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Found 16 papers, 4 papers with code
Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses.
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text
In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops.
ObPose: Leveraging Pose for Object-Centric Scene Inference in 3D
We present ObPose, an unsupervised object-centric inference and generation model which learns 3D-structured latent representations from RGB-D scenes.
Group-level Brain Decoding with Deep Learning
Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models.
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains.
Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion
This encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles.
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations.
There and Back Again: Learning to Simulate Radar Data for Real-World Applications
Here we propose to learn a radar sensor model capable of synthesising faithful radar observations based on simulated elevation maps.
Reconstruction Bottlenecks in Object-Centric Generative Models
A range of methods with suitable inductive biases exist to learn interpretable object-centric representations of images without supervision.
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach.
Improving End-to-End Object Tracking Using Relational Reasoning
Relational reasoning, the ability to model interactions and relations between objects, is valuable for robust multi-object tracking and pivotal for trajectory prediction.
Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis
In this paper we explore the richness of information captured by the latent space of a vision-based generative model.
Imagine That! Leveraging Emergent Affordances for Tool Synthesis in Reaching Tasks
In this paper we investigate an artificial agent's ability to perform task-focused tool synthesis via imagination.
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning.
Ranked #1 on
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on Multi-dSprites
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning
The majority of contemporary object-tracking approaches do not model interactions between objects.
Recovering Missing Characters in Old Hawaiian Writing
In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops.
