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Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

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arxiv 2104.01542 v2 pith:ERNQCBLF submitted 2021-04-04 cs.RO cs.AIcs.CV

Zhenyu Jiang , Yifeng Zhu , Maxwell Svetlik , Kuan Fang , Yuke Zhu This is my paper · ORCID
classification cs.ROcs.AIcs.CV
keywords graspaffordanceimplicitlearningreconstructionresultsrobotclutter
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 5 Pith papers

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  1. Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation

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    Continuous multi-view image-space keypoint trajectories plus per-camera equivariant augmentation beat strong 3D and image baselines on MimicGen and real UR5 tasks.

  2. Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras

    cs.CV 2026-07 conditional novelty 5.0 of 10

    HW-NAS with an RGB-D search space and a cheap depth-to-RGB fine-tune layer yield mid-size networks that often sit on the accuracy–FLOPs/params Pareto front and run real-time on a Jetson Nano.

  3. Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance

    cs.RO 2026-03 unverdicted novelty 5.0 of 10

    The viewpoint-agnostic grasp pipeline using VLM and partial observation handling achieves 90% success (9/10 trials) in cluttered tabletop scenarios on a real quadruped robot, outperforming a view-dependent baseline at...

  4. Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation

    cs.RO 2026-01 conditional novelty 5.0 of 10

    A hierarchical RL-MPC framework with a 'contact intention' interface achieves data-efficient, robust non-prehensile manipulation that transfers zero-shot to a real robot.

  5. Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module

    cs.CV 2026-07 conditional novelty 4.0 of 10

    Small decoder design choices (depthwise vs. standard convolutions, upsampling type, and an auxiliary object-segmentation head) yield modest accuracy gains over the authors' prior baseline on binary grasp affordance se...

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