Learned robotic grasping improves log pile clearing with hydraulic cranes
Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane
RoboticsMachine Learning
Summary
Clearing heavy piles of logs with hydraulic cranes is hard because logs are tightly stacked and hard to grab. The authors designed a computer program that looks at 3D scans and decides exactly where and how to grasp the logs. They taught this program using examples of good grabs and then improved it with trial and error in simulations. When tested on a real crane, their program performed better than a simple rule-based method at picking up and moving logs.
What this means in practice
- •For forestry equipment operators: Automate the selection of grapple positions to improve efficiency and success rate in clearing dense log piles with hydraulic cranes.
- •For industrial robot programmers: Integrate learned grasping policies trained in simulation to robotic systems handling irregular objects in cluttered environments without manual segmentation.
Authors
George Sideris, Lucas Bessai, Heshan Fernando, Elie Ayoub, Nicolas Lemieux, Inna Sharf
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.