Researchers propose the MindHelper Challenge to evaluate a robot's ability to continuously interact with its environment and provide precise assistance when needed. The challenge requires an agent to observe the environment, maintain beliefs about human actions, identify when help is required, generate executable actions, and remain silent when intervention is unnecessary. To address this challenge, the authors introduce MindClaw, a framework that integrates belief tables, cognitive skills, and a trigger-based dispatcher. Experiments show that MindClaw outperforms baseline methods in terms of precise intervention rate and task accuracy. --- Why it matters: This research matters to engineers working on human-robot interaction because it provides a new benchmark for evaluating the ability of robots to provide precise assistance in dynamic environments. The proposed framework and challenge can be used to improve the design of robots that work alongside humans, leading to more efficient and effective collaboration. Source: https://arxiv.org/abs/2606.01063