Visibility-Aware Mobile Grasping in Dynamic Environments
For robots operating in dynamic, unknown environments, this work provides a practical system that significantly improves success rates and collision safety over existing methods.
This paper tackles mobile grasping in dynamic, unknown environments with limited field-of-view, proposing a unified system with whole-body planning and behavior-tree-based high-level planning. It achieves 68.8% and 58.0% success rates in static and dynamic environments, improving by 22.8% and 18.0% over the baseline.
This paper addresses the problem of mobile grasping in dynamic, unknown environments where a robot must operate under a limited field-of-view. The fundamental challenge is the inherent trade-off between ``seeing'' around to reduce environmental uncertainty and ``moving'' the body to achieve task progress in a high-dimensional configuration space, subject to visibility constraints. Previous approaches often assume known or static environments and decouple these objectives, failing to guarantee safety when unobserved dynamic obstacles intersect the robot's path during manipulation. In this paper, we propose a unified mobile grasping system comprising two core components: (1) an iterative low-level whole-body planner coupled with velocity-aware active perception to navigate dynamic environments safely; and (2) a hierarchical high-level planner based on behavior trees that adaptively generates subgoals to guide the robot through exploration and runtime failures. We provide experimental results across 400 randomized simulation scenarios and real-world deployment on a Fetch mobile manipulator. Results show that our system achieves a success rate of 68.8\% and 58.0\% in unknown static and dynamic environments, respectively, significantly boosting success rates by 22.8\% and 18.0\% over the \nam approach in both unknown static and dynamic environments, with improved collision safety.