GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning

• URL: http://arxiv.org/abs/2509.11594v2
• Date: Tue, 16 Sep 2025 06:16:33 GMT
• Title: GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
• Abstract summary: GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGBD snapshot.<n>The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refine the model to match true grasp outcomes.
• Score: 10.736029638634504
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refines the model to match true grasp outcomes. A PointNet++ style point cloud encoder with an MLP scores dense grids of candidate poses, enabling rapid online selection without full task-and-motion optimization. In simulation and on a real mobile manipulator, GBPP outperforms proximity and geometry only baselines, choosing safer and more reachable stances and degrading gracefully when wrong. The results offer a practical recipe for data efficient, geometry aware base placement: use inexpensive heuristics for coverage, then calibrate with targeted simulation.

Related papers

• GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments [63.222920701596564]
  Training embodied agents in the real world requires skilled operators and expensive hardware.<n> Simulation environments offer a compelling alternative by enabling large-scale, cost-effective data augmentation.<n>Fastly constructing high-fidelity simulation scenes with a minimal sim-to-real gap has become a critical objective in robot learning.
  arXiv  Detail & Related papers  (2026-06-16T05:00:42Z)
• Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation [0.6533458718563319]
  This paper presents a framework designed to enhance the grasping capabilities of quadrupeds equipped with arms.<n>We developed a pipeline within the Genesis simulation environment to generate a synthetic dataset of grasp attempts on common objects.<n>This dataset was used to train a custom CNN with a U-Net-like architecture that processes multi-modal input from an onboard RGB and depth cameras.<n>We validated the complete framework on a four-legged robot.
  arXiv  Detail & Related papers  (2025-08-24T17:47:56Z)
• LifelongPR: Lifelong point cloud place recognition based on sample replay and prompt learning [15.464706470200337]
  We propose LifelongPR, a novel continual learning framework for PCPR.<n>LifelongPR effectively extracts and fuses knowledge from sequential point cloud data.<n>Our method achieves 6.50% improvement in mIR@1, 7.96% improvement in mR@1, and an 8.95% reduction in F.
  arXiv  Detail & Related papers  (2025-07-14T08:13:33Z)
• FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models [49.397861654088636]
  We propose a two-step procedure to approximate SVD/QR-based gradient projections into lower-dimensional spaces.<n>We show that our strategy achieves faster runtime and reduced memory usage by up to $25%$ across different model sizes.
  arXiv  Detail & Related papers  (2025-05-23T14:37:00Z)
• ReGUIDE: Data Efficient GUI Grounding via Spatial Reasoning and Search [53.40810298627443]
  ReGUIDE is a framework for web grounding that enables MLLMs to learn data efficiently through self-generated reasoning and spatial-aware criticism.<n>Our experiments demonstrate that ReGUIDE significantly advances web grounding performance across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-05-21T08:36:18Z)
• Micro-Structures Graph-Based Point Cloud Registration for Balancing Efficiency and Accuracy [5.70403503863614]
  We propose a novel micro-structures graph-based global point cloud registration method. Our proposed method performs well on the 3DMatch and ETH datasets.
  arXiv  Detail & Related papers  (2024-10-29T08:36:23Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN [15.271970287767164]
  We develop a graph neural network (GNN) to compute, within a time budget of 1 to 2 milliseconds required by practical systems. We show that it achieves near optimal spectral efficiency in a range of scenarios with different number of APs and UEs.
  arXiv  Detail & Related papers  (2024-06-06T19:29:33Z)
• Deep-PE: A Learning-Based Pose Evaluator for Point Cloud Registration [35.77554768853786]
  Deep-PE is a lightweight, learning-based pose evaluator designed to enhance the accuracy of pose selection. Our network incorporates a Pose-Aware Attention (PAA) module to simulate and learn the alignment status of point clouds. Deep-PE significantly outperforms state-of-the-art methods by at least 8% and 11% in registration recall.
  arXiv  Detail & Related papers  (2024-05-25T06:32:32Z)
• SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving [27.776472262857045]
  This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles. We propose a compact and efficient global feature fusion module that performs directed message passing in a symmetric manner. As a strong baseline, SIMPL exhibits highly competitive performance on Argoverse 1 & 2 motion forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-02-04T15:07:49Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• AutoSimulate: (Quickly) Learning Synthetic Data Generation [70.82315853981838]
  We propose an efficient alternative for optimal synthetic data generation based on a novel differentiable approximation of the objective. We demonstrate that the proposed method finds the optimal data distribution faster (up to $50times$), with significantly reduced training data generation (up to $30times$) and better accuracy ($+8.7%$) on real-world test datasets than previous methods.
  arXiv  Detail & Related papers  (2020-08-16T11:36:11Z)
• RPM-Net: Robust Point Matching using Learned Features [79.52112840465558]
  RPM-Net is a less sensitive and more robust deep learning-based approach for rigid point cloud registration. Unlike some existing methods, our RPM-Net handles missing correspondences and point clouds with partial visibility.
  arXiv  Detail & Related papers  (2020-03-30T13:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.