Sim-to-Real Reinforcement Learning for Path-Safe Target Reaching in Robotic Harvesting of Hydroponic Lettuce

Al Bashir; Partho Ghose; Yibin Wang; Robert G. Hardin; Joshua Peeples; Genhua Niu; Azlan Zahid

cs.ROOct 15, 2026

Sim-to-Real Reinforcement Learning for Path-Safe Target Reaching in Robotic Harvesting of Hydroponic Lettuce

Authors: Al Bashir, Partho Ghose, Yibin Wang, Robert G. Hardin, Joshua Peeples, Genhua Niu, Azlan Zahid

Organizations: Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX, 77843, USA · Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX, 75252, USA · Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, 77843, USA · Department of Horticultural Sciences, Texas A&M University, College Station, TX, 77843, USA

Abstract

Robotic harvesting requires precise manipulation to ensure accurate positioning of the end-effector, especially in a constraint workspace. This study introduces the Path-Safe Approach Proximal Policy Optimization (PSA-PPO), a target-conditioned local reaching framework designed for robotic harvesting of nutrient film technique (NFT)-hydroponic lettuce in greenhouses. The proposed PSA-PPO preserves the standard clipped PPO objective while introducing a harvesting-oriented policy formulation that integrates target-conditioned observations, corridor-aware spatial constraints, trajectory-history-based safety encoding, and task-specific success criteria for constrained robotic manipulation. PSAPPO was trained entirely in Isaac Lab and successfully transferred to a physical UR10e robotic 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 targets demonstrates that path-safety and side-corridor shaping are the principal drivers of harvesting-compatible performance: removing either term substantially degrades strict success even when terminal endpoint accuracy is preserved or improved, confirming that optimization toward the endpoint alone is an insufficient objective for constrained crop-proximal manipulation. Finally, PSA-PPO was integrated with an RGB-D vision 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.

Figures & tables

Figure 1: Overview of the proposed sim-to-real PSA-PPO harvesting framework. The robot-mounted RGB-D camera provides RGB imagery and aligned depth to the lettuce perception module, which generates temporally smoothed 3D cutting-point targets. PSA-PPO is trained entirely in Isaac Lab for target-conditioned, path-safe side-approach reaching. During physical deployment, the frozen policy and vision-derived targets are integrated through the ROS action-control layer to command the UR10e for side-approach harvesting of nutrient film technique (NFT)-grown lettuce.

Panel of Figure 2 — Figure 2: Training architecture of the path-safe, side-corridor approach policy (PSA-PPO) for the UR10e manipulator. Each target $\mathbf{p}^{T}$ defines a horizontal approach direction $\mathbf{a}^{T}$ and same-height staging reference $\mathbf{p}^{S}$ and determines a geometry-conditioned safe-start configuration. The actor receives the 42-dimensional policy observation $\boldsymbol{\xi}_{t}$ and produces a six-dimensional action $\bar{\mathbf{u}}_{t}$ , which is bounded, filtered, and scaled before application. The environment evaluates target closure, corridor following, tool orientation, wrist–TCP path safety, and motion regularity. Rollout data and critic estimates are used to compute returns and advantages for the clipped PPO update. The selected actor checkpoint is subsequently validated in simulation and exported for physical deployment.

Figure 3: Redesigned cut-and-hold lettuce harvesting end effector. (a–f) SolidWorks components and assembled geometry, including top, bottom, side, open, and closed configurations; (g) fabricated mechanism mounted on the OnRobot 2FG7 and UR10e manipulator.

Panel of Figure 4 — Figure 4: ROS-based harvesting-control architecture. Smoothed RGB-D cutting points are transformed to the robot frame and corrected for end-effector geometry. The harvest controller performs target-conditioned pre-positioning before activating the PSA-PPO policy for the learned local side approach. Policy outputs are mapped to UR10e joint commands through the ROS trajectory interface, with independent hardware-side timing and acceleration safeguards. A separate gripper-control branch actuates the 2FG7-driven cut-and-hold mechanism and releases harvested lettuce at the collection basket.

