aiqbench analysis
This research addresses the challenge of robotic manipulation in agriculture by adapting pretrained vision-language-action models through a simulation-to-real transfer framework. The study demonstrates that integrating generalist policies with domain-specific demonstrations significantly improves task performance and robustness against environmental variations like occlusion and viewpoint shifts.
Robot Learning with Vision-Language-Action Models for Agricultural Manipulation
arXiv:2610.26717v1 Announce Type: new Abstract: Robotic manipulation in agricultural environments remains challenging due to variations in plant geometry, occlusion, and camera viewpoints. Pretrained vision-language-action (VLA) and generalist robot policies offer a promising approach to addressing these challenges by learning manipulation behaviors from multimodal observations and language instructions. However, adapting these policies to agricultural tasks requires domain-specific demonstrations, which are costly and time-consuming to collect on physical robots. In this work, we develop a simulation-to-real robot-learning framework for agricultural reaching and grasping that integrates task-specific demonstration collection and pretrained policy adaptation. A MuJoCo-based agricultural environment is developed with diverse tomato plant structures to support demonstration collection and controlled evaluation. Expert demonstrations are collected through joystick teleoperation, with a low-level controller used to execute robot actions. Two pretrained generalist robot policies, Octo and $\pi_{0.5}$, are adapted using simulated demonstrations, followed by further fine-tuning on a physical Franka Research 3 manipulator using real-robot demonstrations. The adapted policies are evaluated under in-distribution, viewpoint/spatial-shift, and out-of-distribution conditions in simulation, followed by physical-robot experiments. The influence of proprioceptive information on policy performance is also investigated. Results show that $\pi_{0.5}$ achieves higher task-performance scores than Octo and exhibits less degradation under distribution shifts. These findings demonstrate the potential of integrating pretrained generalist policies with task-specific demonstration learning for agricultural manipulation.
Abstract:Robotic manipulation in agricultural environments remains challenging due to variations in plant geometry, occlusion, and camera viewpoints. Pretrained vision-language-action (VLA) and generalist robot policies offer a promising approach to addressing these challenges by learning manipulation behaviors from multimodal observations and language instructions. However, adapting these policies to agricultural tasks requires domain-specific demonstrations, which are costly and time-consuming to collect on physical robots. In this work, we develop a simulation-to-real robot-learning framework for agricultural reaching and grasping that integrates task-specific demonstration collection and pretrained policy adaptation. A MuJoCo-based agricultural environment is developed with diverse tomato plant structures to support demonstration collection and controlled evaluation. Expert demonstrations are collected through joystick teleoperation, with a low-level controller used to execute robot actions. Two pretrained generalist robot policies, Octo and $\pi_{0.5}$, are adapted using simulated demonstrations, followed by further fine-tuning on a physical Franka Research 3 manipulator using real-robot demonstrations. The adapted policies are evaluated under in-distribution, viewpoint/spatial-shift, and out-of-distribution conditions in simulation, followed by physical-robot experiments. The influence of proprioceptive information on policy performance is also investigated. Results show that $\pi_{0.5}$ achieves higher task-performance scores than Octo and exhibits less degradation under distribution shifts. These findings demonstrate the potential of integrating pretrained generalist policies with task-specific demonstration learning for agricultural manipulation.
Robotics (cs.RO)
arXiv:2610.26717 [cs.RO]
(or arXiv:2610.26717v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.26717 Focus to learn more arXiv-issued DOI via DataCite
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