory-efficient and differentiable representations of high-resolution 3D data.
B. Occupancy Networks
Occupancy Networks reconstruct 3D objects by fitting a continuous occupancy field conditioned on visual observations. The field maps queried 3D locations to occupancy probabilities.
- Occupancy field: Occupancy Networks fit a continuous occupancy field over an object’s bounding 3D volume from an observed 3D object.They are an early implicit-neural-representation approach to 3D reconstruction.
- Occupancy field: The occupancy function maps a 3D location to an occupancy probability between 0 and 1.This provides the reconstruction output queried at spatial coordinates.
- Conditioning: To generalize across shapes, the occupancy function is conditioned on a context vector computed from visual observations.The resulting neural function takes a spatial coordinate and context as input.
- Structured representations: ConvONets extend Occupancy Networks with convolutional encoders and structured feature grids for scene-level reconstruction.Implicit functions are conditioned on local features retrieved from encoded observations.
IV. PROBLEM FORMULATION
The paper formulates 6-DoF grasp detection for unknown rigid objects in clutter from a single-view depth image, using TSDF observations and jointly learned grasp and occupancy functions.
- IV. PROBLEM FORMULATION: The task is to detect 6-DoF grasp configurations that let a robot grasp and remove objects from clutter.A grasp comprises position, orientation, and gripper opening width.
- A. Assumptions: The robot receives a single-view depth image from an onboard camera and operates with a parallel-jaw gripper in a tabletop workspace.The workspace contains multiple rigid objects and uses a fixed side-view depth camera.
- B. Notations: The depth image is fused into a TSDF voxel grid, whose cells store truncated signed distances to the nearest surface.The TSDF volume is passed to the model as the input observation.
- B. Notations: A 6-DoF grasp is represented by center position t ∈ R^3, gripper orientation r ∈ SO(3), and opening width w ∈ R.These variables specify the parallel-jaw gripper configuration.
- B. Notations: Grasp quality q ∈ [0, 1] estimates the probability that executing a grasp will succeed.The model learns this quantity from binary success labels generated by simulated grasp trials.
- B. Notations: Occupancy b ∈ {0, 1} indicates whether an arbitrary 3D point is occupied by any object in the scene.Occupancy provides the geometric target for simultaneous 3D reconstruction.
- C. Objectives: The model learns an affordance function that maps a grasp center to rotation, gripper width, and grasp quality for selecting executable grasps.Candidate centers can be ranked by predicted grasp quality.
- C. Objectives: A second function maps arbitrary workspace points to occupancy values, from which a 3D mesh can be extracted with Marching Cubes.The two functions support simultaneous grasp affordance learning and 3D reconstruction.
V. METHOD
GIGA uses shared structured feature grids and implicit functions to jointly represent grasp affordance and scene geometry from TSDF observations. Local interpolated features support continuous predictions for grasp parameters and occupancy.
- V. METHOD: GIGA jointly learns grasp affordance and 3D occupancy with shared feature grids and a unified implicit neural representation.The shared representation is designed for 6-DoF grasp detection from partial observations.
- V. METHOD: The structured representation addresses the weakness of flat global features, which lack local spatial information and can produce overly smooth reconstructions.Local geometry is especially relevant for grasp detection.
- V. METHOD: The encoder processes a TSDF voxel field with a 3D CNN and projects voxel features onto canonical planes using average pooling.The resulting planar feature representations provide structured spatial features.
- V. METHOD: Deep implicit functions provide continuous, memory-efficient representations that can encode both geometry and grasp affordance.Their differentiability supports joint multi-task learning.
- V. METHOD: Local features are obtained by bilinear interpolation from the three projected feature planes at a queried 3D point.The plane-feature and projected-point pairs are combined to form the local representation.
- V. METHOD: Affordance implicit functions map grasp centers to orientation, gripper width, and grasp quality continuously.Unlike voxel-based VGN, GIGA does not snap grasp centers to the nearest voxel.
