RICE: Reactive Interaction Controller for Cluttered Canopy Environment

-- Robotic navigation in dense, cluttered environments such as agricultural canopies presents significant challenges due to physical and visual occlusion caused by leaves and branches. Traditional vision-based or model-dependent approaches often fail in these settings, where physical interaction without damaging foliage and branches is necessary to reach a target. We present a novel reactive controller that enables safe navigation for a robotic arm in a contact-rich, cluttered, deformable environment using end-effector position and real-time tactile feedback. Our proposed framework's interaction strategy is based on a trade-off between minimizing disturbance by maneuvering around obstacles and pushing through them to move towards the target. We show that over 35 trials in 3 experimental plant setups with an occluded target, the proposed controller successfully reached the target in all trials without breaking any branch and outperformed the state-of-the-art model-free controller in robustness and adaptability. This work lays the foundation for safe, adaptive interaction in cluttered, contact-rich deformable environments, enabling future agricultural tasks such as pruning and harvesting in plant canopies. Robots struggle to operate in an agricultural environment due to dense and unstructured clutter, such as overlapping leaves and branches [1]. This clutter creates both physical obstructions, which require robots to interact with or navigate around obstacles, and visual occlusions, which hinder perception and path planning toward targets like fruits. When navigating cluttered environments, there are generally three possible strategies: pushing through obstacles, navigating around them, or adaptively combining both [2].