2,134 research outputs found
A structured prediction approach for robot imitation learning
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework
Imitation Learning with Sinkhorn Distances
Imitation learning algorithms have been interpreted as variants of divergence
minimization problems. The ability to compare occupancy measures between
experts and learners is crucial in their effectiveness in learning from
demonstrations. In this paper, we present tractable solutions by formulating
imitation learning as minimization of the Sinkhorn distance between occupancy
measures. The formulation combines the valuable properties of optimal transport
metrics in comparing non-overlapping distributions with a cosine distance cost
defined in an adversarially learned feature space. This leads to a highly
discriminative critic network and optimal transport plan that subsequently
guide imitation learning. We evaluate the proposed approach using both the
reward metric and the Sinkhorn distance metric on a number of MuJoCo
experiments
A Structured Prediction Approach for Robot Imitation Learning
We propose a structured prediction approach for robot imitation learning from
demonstrations. Among various tools for robot imitation learning, supervised
learning has been observed to have a prominent role. Structured prediction is a
form of supervised learning that enables learning models to operate on output
spaces with complex structures. Through the lens of structured prediction, we
show how robots can learn to imitate trajectories belonging to not only
Euclidean spaces but also Riemannian manifolds. Exploiting ideas from
information theory, we propose a class of loss functions based on the
f-divergence to measure the information loss between the demonstrated and
reproduced probabilistic trajectories. Different types of f-divergence will
result in different policies, which we call imitation modes. Furthermore, our
approach enables the incorporation of spatial and temporal trajectory
modulation, which is necessary for robots to be adaptive to the change in
working conditions. We benchmark our algorithm against state-of-the-art methods
in terms of trajectory reproduction and adaptation. The quantitative evaluation
shows that our approach outperforms other algorithms regarding both accuracy
and efficiency. We also report real-world experimental results on learning
manifold trajectories in a polishing task with a KUKA LWR robot arm,
illustrating the effectiveness of our algorithmic framework
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