- V. METHOD: The geometry implicit function maps an arbitrary query point to its occupancy probability using that point and its corresponding local feature.Occupancy query points may differ from grasp-center points.
C. Grasp Detection
GIGA predicts grasp parameters and occupancy probabilities from a TSDF grid, a grasp center, and multiple spatial query points, then samples grasp centers across the workspace to select a final pose.
- C. Grasp Detection: GIGA takes a TSDF voxel grid, a grasp center, and multiple occupancy query points as inputs to its implicit functions.It outputs grasp parameters for the center and occupancy probabilities for the query points.
- C. Grasp Detection: The final grasp is selected by querying learned affordance functions at every voxel-cell position used as a grasp center.Queries are performed in parallel to cover graspable regions throughout the workspace.
D. Training
Training combines affordance and geometry objectives while evaluating clutter-removal scenarios and the complementary roles of geometry and affordance learning. The experiments distinguish packed and pile scene conditions.
- D. Training: The affordance loss combines grasp-quality, orientation, and gripper-width terms, while the geometry loss uses binary cross-entropy for occupancy.The total training loss is the direct sum of affordance and geometry losses.
- D. Training: The orientation objective accounts for parallel-jaw symmetry by treating the original and 180°-rotated wrist configurations as valid targets.The loss uses the smaller quaternion distance to either rotation.
- D. Training: Orientation and gripper width are supervised only for successful grasps, where the ground-truth quality label is q = 1.This conditions parameter supervision on successful simulated trials.
- D. Training: The models are trained with Adam using a learning rate of 2 × 10^-4 and batch size 32.The implementation uses PyTorch.
- D. Training: Packed scenes place objects at canonical poses, whereas pile scenes use objects randomly dropped into the workspace.These are the two simulated scene types used for self-supervised grasp data.
- D. Training: Table I reports mean and standard deviation for grasp success rate and declutter rate, including high-resolution results.GSR denotes grasp success rate, DR denotes declutter rate, and HR denotes high resolution.
- D. Training: The study evaluates whether structured implicit representations encode grasp information and whether joint geometry-affordance learning improves grasping and reconstruction.These questions cover both grasp detection and 3D reconstruction effects.
A. Experimental Setup
GIGA is evaluated in simulated clutter-removal scenes using self-supervised grasp and occupancy data, noisy single-view depth inputs, and continuous implicit querying for grasp execution.
- Data generation: Training uses self-supervised grasp trials and occupancy data collected from the same simulated scenes.Grasp trials are generated by sampling centers and orientations near object surfaces; occupancy labels are queried throughout the workspace.
- Simulation environment: The simulated workspace contains pile and packed clutter scenarios with separate training and test object assets.The simulation uses 303 training objects and 40 held-out test objects from the same asset collection used by VGN.
- Camera observations: The model receives a single noisy depth image fused into a 40 × 40 × 40 TSDF volume.Noise is added to rendered depth images to support sim-to-real transfer, and the TSDF is constructed from the resulting observation.
- Grasp execution: GIGA queries grasp parameters at sampled workspace centers, with a higher-resolution 60×60×60 query grid used by GIGA (HR).The standard comparison uses 40 × 40 × 40 uniformly distributed grasp centers, while the implicit representation permits arbitrary resolutions.
- Evaluation protocol: Each clutter-removal round generates a grasp proposal, executes it, and removes the grasped object until termination conditions are met.A scene ends when all objects are cleared, two consecutive failures occur, or no grasp is detected.
B. Baselines
The evaluation compares GIGA with heuristic, two-stage, single-stage, and affordance-only baselines, using grasping, reconstruction, qualitative, and efficiency evidence.
- Baselines: The baselines include SHAF, GPD, VGN, and GIGA-Aff, an affordance-only ablation without reconstruction supervision.Performance is evaluated with grasp success rate and declutter rate averaged over 100 simulation rounds.