Evaluation	Population	Primary outcomes
Simulation validation	200 workspace targets	Reaching the target w/o any safety measures, path-safe reaching, strict success, final TCP error, and maximum below-plane violation
Formulation ablation	Same 200 targets per formulation	Reach without safety measures, path-safe, and strict success; target-frame behavior, maximum below-plane violation, trajectory length, and episode duration
Physical benchmark	150 scheduled targets per controller; $N=97$ common-success targets for continuous comparisons	Target-reaching reliability, endpoint error, local TCP acceleration and jerk, joint-position tracking error, normalized target convergence, and trajectory geometry
Greenhouse harvesting	144 harvesting attempts	Target reaching, cutting, retention, release, and complete harvesting success

Table 1: Evaluation protocol used for simulation, controlled physical benchmarking, and greenhouse harvesting.

Metric	PSA-PPO	w/o path safety	w/o side corridor	w/o motion reg.
Final reach (%)	99.0	89.0	100.0	98.0
Path-safe (%)	92.5	69.5	73.0	88.5
Strict success (%)	92.0	63.5	73.0	87.0
Final TCP error (mm)	32.3 [29.2, 35.1]	32.1 [28.3, 38.3]	25.3 [21.4, 28.6]	35.0 [30.5, 39.2]
P95 maximum below-plane violation (mm)	2.11	26.8	14.3	4.52
Trajectory length (m)	0.406 [0.264, 0.547]	0.443 [0.294, 0.567]	0.461 [0.294, 0.592]	0.464 [0.309, 0.592]

Table 2: Component-removal ablation of PSA-PPO over the same 200 simulation targets. Continuous quantities are reported as median [Q1,Q3].

Panel of Figure 5 — Figure 5: Effect of PSA-PPO component removal on harvesting-compatible simulation performance over the same 200 targets. (a) Final reaching, path-safe reaching, and strict success with Wilson 95% confidence intervals. (b) Empirical distribution of the maximum wrist–TCP below-plane violation; the dashed vertical line denotes the 2-mm strict path-safety threshold.

Figure 6: Target-frame trajectory profiles for PSA-PPO and the three component-removal formulations over the common 200-target evaluation set: (a) absolute lateral error, (b) absolute vertical error, (c) wrist–TCP below-plane violation, and (d) TCP distance to the target as functions of normalized staging-to-target progress. Curves denote median behavior and shaded regions the corresponding trajectory dispersion.

Figure 7: Strict-success performance of PSA-PPO and the three component-removal formulations across common bins of target radius, target azimuth, and target height (left to right). The bin-wise results characterize variation over the evaluated target set for the selected checkpoints and not variability across independent policy retraining.

Controller	Reached	Failed	Success (%) [95% CI]
PSA-PPO	142/150	8	94.7 [89.8, 97.3]
Cartesian+TOTG	129/150	21	86.0 [79.5, 90.7]
RRTConnect	132/150	18	88.0 [81.8, 92.3]
RRT*	120/150	30	80.0 [72.9, 85.6]

Table 3: Physical target-reaching reliability of PSA-PPO and the classical planning baselines under the common 20-mm reaching criterion. Here, CI denotes the confidence interval.

Figure 8: Empirical cumulative distribution of physical TCP endpoint error. The vertical dashed line denotes the 20-mm reaching criterion. Planner-based control produced tighter endpoint distributions, whereas PSA-PPO exhibited a broader but predominantly tolerance-compliant distribution.

Figure 9: Recorded failure-related events during the physical controller benchmark. Cell counts denote diagnostic events and are not necessarily mutually exclusive; the corresponding numbers of unique unsuccessful targets were 8 for PSA-PPO, 21 for Cartesian+TOTG, 18 for RRTConnect, and 30 for RRT*.

Metric	PSA-PPO	Cartesian+TOTG	RRTConnect	RRT*
RMS acceleration (m/s 2 )	0.1270	0.5277	0.5549	0.2668
RMS jerk (m/s 3 )	11.38	42.17	40.74	18.91
99.5th-percentile jerk (m/s 3 )	77.72	131.88	164.77	127.84
Joint-position tracking RMSE (rad)	$\boldsymbol{6.96\times 10^{-4}}$	$2.56\times 10^{-3}$	$2.47\times 10^{-3}$	$1.15\times 10^{-3}$

Table 4: Physical motion quality within the final 0.22-m target-relative approach. Values are medians over the common-success target set (

N=97

). Lower values indicate lower dynamic excitation or tracking error.

