Collaborative Interaction Models for Optimized Human-Robot Teamwork

Effective human-robot collaboration requires informed anticipation. The robot must anticipate the human’s actions, but also react quickly and intuitively when its predictions are wrong. The robot must plan its actions to account for the human’s own plan, with the knowledge that the human’s behavior will change based on what the robot actually does. This cyclical game of predicting a human’s future actions and generating a corresponding motion plan is extremely difficult to model using standard techniques. In this work, we describe a novel Model Predictive Control (MPC)-based framework for finding optimal trajectories in a collaborative, multi-agent setting, in which we simultaneously plan for the robot while predicting the actions of its external collaborators. We use human-robot handovers to demonstrate that with a strong model of the collaborator, our framework produces fluid, reactive human-robot interactions in novel, cluttered environments. Our method efficiently generates coordinated trajectories, and achieves a high success rate in handover, even in the presence of significant sensor noise.

Authors

• Adam Fishman
• Chris Paxton 0001
• Wei Yang 0019
• Dieter Fox
• Byron Boots
• Nathan D. Ratliff

Keywords

• Robot kinematics
• Handover
• Predictive models
• Robot sensing systems
• Trajectory
• Teamwork
• Standards
• Collaborative Model
• Path Planning
• Model Predictive Control
• Human-robot Collaboration
• Planning Of Robots
• Prediction Model
• Time Step
• Cost Function
• Control Agents
• Radial Basis Function
• External Agents
• End-effector
• Maximum A Posteriori
• Collaborative System
• Collaborative Tasks
• Joint Limits
• Task Space
• End Of The Trajectory
• End-effector Position
• Factor Graph
• Model Predictive Control Approach
• Model Predictive Control Algorithm
• Ith Agent
• World Frame
• Equilibrium Behavior
• Trajectories Of System
• Presence Of Obstacles
• Global Minimum
• Obstacle Avoidance
• Soft Constraints