3,779 research outputs found
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
Multi-Task Policy Search for Robotics
© 2014 IEEE.Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in realrobot experiments are shown
Coordinated Multi-Agent Imitation Learning
We study the problem of imitation learning from demonstrations of multiple
coordinating agents. One key challenge in this setting is that learning a good
model of coordination can be difficult, since coordination is often implicit in
the demonstrations and must be inferred as a latent variable. We propose a
joint approach that simultaneously learns a latent coordination model along
with the individual policies. In particular, our method integrates unsupervised
structure learning with conventional imitation learning. We illustrate the
power of our approach on a difficult problem of learning multiple policies for
fine-grained behavior modeling in team sports, where different players occupy
different roles in the coordinated team strategy. We show that having a
coordination model to infer the roles of players yields substantially improved
imitation loss compared to conventional baselines.Comment: International Conference on Machine Learning 201
Probabilistic movement modeling for intention inference in human-robot interaction.
Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.
Neural probabilistic motor primitives for humanoid control
We focus on the problem of learning a single motor module that can flexibly
express a range of behaviors for the control of high-dimensional physically
simulated humanoids. To do this, we propose a motor architecture that has the
general structure of an inverse model with a latent-variable bottleneck. We
show that it is possible to train this model entirely offline to compress
thousands of expert policies and learn a motor primitive embedding space. The
trained neural probabilistic motor primitive system can perform one-shot
imitation of whole-body humanoid behaviors, robustly mimicking unseen
trajectories. Additionally, we demonstrate that it is also straightforward to
train controllers to reuse the learned motor primitive space to solve tasks,
and the resulting movements are relatively naturalistic. To support the
training of our model, we compare two approaches for offline policy cloning,
including an experience efficient method which we call linear feedback policy
cloning. We encourage readers to view a supplementary video (
https://youtu.be/CaDEf-QcKwA ) summarizing our results.Comment: Accepted as a conference paper at ICLR 201
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