Panel of Figure 10 — Figure 10: Measured trajectory profiles for the four physical controllers over the common-success target population: (a) TCP speed, (b) acceleration, (c) jerk over normalized active approach progression, and (d) perpendicular deviation from the start–target reference line over normalized longitudinal progress. The normalized axes describe trajectory progression rather than absolute wall-clock execution time.

Figure 11: Normalized target-distance contraction during the final 0.22-m approach over the common-success target set. Remaining TCP-to-target distance is normalized by its value at local-window entry, while the horizontal axis represents each trajectory’s normalized active progression. PSA-PPO reached 50% remaining distance earlier in normalized progression than the three planner-based controllers.

Harvesting stage	Successful	Overall (%)	Conditional (%)
Target reached	136/144	94.4	94.4
Lettuce cut	124/144	86.1	91.2

Table 5: Stage-wise performance of the integrated robotic lettuce harvesting system under greenhouse conditions.

Figure 12: Representative online target stream during greenhouse harvesting. The image was acquired from the robot-mounted RGB-D camera while the UR10e remained at the home configuration. The vision node identified the available lettuce targets and published their corresponding 3D cutting-point coordinates for subsequent robot-frame transformation and target-conditioned reaching.

Panel of Figure 13 — Figure 13: Representative greenhouse execution of the integrated harvesting pipeline: (a) PSA-PPO-controlled side approach toward the vision-derived cutting location; (b) simultaneous cutting and retention using the 2FG7-driven cut-and-hold end effector; and (c) release of the harvested lettuce into the collection basket.

Panel of Figure 14 — Figure 14: Physical controller trade-offs between target-reaching reliability and (a) median RMS jerk within the final 0.22-m approach and (b) median trajectory detour ratio. PSA-PPO favors the reliability–dynamic-excitation operating regime, whereas the planner-based controllers retain greater geometric directness.

Appendix figures & tables24 assets

Supplementary material from the paper’s appendix.

Appendix

Parameter	Value	Definition
Observation dimension	42	Policy input dimension
Action dimension	6	UR10e joint-space action dimension
Simulation step	$1/120$ s	Physics integration step
Control decimation	4	Physics steps per policy action
Policy frequency	30 Hz	Effective policy rate
Episode limit	9.0 s	Maximum training episode duration

Appendix

Table A.1 : Policy interaction, action-processing, capture, and path-safety parameters.

Setting	Value	Implementation
Training-target distribution
Target radius	$[0.60,1.25]$ m	Horizontal front-workspace radius
Target azimuth	$[-85^{\circ},85^{\circ}]$	Front-workspace azimuth
Target height	$[0.50,0.68]$ m	Vertical target range
Approach distance $d_{a}$	$[0.18,0.28]$ m	Same-height staging-to-target distance
Base radial sampling	Area-uniform	$\rho=\sqrt{\rho_{min}^{2}+u(\rho_{max}^{2}-\rho_{min}^{2})}$

Appendix

Table A.2 : Training-workspace sampling and target-conditioned safe-start configuration.

