# 从点云学习液压起重机清理原木堆的抓取目标定位

> 原文链接：https://robotics.hellobike.com/papers/2026-10-06-2610-07613
> 频道：papers | 更新日期：2026-10-06 | 标签：arxiv, cs.RO
> 核心摘要：该文用未分割点云学习抓取位置、深度和姿态策略，并在仿真与拖车式液压林业起重机清垛中验证。

# 从点云学习液压起重机清理原木堆的抓取目标定位

::: v-pre
> 原文链接：[Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane](https://arxiv.org/abs/2610.07613)（arXiv 2610.07613 · 发布日期：2026-10-06）

**Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane**

该文用未分割点云学习抓取位置、深度和姿态策略，并在仿真与拖车式液压林业起重机清垛中验证。

## 核心要点

- 密集原木堆每次移除都会改变后续抓取，需要从未分割点云选择抓斗位姿。
- 网络在观测点分类抓取目标，并预测深度与抓斗朝向，统一支持BC、RL和部署。
- 仿真中BC清除100堆中的98堆；BC→RL进一步改善装载稳定性。
- 12次现场试验中，BC与BC→RL存放93.8%和88.9%，高于启发式80.4%。

**验证范围与局限**：验证限于仿真和拖车式液压林业起重机的12次现场试验，BC→RL的仿真稳定性增益未在真机体现。

## 英文摘要 (Abstract)

In mill yards, log loaders clear dense piles by a sequence of bundle grasps: hundreds of logs rest in contact, and each removal changes the pile available to the next grasp. A learned policy chooses where to place and orient the grapple from unsegmented point clouds and runs on a trailer-mounted hydraulic forestry crane. The policy classifies at which observed point to grasp and predicts depth and grapple orientation there. The same network outputs support behavior cloning (BC), reinforcement learning (RL), and deployment. BC learns from successful top-of-pile demonstrations; RL explores for improvements by fine-tuning the cloned policy (BC$\to$RL) or by training from scratch. In simulation, BC clears 98 of 100 piles of 200 logs, while BC$\to$RL improves load stability. Twelve field trials compare a geometric heuristic, RL from scratch, BC, and BC$\to$RL through complete grasp-transport-deposit cycles. BC and BC$\to$RL deposit 93.8% and 88.9% of pooled inventory, against 80.4% for the heuristic. BC$\to$RL deposits logs on 83.6% of its cycles, against 79.6% for the heuristic and 65.7% for BC, while its simulated stability gain does not carry over to the crane testbed. Trained entirely in simulation and run unchanged on the crane, the learned policies clear more than the hand-filtered heuristic while observing unfiltered clouds that still contain the storage rack's rails and poles.

作者：George Sideris, Lucas Bessai, Heshan Fernando, Elie Ayoub, Nicolas Lemieux, Inna Sharf
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## 原始参考来源 (Sources)

- [arXiv 2610.07613：Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane](https://arxiv.org/abs/2610.07613)