21,787 research outputs found
Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis
This work handles the inverse reinforcement learning (IRL) problem where only
a small number of demonstrations are available from a demonstrator for each
high-dimensional task, insufficient to estimate an accurate reward function.
Observing that each demonstrator has an inherent reward for each state and the
task-specific behaviors mainly depend on a small number of key states, we
propose a meta IRL algorithm that first models the reward function for each
task as a distribution conditioned on a baseline reward function shared by all
tasks and dependent only on the demonstrator, and then finds the most likely
reward function in the distribution that explains the task-specific behaviors.
We test the method in a simulated environment on path planning tasks with
limited demonstrations, and show that the accuracy of the learned reward
function is significantly improved. We also apply the method to analyze the
motion of a patient under rehabilitation.Comment: arXiv admin note: text overlap with arXiv:1707.0939
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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