Component	Implemented term	Weight	Definition / activation
$\mathcal{R}_{t}^{tar}$	Staging-distance progress; staging distance	$+25;\;-10$	Multiplied by $1-\sigma(14(\ell_{t}-0.02))$
$\mathcal{R}_{t}^{tar}$	Target-distance progress; axial-error progress; line-progress increment; clipped line progress	$+720;\;+610;\;+360;\;+185$	Distance/axial increments clipped to $\pm 0.04$ ; line increment to $\pm 0.05$ ; direct $\ell_{t}$ term clipped to $[-0.3,1]$
$\mathcal{R}_{t}^{tar}$	$\exp(-7d_{t}^{T})$ ; target shells at $d_{t}^{T}<0.15,0.10,0.07$ m	$+145;\;+105;\;+165;\;+260$	Near-target shaping
$\mathcal{R}_{t}^{tar}$	Stop-short; staging loiter; target distance; $\|e_{t}^{ax}\|$ ; $\|e_{t}^{lat}\|$ ; $\|e_{t}^{z}\|$ ; axial overshoot	$-950;\;-260;\;-190;\;-150;$ $-180;\;-260;\;-360$	Staging loiter is active within 0.09 m of $\mathbf{p}^{S}$ when $d_{t}^{T}>0.10$ m; target-overshoot margin is 0.025 m
$\mathcal{R}_{t}^{cor}$	Excessive TCP height; line-band violation; lateral-band violation; vertical-band violation; corridor axial overshoot	$-260;\;-330;\;-310;\;-360;$ $-130$	The first four terms use $\sigma(10(\ell_{t}+0.08))$ where applicable; excessive-height threshold is 0.030 m
$\mathcal{R}_{t}^{ori}$	Tool-axis alignment reward; approach-axis deficit; vertical-orientation violation; soft vertical-orientation violation	$+150;\;-1100;\;-780;\;-320$	Alignment reward $\exp[-5(1-c_{t}^{a})]$ ; hard/soft vertical thresholds are 0.12/0.085

Appendix

Table A.3 : Implementation-level reward terms used for the final PSA-PPO policy. The component symbols correspond to the compact decomposition in the main text.

Formulation	Training modification relative to PSA-PPO	Preserved elements
PSA-PPO	No component removed. The $+2400$ strict-capture bonus required final capture, acceptable safe-start clearance, and acceptable accumulated path clearance. Successful termination used the same strict predicate, and accumulated below-plane violation above 0.050 m triggered hard-safety termination.	Complete target, corridor, orientation, path-safety, and motion-regularization formulation.
w/o path safety (C2b)	Removed the five path-safety reward terms: $+80\,\mathbb{I}[b_{t}\leq\varepsilon_{\mathrm{safe}}]$ , $-4200\,b_{t}$ , $-12000\,b_{t}^{2}$ , $-3600\,\max(b_{t}^{\max}-\varepsilon_{\mathrm{safe}},0)$ , and $-2600\,b_{0}$ . The $+2400$ bonus remained, but its gate was changed from strict capture to final capture only. Training success termination likewise used final capture only, and hard-safety termination was disabled.	The 42-D observation, including wrist–TCP clearance channels, was unchanged. Target/corridor/orientation rewards, motion regularization, safe-start logic, and path-safety logging were retained. Strict path safety was restored only in the common post-training evaluator.
w/o side corridor (C2c)	Removed staging/corridor shaping: $+25$ staging-progress, $-10$ staging-distance, $+360$ line-progress increment, $+185$ clipped line progress, $-260$ staging loiter, $-260$ excessive-TCP-height, $-330$ line-band, $-310$ lateral-band, $-360$ vertical-band, and $-130$ corridor axial-overshoot terms.	Target-distance and target-frame error terms, orientation, path-safety rewards/history, strict-capture termination, hard-safety termination, and motion regularization were unchanged.
w/o motion regularization (C2d)	Removed the four action/joint regularizers: $-0.0035\\|\mathbf{u}_{t}\\|_{2}^{2}$ , $-0.015\\|\Delta\mathbf{u}_{t}\\|_{2}^{2}$ , $-0.026\\|\Delta^{2}\mathbf{u}_{t}\\|_{2}^{2}$ , and $-0.00050\\|\dot{\mathbf{q}}_{t}\\|_{2}^{2}$ . The constant per-step cost $-0.10$ was retained.	Target, corridor, orientation, path-safety, strict-capture, and termination logic were unchanged.

Appendix

Table A.4 : Exact training modifications used for the PSA-PPO component-removal ablations. Only the listed reward or termination logic was changed relative to the full formulation; observation dimensionality, action processing, target sampling, safe-start construction, capture thresholds, and the post-training strict evaluator were retained.

Parameter	Value	Setting
Training seed	42	RSL-RL seed
Rollout length	32 steps/env.	Samples per environment/update
Maximum iterations	12,000	PPO iterations
Actor hidden layers	256–256–128	Fully connected
Critic hidden layers	256–256–128	Fully connected
Activation	ELU	Hidden activation

Appendix

Table A.5 : PPO optimization and actor–critic network configuration.

