robotics
The Lettuce Harvester Learns That Reaching Is Not Enough
A sim-trained policy protects the approach corridor, transfers to a UR10e without real-world fine-tuning and completes 86.1 percent of harvest attempts.
Summary
A sim-trained policy protects the approach corridor, transfers to a UR10e without real-world fine-tuning and completes 86.1 percent of harvest attempts.
A robot can hit the target point and still damage a crop on the way in. PSA-PPO adds corridor-aware constraints, trajectory-history safety encoding and task-specific success criteria to a target-conditioned reaching policy trained entirely in Isaac Lab. On 150 scheduled physical targets, it reached 94.7 percent, beating three classical planners while reducing final-approach jerk. Integrated with RGB-D perception and an end effector, the system reached 94.4 percent of targets and completed 86.1 percent of 144 lettuce harvest attempts. The results come from a controlled hydroponic setup and do not establish commercial farm readiness.
Why it matters
A sim-trained policy protects the approach corridor, transfers to a UR10e without real-world fine-tuning and completes 86.1 percent of harvest attempts.
Limits and context
- The results come from a controlled hydroponic setup and do not establish commercial farm readiness.
Key claims
A sim-trained policy protects the approach corridor, transfers to a UR10e without real-world fine-tuning and completes 86.1 percent of harvest attempts.
Qualification: The results come from a controlled hydroponic setup and do not establish commercial farm readiness.
Evidence: source-2026-10-18-009
Sources
- arXiv preprint 2610.22337, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.