67,984 research outputs found
High Level Learning Using the Temporal Features of Human Demonstrated Sequential Tasks
Modelling human-led demonstrations of high-level sequential tasks is fundamental to a number of practical inference applications including vision-based policy learning and activity recognition. Demonstrations of these tasks are captured as videos with long durations and similar spatial contents. Learning from this data is challenging since inference cannot be conducted solely on spatial feature presence and must instead consider how spatial features play out across time. To be successful these temporal representations must generalize to variations in the duration of activities and be able to capture relationships between events expressed across the scale of an entire video.
Contemporary deep learning architectures that represent time (convolution-based and Recurrent Neural Networks) do not address these concerns. Representations learned by these models describe temporal features in terms of fixed durations such as minutes, seconds, and frames. They are also developed sequentially and must use unreasonably large models to capture temporal features expressed at scale. Probabilistic temporal models have been successful in representing the temporal information of videos in a duration invariant manner that is robust to scale, however, this has only been accomplished through the use of user-defined spatial features. Such abstractions make unrealistic assumptions about the content being expressed in these videos, the quality of the perception model, and they also limit the potential applications of trained models. To that end, I present D-ITR-L, a temporal wrapper that extends the spatial features extracted from a typically CNN architecture and transforms them into temporal features.
D-ITR-L-derived temporal features are duration invariant and can identify temporal relationships between events at the scale of a full video. Validation of this claim is conducted through various vision-based policy learning and action recognition settings. Additionally, these studies show that challenging visual domains such as human-led demonstration of high-level sequential tasks can be effectively represented when using a D-ITR-L-based model
High Level Learning Using the Temporal Features of Human Demonstrated Sequential Tasks
Modelling human-led demonstrations of high-level sequential tasks is fundamental to a number of practical inference applications including vision-based policy learning and activity recognition. Demonstrations of these tasks are captured as videos with long durations and similar spatial contents. Learning from this data is challenging since inference cannot be conducted solely on spatial feature presence and must instead consider how spatial features play out across time. To be successful these temporal representations must generalize to variations in the duration of activities and be able to capture relationships between events expressed across the scale of an entire video.
Contemporary deep learning architectures that represent time (convolution-based and Recurrent Neural Networks) do not address these concerns. Representations learned by these models describe temporal features in terms of fixed durations such as minutes, seconds, and frames. They are also developed sequentially and must use unreasonably large models to capture temporal features expressed at scale. Probabilistic temporal models have been successful in representing the temporal information of videos in a duration invariant manner that is robust to scale, however, this has only been accomplished through the use of user-defined spatial features. Such abstractions make unrealistic assumptions about the content being expressed in these videos, the quality of the perception model, and they also limit the potential applications of trained models. To that end, I present D-ITR-L, a temporal wrapper that extends the spatial features extracted from a typically CNN architecture and transforms them into temporal features.
D-ITR-L-derived temporal features are duration invariant and can identify temporal relationships between events at the scale of a full video. Validation of this claim is conducted through various vision-based policy learning and action recognition settings. Additionally, these studies show that challenging visual domains such as human-led demonstration of high-level sequential tasks can be effectively represented when using a D-ITR-L-based model
Deep Object-Centric Representations for Generalizable Robot Learning
Robotic manipulation in complex open-world scenarios requires both reliable
physical manipulation skills and effective and generalizable perception. In
this paper, we propose a method where general purpose pretrained visual models
serve as an object-centric prior for the perception system of a learned policy.
We devise an object-level attentional mechanism that can be used to determine
relevant objects from a few trajectories or demonstrations, and then
immediately incorporate those objects into a learned policy. A task-independent
meta-attention locates possible objects in the scene, and a task-specific
attention identifies which objects are predictive of the trajectories. The
scope of the task-specific attention is easily adjusted by showing
demonstrations with distractor objects or with diverse relevant objects. Our
results indicate that this approach exhibits good generalization across object
instances using very few samples, and can be used to learn a variety of
manipulation tasks using reinforcement learning
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments
Deriving robust control policies for realistic urban navigation scenarios is
not a trivial task. In an end-to-end approach, these policies must map
high-dimensional images from the vehicle's cameras to low-level actions such as
steering and throttle. While pure Reinforcement Learning (RL) approaches are
based exclusively on rewards,Generative Adversarial Imitation Learning (GAIL)
agents learn from expert demonstrations while interacting with the environment,
which favors GAIL on tasks for which a reward signal is difficult to derive. In
this work, the hGAIL architecture was proposed to solve the autonomous
navigation of a vehicle in an end-to-end approach, mapping sensory perceptions
directly to low-level actions, while simultaneously learning mid-level input
representations of the agent's environment. The proposed hGAIL consists of an
hierarchical Adversarial Imitation Learning architecture composed of two main
modules: the GAN (Generative Adversarial Nets) which generates the Bird's-Eye
View (BEV) representation mainly from the images of three frontal cameras of
the vehicle, and the GAIL which learns to control the vehicle based mainly on
the BEV predictions from the GAN as input.Our experiments have shown that GAIL
exclusively from cameras (without BEV) fails to even learn the task, while
hGAIL, after training, was able to autonomously navigate successfully in all
intersections of the city
- …