Parameter	Value	Setting
Targets per formulation	200	Identical target list
Target radius	$[0.60,1.25]$ m	Front workspace
Target azimuth	$[-85^{\circ},85^{\circ}]$	Front workspace
Target height	$[0.50,0.68]$ m	Vertical range
Radius sampling	Area-uniform	Horizontal annulus
Target-generation seed	20260724	Fixed evaluation set

Appendix

Table B.1 : Fixed random-workspace simulation-evaluation contract.

Outcome	Contrast	PSA-PPO (%)	Variant (%)	$\Delta$ (pp) [95% CI]	Holm $p$
Final reach	w/o path safety	99.0	89.0	10.0 [6.0, 14.5]	$3.29\times 10^{-5}$
	w/o side corridor	99.0	100.0	$-1.0$ [ $-2.5$ , 0.0]	1.000
	w/o motion regularization	99.0	98.0	1.0 [ $-1.5$ , 3.5]	1.000
Path-safe	w/o path safety	92.5	69.5	23.0 [16.5, 29.5]	$1.14\times 10^{-10}$
	w/o side corridor	92.5	73.0	19.5 [14.0, 25.5]	$1.14\times 10^{-10}$
	w/o motion regularization	92.5	88.5	4.0 [1.0, 7.5]	0.0386

Appendix

Table B.2 : Paired binary outcome differences between PSA-PPO and the three component-removal formulations over the common 200-target set. Positive

\Delta

favors PSA-PPO.

Figure B.1 : Paired strict outcomes for each component removal over the same 200 targets. Each bar partitions targets into cases where both policies were strictly successful, only PSA-PPO was successful, only the ablated policy was successful, or both failed.

\Delta

denotes the strict-success rate of PSA-PPO minus that of the corresponding ablation.

Figure B.2 : Target-wise simulation outcomes over the common workspace for PSA-PPO and the three component-removal formulations. Markers distinguish strict success, safety-only failure, final-only failure, and failure of both criteria. The plots localize evaluation failures but do not represent variation across policy retraining seeds.

Dimension	Interval	$N$	Final (%)	Path-safe (%)	Strict (%)	Strict 95% CI	Median error / P95 violation (mm)
Radius	[0.6000, 0.7625) m	44	97.7	95.5	93.2	[81.8, 97.7]	33.16 / 1.34
Radius	[0.7625, 0.9250) m	45	100.0	97.8	97.8	[88.4, 99.6]	33.63 / 0.00
Radius	[0.9250, 1.0875) m	47	100.0	95.7	95.7	[85.8, 98.8]	29.89 / 0.36
Radius	[1.0875, 1.2500] m	64	98.4	84.4	84.4	[73.6, 91.3]	33.15 / 3.63
Azimuth	[-85.0, -51.0) deg	37	97.3	78.4	78.4	[62.8, 88.6]	32.40 / 6.03
Azimuth	[-51.0, -17.0) deg	42	100.0	95.2	95.2	[84.2, 98.7]	33.11 / 0.62

Appendix

Table B.3 : Numerical workspace robustness of the selected PSA-PPO checkpoint across predefined equal-width bins.

Figure B.3 : Representative trajectory overlays for the three component-removal contrasts. The displayed targets were selected from the common evaluation set to expose the corresponding contrast: target 65 for path-safety removal, target 31 for side-corridor removal, and target 199 for motion-regularization removal. The star marks the target location. These examples supplement, rather than replace, the paired population statistics.

Figure B.4 : Post-hoc fixed-band corridor diagnostics over the common target set: fraction of trajectory samples inside both lateral and vertical bands, per-target P95 absolute lateral error, and per-target P95 absolute vertical error. The distributions do not exhibit a uniform ordering across formulations and should not be interpreted as a direct measurement of the complete corridor reward.

Figure B.5 : Empirical distributions of direct motion-regularization diagnostics: raw-action-rate RMS, raw-action-acceleration RMS, and joint-velocity-norm RMS. The no-motion-regularization policy does not shift these quantities uniformly toward larger values, demonstrating that the component removal changes the learned operating point rather than providing a monotonic smoothness test.

