Human-Machine Collaborative Systems for Microsurgical Applications
International Journal of Robotics Research
Predictive interaction using the delphian desktop
Proceedings of the 18th annual ACM symposium on User interface software and technology
Planning Algorithms
Endpoint prediction using motion kinematics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Motion intention recognition in robot assisted applications
Robotics and Autonomous Systems
A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Using prediction to enhance remote robot supervision across time delay
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
On responsiveness, safety, and completeness in real-time motion planning
Autonomous Robots
Strategies for human-in-the-loop robotic grasping
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Probabilistic pointing target prediction via inverse optimal control
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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The approach of inferring user's intended task and optimizing low-level robot motions has promise for making robot teleoperation interfaces more intuitive and responsive. But most existing methods assume a finite set of candidate tasks, which limits a robot's functionality. This paper proposes the notion of freeform tasks that encode an infinite number of possible goals (e.g., desired target positions) within a finite set of types (e.g., reach, orient, pick up). It also presents two technical contributions to help make freeform UIs possible. First, an intent predictor estimates the user's desired task, and accepts freeform tasks that include both discrete types and continuous parameters. Second, a cooperative motion planner continuously updates the robot's trajectories to achieve the inferred tasks by repeatedly solving optimal control problems. The planner is designed to respond interactively to changes in the indicated task, avoid collisions in cluttered environments, handle time-varying objective functions, and achieve high-quality motions using a hybrid of numerical and sampling-based techniques. The system is applied to the problem of controlling a 6D robot manipulator using 2D mouse input in the context of two tasks: static target reaching and dynamic trajectory tracking. Simulations suggest that it enables the robot to reach intended targets faster and to track intended trajectories more closely than comparable techniques.