- Quantitative comparison: GIGA and GIGA-Aff outperform other baselines in almost all reported scenarios and metrics.The authors attribute GIGA-Aff’s performance relative to VGN partly to the expressiveness of continuous implicit representations.
- Resolution and representation: GIGA (HR) gives the highest performance in all cases by querying the continuous affordance field at higher resolution.Unlike VGN’s voxel-grid snapping, GIGA’s implicit representation supports continuous grasp-affordance fitting and arbitrary-resolution queries.
- Geometry-affordance synergy: Geometry supervision improves grasping particularly in packed scenes, where GIGA exceeds GIGA-Aff by around 5% versus around 2% in pile scenes.The authors connect the packed-scene gain to partially visible objects occluded by taller objects.
- Qualitative results: GIGA predicts collision-free, diverse grasps in clutter and identifies graspable regions on partially occluded objects that baselines often miss.Its affordance maps can be asymmetric over symmetric objects when neighboring clutter makes only part of an object safely graspable.
- Efficiency: GIGA’s planning time is 46ms, while VGN’s multi-view scanning adds an estimated 16–20s before each grasp.Because GIGA uses a fixed single-view camera, the authors report at least an order-of-magnitude faster grasping than VGN.
- Multi-view comparison: With six-view inputs, GIGA achieves 88.8% grasp success rate in packed scenes and 69.6% in pile scenes.These results are reported as on par with GIGA’s single-view performance, despite multi-view acquisition being more time-consuming.
D. 3D Reconstruction
The reconstruction study compares specialized and joint models, showing that grasp supervision concentrates representational capacity near actionable regions while sacrificing some non-graspable geometry.
- Evaluation: The study evaluates volumetric IoU overall and IoU-Grasp near successful grasp regions using meshes extracted by marching cubes.IoU-Grasp measures reconstruction quality in the regions between the fingers of sampled successful grasps.
- Real-world evaluation: Real-world clutter-removal evaluation reports grasp success rate, declutter rate, successful grasps, and total grasp trials.The cited table reports these four measures for physical-robot performance.
- Real-world examples: Real-world examples include partially occluded grasps, localized grasping of a bear doll, and a failure caused by gripper slippage on a small contact surface.The figure illustrates both successful operation under occlusion and a concrete contact-related failure mode.
- Quantitative results: GIGA-Geo achieves the highest overall IoU and the highest IoU in graspable parts because it specializes in 3D reconstruction.GIGA and GIGA-Detach jointly or indirectly support grasp prediction, whose spatial distribution is localized.
- Grasp-supervision effects: GIGA-Detach has the largest IoU-Grasp-to-IoU gap, followed by GIGA, indicating allocation of representational resources toward actionable regions.The qualitative reconstructions show missing geometry is mostly non-graspable, such as parts of a bowl or handle.
E. Real Robot Experiments
In real-hardware clutter-removal experiments, GIGA achieved higher success rates and cleared more objects than VGN, including partially occluded and difficult-to-grasp regions. The authors also identify simulation contact and friction models as a failure source and outline limits for future extensions.
- Experimental setup: 15 rounds evaluated GIGA and VGN across Packed and Pile scenarios, with five randomly selected objects placed on the table in each round.Each trial used a side-view depth image, passed its TSDF to the model, and executed the predicted top grasp.
- Results: GIGA achieved higher success rates and cleared more objects than VGN in the real-world clutter-removal evaluation.The comparison included both Packed and Pile scenarios.
- Results: GIGA better detected grasps for partially occluded objects and graspable regions such as edges and handles than VGN.These examples are reported as qualitative comparisons from the real-robot experiments.
- Failure analysis: A reported grasp failure resulted from insufficient contact surface, which the authors attributed to unrealistic contact and friction models in simulation.This identifies a mismatch between simulated training conditions and the real grasping case.
- Limitations and future work: The method assumes one ground-truth grasp pose per grasp center and does not explicitly reason about collision-free paths to grasps.The authors propose generative modeling for viable grasp distributions and using reconstructed scenes to constrain collision-free prediction.