用强化学习让伐木吊车自动抓起重木头，成功率96%。
Towards Autonomous Wood-Log Grasping with a Forestry Crane: Simulator and Benchmarking
- 用深度强化学习+课程策略实现自主抓取
- 在不同直径木头和随机初始位姿下成功率96%
- 开源仿真器与基准测试，供研究者复现
林业机械在林场环境中执行操作任务时面临挑战，尤其体现在欠驱动起重机系统的复杂动力学以及需抓取的重型原木。本研究探讨了使用强化学习实现林业起重机在自主抓取和提升重型原木任务中的可行性。首先，基于Mujoco物理引擎构建仿真器，真实模拟场景：利用CAD数据建模8自由度林业起重机，并生成不同尺寸的原木。随后，采用课程学习策略，结合速度控制器，实现基于深度强化学习的自主原木抓取。在新构建的仿真环境中，所提控制策略在不同直径原木及随机初始配置下均达到96%的成功率。此外，设计了奖励函数并实现了强化学习基线，为大规模操作任务提供开源基准。演示视频可访问 https://www.acin.tuwien.ac.at/en/d18a/
原文摘要 · Abstract (English)
Forestry machines operated in forest production environments face challenges when performing manipulation tasks, especially regarding the complicated dynamics of underactuated crane systems and the heavy weight of logs to be grasped. This study investigates the feasibility of using reinforcement learning for forestry crane manipulators in grasping and lifting heavy wood logs autonomously. We first build a simulator using Mujoco physics engine to create realistic scenarios, including modeling a forestry crane with 8 degrees of freedom from CAD data and wood logs of different sizes. We further implement a velocity controller for autonomous log grasping with deep reinforcement learning using a curriculum strategy. Utilizing our new simulator, the proposed control strategy exhibits a success rate of 96% when grasping logs of different diameters and under random initial configurations of the forestry crane. In addition, reward functions and reinforcement learning baselines are implemented to provide an open-source benchmark for the community in large-scale manipulation tasks. A video with several demonstrations can be seen at https://www.acin.tuwien.ac.at/en/d18a/
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