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Robotics
Associated Publications
RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies
RMPflow: A Computational Graph for Automatic Motion Policy Generation
Robust Learning of Tactile Force Estimation through Robot Interaction
Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies
Joint Space Control via Deep Reinforcement Learning
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
Indirect Object-to-Robot Pose Estimation from an External Monocular RGB Camera
Automated Synthetic-to-Real Generalization
MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views
6-DOF Grasping for Target-driven Object Manipulation in Clutter
DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System
Toward Sim-to-Real Directional Semantic Grasping
Camera-to-Robot Pose Estimation from a Single Image
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
Fluidic Elastomer Actuators for Haptic Interactions in Virtual Reality
Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration
Simultaneous Edge Alignment and Learning
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
EOE: Expected Overlap Estimation over Unstructured Point Cloud Data
Geometry-Aware Learning of Maps for Camera Localization
Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations
A Variable Shape and Variable Stiffness Controller for Haptic Virtual Interactions
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Riemannian Motion Policies
Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control
Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness
Accelerated Generative Models for 3D Point Cloud Data
Researchers
Adithya Murali
Animesh Garg
Ankur Handa
Arsalan Mousavian
Balakumar Sundaralingam
Benjamin Eckart
Byron Boots
Chris Paxton
Clemens Eppner
De-An Huang
Dieter Fox
Fabio Ramos
Iuri Frosio
Jeff Smith
Jonathan Tremblay
Karl Van Wyk
Lucas Manuelli
Nathan Ratliff
Rowland O'Flaherty
Stan Birchfield
Stephen Tyree
Tucker Hermans
Wei Yang
Wonmin Byeon
Yashraj Narang
Yu Xiang
Yu-Wei Chao
Yuke Zhu
Zhiding Yu
