Implicit Coordination in Robotic Teams using Learned Prediction Models

Many application tasks require the cooperation of two or more robots. Humans are good at cooperation in shared workspaces, because they anticipate and adapt to the intentions and actions of others. In contrast, multi-agent and multi-robot systems rely on communication to exchange their intentions. This causes problems in domains where perfect communication is not guaranteed, such as rescue robotics, autonomous vehicles participating in traffic, or robotic soccer. In this paper, we introduce a computational model for implicit coordination, and apply it to a typical coordination task from robotic soccer: regaining ball possession. The computational model specifies that performance prediction models are necessary for coordination, so we learn them off-line from observed experience. By taking the perspective of the team mates, these models are then used to predict utilities of others, and optimize a shared performance model for joint actions. In several experiments conducted with our robotic soccer team, we evaluate the performance of implicit coordination

Authors

• Freek Stulp
• Michael Isik
• Michael Beetz

Keywords

• Robot kinematics
• Predictive models
• Humans
• Intelligent robots
• Robotics and automation
• Multirobot systems
• Remotely operated vehicles
• Mobile robots
• Computational modeling
• Fasteners
• Prediction Model
• Swarm Robotics
• Implicit Coordination
• Computational Model
• Team Sports
• Joint Action
• Autonomous Vehicles
• Actions Of Others
• Coordination Model
• Soccer Team
• Coworking Spaces
• Neural Network
• Learning Models
• Decision Tree
• Mean Absolute Error
• Kinetic Experiments
• Tree Model
• Temporal Model
• Log Files
• Human-robot Interaction
• Position Of The Robot
• Navigation Task
• Belief State
• State Estimation Error
• Static Experiments
• Strategic Considerations
• Temporal Prediction
• Real Robot
• Laser Ranging
• Intentions Of Others