PSA-PPO for Robotic Hydroponic Lettuce Harvesting
Overview
This study introduces PSA-PPO, a target-conditioned local reaching framework for robotic harvesting of hydroponic lettuce. Trained entirely in simulation, PSA-PPO achieves 94.7% reaching rate in physical testing, outperforming classical planners. The method integrates safety constraints and trajectory-based encoding to improve performance in constrained environments.
From arXiv Robotics — research abstracts
Event evidence and corrections
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Environment: controlled real
“arm without any real-world policy fine-tuning. In a controlled physical benchmark against 150 scheduled targets, PSA-PPO achieved the highest reaching rate of 94.7%, outperforming Cartesian+TOTG (86.0%), RRTConnect (88.0%), and RRT* (80.0%), while reducing median RMS jerk by 39.8 to 73.0% relative to the classical planners during the final approach segment. A formulation ablation evaluated over 200 simulation target”
Exact source · revision 1Source owner not reported
Reported quantity · number of targets: 150 other · Basis not reported
“arm without any real-world policy fine-tuning. In a controlled physical benchmark against 150 scheduled targets, PSA-PPO achieved the highest reaching rate of 94.7%, outperforming Cartesian+TOTG (86.0%), RRTConnect (88.0%), and RRT* (80.0%), while reducing median RMS jerk by 39.8 to 73.0% relative to the classical planners during the final approach segment. A formulation ablation evaluated over 200 simulation target”
Exact source · revision 1Source owner not reported
Reported quantity · number of harvesting attempts: 144 other · Basis not reported
“sion module and an end-effector and evaluated as a complete greenhouse harvesting system across 144 lettuce harvesting attempts, achieving 94.4% vision-guided target reaching and 86.1% complete harvesting success.”
Exact source · revision 1Source owner not reported
Evidence dependency changes (2)
- 2026-10-30T22:50:44.016Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:50:24.313Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalSim-to-Real Reinforcement Learning for Path-Safe Target Reaching in Robotic Harvesting of Hydroponic Lettuce
This study introduces PSA-PPO, a target-conditioned local reaching framework for robotic harvesting of hydroponic lettuce. Trained entirely in simulation, PSA-PPO achieves 94.7% reaching rate in physical testing, outperforming classical planners. The method integrates safety constraints and trajectory-based encoding to improve performance in constrained environments.
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