RICE: Reactive Interaction Controller for Cluttered Canopy Environment

2025-06-12

AI Summary

Addressing the challenge of robotic arm navigation in dense, deformable, and heavily visually and physically occluded environments—such as agricultural tree canopies—this paper proposes a reactive interactive control strategy integrating tactile feedback and pose estimation. The method enables safe, continuous-contact navigation and precise target acquisition by dynamically balancing obstacle circumvention and controlled penetration, without requiring prior environmental models. It unifies real-time tactile sensing, high-precision end-effector position control, heuristic obstacle interaction decision-making, and a model-free reactive framework. Evaluated across three realistic plant scenarios with 35 trials, the approach achieved 100% success rate in reaching occluded targets, zero branch damage, and demonstrated significantly enhanced robustness and adaptability compared to existing model-free methods.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• Safe robotic navigation in dense agricultural canopies with occlusion
• Minimizing foliage damage while interacting with obstacles
• Enabling adaptive arm movement using real-time tactile feedback

Innovation

• Reactive controller using tactile feedback
• Trade-off between obstacle avoidance and pushing
• Safe navigation in deformable cluttered environments