Figure B.6 : Final tool-orientation distributions over the common evaluation set: approach-axis alignment (left) and absolute tool-axis vertical component (right). Orientation terms and final orientation criteria were unchanged across the three component-removal formulations.

Parameter	Value	Role
HOME TCP position	$[-0.164,\;0.641,\;0.382]$ m	Robot observation/home pose
Benchmark tool orientation $(w,x,y,z)$	$[0.009162454,\;0.005118748,\;0.718754832,\;0.695184393]$	Common fixed physical orientation
Base frame	robot_arm_base_link	Cartesian reference frame
Tool frame	robot_arm_tool0	Measured TCP frame
Paper evaluation tolerance	0.020 m	Binary target-reaching criterion
ROS monitor tolerance	0.002 m	Online execution-monitor threshold

Appendix

Table C.1 : Principal physical execution and logging parameters used for PSA-PPO deployment and the controlled real-robot benchmark.

Setting	Value	Application
Move group	arm	All MoveIt baselines
RRTConnect backend	OMPL RRTConnect ( RRTConnectkConfigDefault )	Pose planning using the frozen RRTConnect planner ID
RRT* backend	OMPL RRT* ( RRTstar )	Pose planning using the post-fix RRT* planner ID
Cartesian backend	Cartesian linear + TOTG ( CartesianLinearTOTG )	Collision-checked Cartesian interpolation followed by retiming
Planning time	8.0 s	OMPL pose planners
Planning attempts	3	OMPL pose planners

Appendix

Table C.2 : MoveIt baseline configuration used in the controlled physical benchmark. Parameters listed as common were applied to the planner-based controllers unless the backend required a method-specific setting.

	PSA-PPO	Cartesian+TOTG	RRTConnect	RRT*
Scheduled targets	150	150	150	150
Archived measured trajectories	150	150	150	125
Paper-level reached targets	142	129	132	120
Paper-level failed targets	8	21	18	30

Appendix

Table C.3 : Physical benchmark populations used for binary and continuous controller comparisons.

Metric	PSA-PPO	Cartesian+TOTG	RRTConnect	RRT*
RMS acceleration (m/s 2 )	0.1776	0.5776	0.5856	0.2739
RMS jerk (m/s 3 )	15.82	47.68	45.01	19.91
99.5th-percentile jerk (m/s 3 )	105.11	143.38	172.29	127.58
Joint-position tracking RMSE (rad)	$8.41\times 10^{-4}$	$2.87\times 10^{-3}$	$2.76\times 10^{-3}$	$1.31\times 10^{-3}$

Appendix

Table C.4 : Selected physical motion metrics over the complete reconstructed approach on the common-success target set (

N=97

). Values are controller medians. Lower values indicate lower dynamic excitation or tracking error.

Metric	Planner	PSA-PPO reduction (%)	Median paired difference [95% CI]	Holm-adjusted $p$
RMS acceleration (m/s 2 )	Cartesian+TOTG	75.9	$-0.4012$ [ $-0.4118,-0.3953$ ]	$4.02\times 10^{-16}$
	RRTConnect	77.1	$-0.4295$ [ $-0.4419,-0.4162$ ]	$4.02\times 10^{-16}$
	RRT*	52.4	$-0.1332$ [ $-0.1398,-0.1303$ ]	$4.02\times 10^{-16}$
RMS jerk (m/s 3 )	Cartesian+TOTG	73.0	$-30.71$ [ $-31.79,-29.83$ ]	$4.02\times 10^{-16}$
	RRTConnect	72.1	$-29.34$ [ $-30.62,-28.54$ ]	$4.02\times 10^{-16}$
	RRT*	39.8	$-7.177$ [ $-7.625,-6.442$ ]	$4.02\times 10^{-16}$

Appendix

Table C.5 : Paired effect-size analysis for PSA-PPO relative to each classical planner within the final 0.22-m target-relative approach. Differences are computed target-wise over the identical common-success set (

N=97

Controller	Median TCP error (mm)	Fraction of 20-mm tolerance (%)
PSA-PPO	9.78	48.9
Cartesian+TOTG	0.06	0.3
RRTConnect	1.62	8.1
RRT*	1.61	8.1

Appendix

Table C.6 : Terminal positioning accuracy on the common-success physical target set. The physical reaching tolerance was 20 mm.

