Search Results for author: Greg Turk

Found 13 papers, 5 papers with code

Robot Learning from Randomized Simulations: A Review

no code implementations1 Nov 2021 Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters

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

1 code implementation20 May 2021 Henry M. Clever, Patrick Grady, Greg Turk, Charles C. Kemp

We present a method that infers contact pressure between a human body and a mattress from a depth image.

Image Generation

Learning to Manipulate Amorphous Materials

no code implementations3 Mar 2021 Yunbo Zhang, Wenhao Yu, C. Karen Liu, Charles C. Kemp, Greg Turk

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

no code implementations11 Dec 2020 Wenhao Yu, C. Karen Liu, Greg Turk

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

1 code implementation CVPR 2020 Henry M. Clever, Zackory Erickson, Ariel Kapusta, Greg Turk, C. Karen Liu, Charles C. Kemp

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

no code implementations14 Sep 2019 Alexander Clegg, Zackory Erickson, Patrick Grady, Greg Turk, Charles C. Kemp, C. Karen Liu

We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing.

Learning Novel Policies For Tasks

no code implementations13 May 2019 Yunbo Zhang, Wenhao Yu, Greg Turk

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.

Policy Gradient Methods

Sim-to-Real Transfer for Biped Locomotion

no code implementations4 Mar 2019 Wenhao Yu, Visak CV Kumar, Greg Turk, C. Karen Liu

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

1 code implementation ICLR 2019 Wenhao Yu, C. Karen Liu, Greg Turk

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.

Transfer Learning

Learning Symmetric and Low-energy Locomotion

2 code implementations24 Jan 2018 Wenhao Yu, Greg Turk, C. Karen Liu

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

no code implementations27 Sep 2017 Zackory Erickson, Henry M. Clever, Greg Turk, C. Karen Liu, Charles C. Kemp

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.

Common Sense Reasoning

Multi-task Learning with Gradient Guided Policy Specialization

no code implementations23 Sep 2017 Wenhao Yu, C. Karen Liu, Greg Turk

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.

Legged Robots Multi-Task Learning

Preparing for the Unknown: Learning a Universal Policy with Online System Identification

1 code implementation8 Feb 2017 Wenhao Yu, Jie Tan, C. Karen Liu, Greg Turk

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.

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