Retargetting motion to new characters
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent Activity
IEEE Intelligent Systems
ICML '06 Proceedings of the 23rd international conference on Machine learning
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Generality and legibility in mobile manipulation
Autonomous Robots
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Expressing thought: improving robot readability with animation principles
Proceedings of the 6th international conference on Human-robot interaction
Inferring Skill from Tests of Programming Performance: Combining Time and Quality
ESEM '11 Proceedings of the 2011 International Symposium on Empirical Software Engineering and Measurement
CHOMP: Covariant Hamiltonian optimization for motion planning
International Journal of Robotics Research
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Evaluating directional cost models in navigation
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Communication of intent in assistive free flyers
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Familiarization to robot motion
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Deliberate delays during robot-to-human handovers improve compliance with gaze communication
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Goal-predictability vs. trajectory-predictability: which legibility factor counts
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
An emotional model for social robots: late-breaking report
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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A key requirement for seamless human-robot collaboration is for the robot to make its intentions clear to its human collaborator. A collaborative robot's motion must be legible, or intent-expressive. Legibility is often described in the literature as and effect of predictable, unsurprising, or expected motion. Our central insight is that predictability and legibility are fundamentally different and often contradictory properties of motion. We develop a formalism to mathematically define and distinguish predictability and legibility of motion. We formalize the two based on inferences between trajectories and goals in opposing directions, drawing the analogy to action interpretation in psychology. We then propose mathematical models for these inferences based on optimizing cost, drawing the analogy to the principle of rational action. Our experiments validate our formalism's prediction that predictability and legibility can contradict, and provide support for our models. Our findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion.