Search Results for author:
Found 17 papers, 7 papers with code
Annotated Hands for Generative Models
These annotations provide additional structure that coax the generative model to produce higher quality hand images.
Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism
To build a successful guide robot, our paper explores three key topics: (1) formalizing the navigation mechanism of a guide dog and a human, (2) developing a data-driven model of their interaction, and (3) improving user safety.
Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum
In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects.
Auditing Gender Presentation Differences in Text-to-Image Models
Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools.
Robot Learning from Randomized Simulations: A Review
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data.
BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image
We present a method that infers contact pressure between a human body and a mattress from a depth image.
Learning to Manipulate Amorphous Materials
We produce a final animation by using inverse kinematics to guide a character's arm and hand to match the motion of the manipulation tool such as a knife or a frying pan.
Protective Policy Transfer
When used with a set of thresholds, the safety estimator becomes a classifier for switching between the protective policy and the task policy.
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes.
3D human pose and shape estimation
3D Human Shape Estimation
+1
Learning to Collaborate from Simulation for Robot-Assisted Dressing
We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing.
Learning Novel Policies For Tasks
Our method does this by creating a second reward function that recognizes previously seen state sequences and rewards those by novelty, which is measured using autoencoders that have been trained on state sequences from previously discovered policies.
Sim-to-Real Transfer for Biped Locomotion
We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware.
Policy Transfer with Strategy Optimization
Transfer learning using domain randomization is a promising approach, but it usually assumes that the target environment is close to the distribution of the training environments, thus relying heavily on accurate system identification.
Learning Symmetric and Low-energy Locomotion
Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function.
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body.
Multi-task Learning with Gradient Guided Policy Specialization
Then, during the specialization training stage we selectively split the weights of the policy based on a per-weight metric that measures the disagreement among the multiple tasks.
Preparing for the Unknown: Learning a Universal Policy with Online System Identification
Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment.
