Search Results for author: C. Karen Liu

Found 43 papers, 20 papers with code

DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics

no code implementations24 Sep 2023 Yifeng Jiang, Jungdam Won, Yuting Ye, C. Karen Liu

We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics.

Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation

no code implementations2 Sep 2023 Yuanpei Chen, Chen Wang, Li Fei-Fei, C. Karen Liu

However, the challenges arise due to the high-dimensional action space of dexterous hand and complex compositional dynamics of the long-horizon tasks.

Reinforcement Learning (RL)

Diffusion Inertial Poser: Human Motion Reconstruction from Arbitrary Sparse IMU Configurations

no code implementations31 Aug 2023 Tom Van Wouwe, Seunghwan Lee, Antoine Falisse, Scott Delp, C. Karen Liu

We show that DiffIP has the benefit of flexibility with respect to the IMU configuration while being as accurate as the state-of-the-art for the commonly used six IMU configuration.

CIRCLE: Capture In Rich Contextual Environments

1 code implementation CVPR 2023 Joao Pedro Araujo, Jiaman Li, Karthik Vetrivel, Rishi Agarwal, Deepak Gopinath, Jiajun Wu, Alexander Clegg, C. Karen Liu

Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes.

Scene Synthesis from Human Motion

no code implementations4 Jan 2023 Sifan Ye, Yixing Wang, Jiaman Li, Dennis Park, C. Karen Liu, Huazhe Xu, Jiajun Wu

Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly.

NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action

1 code implementation28 Dec 2022 Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, Joao Pedro Araujo, Jeffrey Gu, C. Karen Liu, Serena Yeung

Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.

3D Reconstruction Human Mesh Recovery +1

Physically Plausible Animation of Human Upper Body from a Single Image

no code implementations9 Dec 2022 Ziyuan Huang, Zhengping Zhou, Yung-Yu Chuang, Jiajun Wu, C. Karen Liu

We present a new method for generating controllable, dynamically responsive, and photorealistic human animations.

Ego-Body Pose Estimation via Ego-Head Pose Estimation

no code implementations CVPR 2023 Jiaman Li, C. Karen Liu, Jiajun Wu

In addition, collecting large-scale, high-quality datasets with paired egocentric videos and 3D human motions requires accurate motion capture devices, which often limit the variety of scenes in the videos to lab-like environments.

Benchmarking Disentanglement +1

EDGE: Editable Dance Generation From Music

1 code implementation CVPR 2023 Jonathan Tseng, Rodrigo Castellon, C. Karen Liu

Dance is an important human art form, but creating new dances can be difficult and time-consuming.

Motion Synthesis

GIMO: Gaze-Informed Human Motion Prediction in Context

1 code implementation20 Apr 2022 Yang Zheng, Yanchao Yang, Kaichun Mo, Jiaman Li, Tao Yu, Yebin Liu, C. Karen Liu, Leonidas J. Guibas

We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures.

Human motion prediction motion prediction

Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation

1 code implementation29 Mar 2022 Yifeng Jiang, Yuting Ye, Deepak Gopinath, Jungdam Won, Alexander W. Winkler, C. Karen Liu

Real-time human motion reconstruction from a sparse set of (e. g. six) wearable IMUs provides a non-intrusive and economic approach to motion capture.

Motion Estimation

A Survey on Reinforcement Learning Methods in Character Animation

no code implementations7 Mar 2022 Ariel Kwiatkowski, Eduardo Alvarado, Vicky Kalogeiton, C. Karen Liu, Julien Pettré, Michiel Van de Panne, Marie-Paule Cani

Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment.

reinforcement-learning Reinforcement Learning (RL)

ADeLA: Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation

no code implementations CVPR 2022 Hanxiang Ren, Yanchao Yang, He Wang, Bokui Shen, Qingnan Fan, Youyi Zheng, C. Karen Liu, Leonidas J. Guibas

We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available, but the annotations may only be abundant for a few typical views.

Semantic Segmentation Unsupervised Domain Adaptation

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

1 code implementation13 Aug 2021 Chen Wang, Claudia Pérez-D'Arpino, Danfei Xu, Li Fei-Fei, C. Karen Liu, Silvio Savarese

Our method co-optimizes a human policy and a robot policy in an interactive learning process: the human policy learns to generate diverse and plausible collaborative behaviors from demonstrations while the robot policy learns to assist by estimating the unobserved latent strategy of its human collaborator.

BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments

no code implementations6 Aug 2021 Sanjana Srivastava, Chengshu Li, Michael Lingelbach, Roberto Martín-Martín, Fei Xia, Kent Vainio, Zheng Lian, Cem Gokmen, Shyamal Buch, C. Karen Liu, Silvio Savarese, Hyowon Gweon, Jiajun Wu, Li Fei-Fei

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation.

iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks

1 code implementation6 Aug 2021 Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, Andrey Kurenkov, C. Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, Silvio Savarese

We evaluate the new capabilities of iGibson 2. 0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI.

