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Found 14 papers, 1 papers with code
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments.
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents.
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields
A simulation is then created using the rendering engine in a physics simulator which computes contact dynamics from the static scene geometry (estimated from the NeRF volume density) and the dynamic objects' geometry and physical properties (assumed known).
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots.
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model.
DIViS: Domain Invariant Visual Servoing for Collision-Free Goal Reaching
However, DIViS can directly be deployed on a real robot and is capable of servoing to the user-specified object categories while avoiding collisions in the real world.
Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control
In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback.
Sim2Real View Invariant Visual Servoing by Recurrent Control
To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object.
CAD2RL: Real Single-Image Flight without a Single Real Image
We propose a learning method that we call CAD$^2$RL, which can be used to perform collision-free indoor flight in the real world while being trained entirely on 3D CAD models.
VISALOGY: Answering Visual Analogy Questions
In this paper, we study the problem of answering visual analogy questions.
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
Next, we show that the association of high-quality segmentations to textual phrases aids in richer semantic understanding and reasoning of these textual phrases.
VisKE: Visual Knowledge Extraction and Question Answering by Visual Verification of Relation Phrases
How can we know whether a statement about our world is valid.
Incorporating Scene Context and Object Layout into Appearance Modeling
In this paper, we propose a method to learn scene structures that can encode three main interlacing components of a scene: the scene category, the context-specific appearance of objects, and their layout.
Probabilistic Label Trees for Efficient Large Scale Image Classification
Large-scale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows.
