Humans and animals can make real - time adjustments to movements by imagining their action outcomes to prevent unanticipated o r even catastrophic motion failures in unknown unstructured environments. Action imagination, as a refined sensorimotor strategy, leverages perception - action loops to handle physical interaction -induced uncer tainties in perception and system modeling within complex systems. Inspired by the action -awareness capability of animal intelligence, this study proposes a n imagination - inspired motion planner (I -MP) framework that speci fically enhances robots' action reliability by imagining plausible spatial states for approaching . After topologizing the workspace, I -MP build perception-action loop enabling robots autonomously build contact models. Leveraging fixed-point theory and Hausdorff distance, the planner compute s convergent spatial states under interaction characteristics and mission constraints. By homogenously representing multi - dimensional environmental characteristics through work, the robot can approach the imagined spatial states via real - time computation o f energy gradients. Consequently, e xperimental results demonstrate the practicality and robustness of IMP in complex cluttered environments.

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.