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Found 12 papers, 0 papers with code
Real-Time Simulated Avatar from Head-Mounted Sensors
We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets.
RoHM: Robust Human Motion Reconstruction via Diffusion
We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling.
Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation
Model Predictive Control (MPC) provides an optimal control solution based on a cost function while allowing for the implementation of process constraints.
Universal Humanoid Motion Representations for Physics-Based Control
We close this gap by significantly increasing the coverage of our motion representation space.
Physics-based Motion Retargeting from Sparse Inputs
We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies.
QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors
Most existing methods for motion tracking avoid environment interaction apart from foot-floor contact due to their complex dynamics and hard constraints.
Perpetual Humanoid Control for Real-time Simulated Avatars
We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e. g. pose estimates from video or generated from language) and unexpected falls.
QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars
Real-time tracking of human body motion is crucial for interactive and immersive experiences in AR/VR.
Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines
One solution is the use of machine learning (ML) and model predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller.
Deep Learning based Model Predictive Control for Compression Ignition Engines
Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine.
On-the-fly Augmented Reality for Orthopaedic Surgery Using a Multi-Modal Fiducial
Then, annotations on the 2D X-ray images can be rendered as virtual objects in 3D providing surgical guidance.
Optimal and Learning Control for Autonomous Robots
Optimal and Learning Control for Autonomous Robots has been taught in the Robotics, Systems and Controls Masters at ETH Zurich with the aim to teach optimal control and reinforcement learning for closed loop control problems from a unified point of view.
