This paper presents an end-to-end deep reinforcement learning (RL) framework for occlusion-aware robotic manipulation in cluttered plant environments. Our approach enables a robot to interact with a deformable plant to reveal hidden objects of interest, such as fruits, using multimodal observations. We decouple the kinematic planning problem from robot control to simplify zero-shot sim2real transfer for the trained policy. Our results demonstrate that the trained policy, deployed using our framework, achieves up to 86.7% success in real-world trials across diverse initial conditions. Our findings pave the way toward autonomous, perception-driven agricultural robots that intelligently interact with complex foliage plants to "find the fruit" in challenging occluded scenarios, without the need for explicitly designed geometric and dynamic models of every plant scenario.
View on arXiv@article{subedi2025_2505.16547,
title={ Find the Fruit: Designing a Zero-Shot Sim2Real Deep RL Planner for Occlusion Aware Plant Manipulation },
author={ Nitesh Subedi and Hsin-Jung Yang and Devesh K. Jha and Soumik Sarkar },
journal={arXiv preprint arXiv:2505.16547},
year={ 2025 }
}