Metric	PSA-PPO	Cartesian+TOTG	RRTConnect	RRT*
Detour ratio	1.8094	1.6697	1.6812	1.6962
Path efficiency	0.5527	0.5989	0.5948	0.5896
Backtracking ratio	0.0371	0.0178	0.0195	0.0218
Total Cartesian turning ( ∘ )	410.09	16.91	75.58	75.14
Base-azimuth travel ( ∘ )	26.25	25.73	25.97	26.45
Total joint travel (rad)	3.108	2.850	2.910	2.723

Appendix

Table C.7 : Trajectory-geometry and spatial-excursion statistics over the full reconstructed physical approach. Median quantities characterize typical path geometry;

P_{95}

and maximum deviations characterize distribution tails.

Planner	Witnessed failures	Additional ranked identities selected	Archived trajectories	Hard-event selected trials	Highest-priorityt diagnostic target
Cartesian+TOTG	21	19	150	0	T119
RRTConnect	18	18	150	3	T040
RRT*	30	5	125	4	T087

Appendix

Table C.8 : Summary of the anomaly-ranked witnessed-count sensitivity construction for the planner baselines.

Figure C.1 : Minimum wrist- and elbow-angle margins to the nearest multiple of

\pi

in the physical benchmark. The dashed

5^{\circ}

reference denotes the diagnostic threshold used by the anomaly-ranking sensitivity analysis. The quantity is kinematic evidence only; near-threshold configurations are not interpreted as proof of singularity-induced failure.

Panel of Figure 43 — Figure C.2 : Matched target-level trajectory diagnostics from the anomaly-ranked sensitivity analysis: (a) PSA-PPO and Cartesian+TOTG on T119, (b) PSA-PPO and RRTConnect on T040, and (c) PSA-PPO and RRT* on T087. The examples are the highest-priority selected planner cases for their respective methods and are not representative population trajectories. Spatially expansive motion and near-singularity diagnostics are not interpreted as collision events or as proof of failure causation.

Robotic Harvesting

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The Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China. · College of Engineering, Huazhong Agricultural University, Wuhan 430070, China

May 22, 2026J/K move · Enter open · S save

Robotic Strawberry Harvesting with Robust Vision and Deep Reinforcement Learning based Sim-to-Real Control

Al Bashir, Shao-Yang Chang, Partho Ghose, Prem Raj+2

Department of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA · Department of Biomechatronics Engineering, National Taiwan University, 10617, Taipei City, Taiwan · Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA

RL for Robotics Instance Segmentation Agricultural Robotics Robotic Harvesting

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This study presents a closed-loop robotic strawberry harvesting system that combines a robust vision module, simulation-trained deep reinforcement learning (DRL) control, and ROS-based realrobot execution. For perception, we propose HRAttnEdge-YOLO26-seg, a modified YOLO26-seg architecture that incorporates a high-resolution P2 branch, segmentation-path attention, and edgesupervised prototype learning to improve instance segmentation in cluttered scenes. For control, we train a target-conditioned Proximal Policy Optimization (PPO) policy in Isaac Lab to produce smooth joint-position commands for a UR10e manipulator and deploy it on a UR10e robot for targetfruit reaching and harvesting. This simulation-based approach reduces hardware dependency, lowers development cost, and allows scalable policy training without exhaustive physical trials before real deployment. The proposed vision model demonstrated the highest overall performance among the evaluated methods. On both self-collected and public datasets, the model showed a 10 to 14% improvement in segmentation performance. In controlled in-house tests, the PPO controller produced stable and dynamically smoother motion than a inverse kinematics (IK)-based MoveIt baseline. In greenhouse trials, the proposed integrated system harvested 281 strawberries, achieving 96.6% reaching success, 91.3% grasp-and-pull success, and 84.3% overall harvesting success. These results illustrate that task-specific perception combined with simulation-trained PPO can serve as a practical and resource-efficient alternative to conventional planner-dependent reaching in manipulation, enabling reliable closed-loop robotic harvesting in complex agricultural environments.

Abstract

Robotic Harvesting

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