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Found 9 papers, 2 papers with code
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints
Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts.
Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph
We use a coarse graph, derived from a dense graph, to estimate the human's 3D pose, and the dense graph to estimate the 3D shape.
Ranked #251 on
3D Human Pose Estimation
on Human3.6M
Lyapunov Robust Constrained-MDPs: Soft-Constrained Robustly Stable Policy Optimization under Model Uncertainty
Safety and robustness are two desired properties for any reinforcement learning algorithm.
Cross-domain Imitation from Observations
Once this correspondence is found, we can directly transfer the demonstrations on one domain to the other and use it for imitation.
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon.
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty
In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties.
Learning from Trajectories via Subgoal Discovery
Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples.
Trajectory-based Learning for Ball-in-Maze Games
We show that in spite of not using human-generated trajectories and just using the simulator as a model to generate a limited number of trajectories, we can get a speed-up of about 2-3x in the learning process.
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics
Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task---rather than being productive---can be reduced by transferring the learned task to the real robot.
