2,280 research outputs found
Imitative Planning using Conditional Normalizing Flow
We explore the application of normalizing flows for improving the performance
of trajectory planning for autonomous vehicles (AVs). Normalizing flows provide
an invertible mapping from a known prior distribution to a potentially complex,
multi-modal target distribution and allow for fast sampling with exact PDF
inference. By modeling a trajectory planner's cost manifold as an energy
function we learn a scene conditioned mapping from the prior to a Boltzmann
distribution over the AV control space. This mapping allows for control samples
and their associated energy to be generated jointly and in parallel. We propose
using neural autoregressive flow (NAF) as part of an end-to-end deep learned
system that allows for utilizing sensors, map, and route information to
condition the flow mapping. Finally, we demonstrate the effectiveness of our
approach on real world datasets over IL and hand constructed trajectory
sampling techniques.Comment: Submittted to 4th Conference on Robot Learning (CoRL 2020), Cambridge
MA, US
An active inference model of hierarchical action understanding, learning and imitation
We advance a novel active inference model of the cognitive processing that underlies the acquisition of a hierarchical action repertoire and its use for observation, understanding and imitation. We illustrate the model in four simulations of a tennis learner who observes a teacher performing tennis shots, forms hierarchical representations of the observed actions, and imitates them. Our simulations show that the agent's oculomotor activity implements an active information sampling strategy that permits inferring the kinematic aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions: proximal goals and intentions. Finally, the inferred action representations can steer imitative responses, but interfere with the execution of different actions. Our simulations show that hierarchical active inference provides a unified account of action observation, understanding, learning and imitation and helps explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization
Reliable uncertainty quantification in deep neural networks is very crucial
in safety-critical applications such as automated driving for trustworthy and
informed decision-making. Assessing the quality of uncertainty estimates is
challenging as ground truth for uncertainty estimates is not available.
Ideally, in a well-calibrated model, uncertainty estimates should perfectly
correlate with model error. We propose a novel error aligned uncertainty
optimization method and introduce a trainable loss function to guide the models
to yield good quality uncertainty estimates aligning with the model error. Our
approach targets continuous structured prediction and regression tasks, and is
evaluated on multiple datasets including a large-scale vehicle motion
prediction task involving real-world distributional shifts. We demonstrate that
our method improves average displacement error by 1.69% and 4.69%, and the
uncertainty correlation with model error by 17.22% and 19.13% as quantified by
Pearson correlation coefficient on two state-of-the-art baselines.Comment: Accepted to ECCV 2022 workshop - Safe Artificial Intelligence for
Automated Drivin
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
Get Back Here: Robust Imitation by Return-to-Distribution Planning
We consider the Imitation Learning (IL) setup where expert data are not
collected on the actual deployment environment but on a different version. To
address the resulting distribution shift, we combine behavior cloning (BC) with
a planner that is tasked to bring the agent back to states visited by the
expert whenever the agent deviates from the demonstration distribution. The
resulting algorithm, POIR, can be trained offline, and leverages online
interactions to efficiently fine-tune its planner to improve performance over
time. We test POIR on a variety of human-generated manipulation demonstrations
in a realistic robotic manipulation simulator and show robustness of the
learned policy to different initial state distributions and noisy dynamics
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