Imitation Learning

ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

1 code implementation29 Jul 2021 Yanchao Yang, Hanxiang Ren, He Wang, Bokui Shen, Qingnan Fan, Youyi Zheng, C. Karen Liu, Leonidas Guibas

Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels in the target domain.

Inductive Bias Semantic Segmentation +1

DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis

2 code implementations27 Jul 2021 Yuefan Shen, Yanchao Yang, Youyi Zheng, C. Karen Liu, Leonidas Guibas

We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth.

Contrastive Learning Image Generation

Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact

1 code implementation30 Mar 2021 Keenon Werling, Dalton Omens, Jeongseok Lee, Ioannis Exarchos, C. Karen Liu

We present a fast and feature-complete differentiable physics engine, Nimble (nimblephysics. org), that supports Lagrangian dynamics and hard contact constraints for articulated rigid body simulation.

Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments

no code implementations13 Mar 2021 Visak Kumar, Sehoon Ha, C. Karen Liu

An EAP takes as input the predicted future state error in the target environment, which is provided by an error-prediction function, simultaneously trained with the EAP.

Task-Specific Design Optimization and Fabrication for Inflated-Beam Soft Robots with Growable Discrete Joints

1 code implementation8 Mar 2021 Ioannis Exarchos, Brian H. Do, Fabio Stroppa, Margaret M. Coad, Allison M. Okamura, C. Karen Liu

Soft robot serial chain manipulators with the capability for growth, stiffness control, and discrete joints have the potential to approach the dexterity of traditional robot arms, while improving safety, lowering cost, and providing an increased workspace, with potential application in home environments.


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.

Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop

no code implementations7 Dec 2020 Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Florian Golemo, Melissa Mozifian, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, Martha White

This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.

COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing

no code implementations23 Nov 2020 Zhuo Xu, Wenhao Yu, Alexander Herzog, Wenlong Lu, Chuyuan Fu, Masayoshi Tomizuka, Yunfei Bai, C. Karen Liu, Daniel Ho

General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.

Reinforcement Learning (RL) Robot Manipulation

Policy Transfer via Kinematic Domain Randomization and Adaptation

1 code implementation3 Nov 2020 Ioannis Exarchos, Yifeng Jiang, Wenhao Yu, C. Karen Liu

Transferring reinforcement learning policies trained in physics simulation to the real hardware remains a challenge, known as the "sim-to-real" gap.

Bayesian Optimization Domain Adaptation

Learning Task-Agnostic Action Spaces for Movement Optimization

1 code implementation22 Sep 2020 Amin Babadi, Michiel Van de Panne, C. Karen Liu, Perttu Hämäläinen

We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier.

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

Assistive Gym: A Physics Simulation Framework for Assistive Robotics

3 code implementations10 Oct 2019 Zackory Erickson, Vamsee Gangaram, Ariel Kapusta, C. Karen Liu, Charles C. Kemp

Assistive Gym models a person's physical capabilities and preferences for assistance, which are used to provide a reward function.

Visualizing Movement Control Optimization Landscapes

no code implementations17 Sep 2019 Perttu Hämäläinen, Juuso Toikka, Amin Babadi, C. Karen Liu

A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters.

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.

Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation

no code implementations9 Jul 2019 K. Niranjan Kumar, Irfan Essa, Sehoon Ha, C. Karen Liu

Using our method, we train a robotic arm to estimate the mass distribution of an object with moving parts (e. g. an articulated rigid body system) by pushing it on a surface with unknown friction properties.


Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation

2 code implementations30 Apr 2019 Yifeng Jiang, Tom Van Wouwe, Friedl De Groote, C. Karen Liu

In addition, the metabolic energy function on muscle activations is transformed to a nonlinear function of joint torques, joint configuration and joint velocity.

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.

Bayesian Optimization Friction

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

Data-Augmented Contact Model for Rigid Body Simulation

no code implementations11 Mar 2018 Yifeng Jiang, Jiazheng Sun, C. Karen Liu

Accurately modeling contact behaviors for real-world, near-rigid materials remains a grand challenge for existing rigid-body physics simulators.

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.

Multi-Task Learning

Learning a Unified Control Policy for Safe Falling

no code implementations8 Mar 2017 Visak CV Kumar, Sehoon Ha, C. Karen Liu

With this mixture of actor-critic architecture, the discrete contact sequence planning is solved through the selection of the best critics while the continuous control problem is solved by the optimization of actors.

Continuous Control

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.


A Linear-Time Variational Integrator for Multibody Systems

1 code implementation9 Sep 2016 Jeongseok Lee, C. Karen Liu, Frank C. Park, Siddhartha S. Srinivasa

Our key contribution is to derive a recursive algorithm that evaluates DEL equations in $O(n)$, which scales up well for complex multibody systems such as humanoid robots.


Traversing Environments Using Possibility Graphs for Humanoid Robots

no code implementations12 Aug 2016 Michael X. Grey, Aaron D. Ames, C. Karen Liu

Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments.

Motion Planning

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