Papers
959 papers found
Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation
Peter R. Florence, Lucas Manuelli, Russ Tedrake
Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment
Fabio Muratore, Felix Treede, Michael Gienger et al.
Driving Policy Transfer via Modularity and Abstraction
Matthias Mueller, Alexey Dosovitskiy, Bernard Ghanem et al.
Dyadic collaborative Manipulation through Hybrid Trajectory Optimization
Theodoros Stouraitis, Iordanis Chatzinikolaidis, Michael Gienger et al.
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Ajinkya Jain, Scott Niekum
Energy-Based Hindsight Experience Prioritization
Rui Zhao, Volker Tresp
ESIM: an Open Event Camera Simulator
Henri Rebecq, Daniel Gehrig, Davide Scaramuzza
Expanding Motor Skills using Relay Networks
Visak CV Kumar, Sehoon Ha, C.Karen Liu
Fast 3D Modeling with Approximated Convolutional Kernels
Vitor Guizilini, Fabio Ramos
Feature Learning for Scene Flow Estimation from LIDAR
Arash K. Ushani, Ryan M. Eustice
Few-Shot Goal Inference for Visuomotor Learning and Planning
Annie Xie, Avi Singh, Sergey Levine et al.
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
Rika Antonova, Mia Kokic, Johannes A. Stork et al.
GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning
Jacky Liang, Viktor Makoviychuk, Ankur Handa et al.
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
Eric Jang, Coline Devin, Vincent Vanhoucke et al.
Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge
Daniel Nyga, Subhro Roy, Rohan Paul et al.
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents
Haonan Yu, Xiaochen Lian, Haichao Zhang et al.
HDNET: Exploiting HD Maps for 3D Object Detection
Bin Yang, Ming Liang, Raquel Urtasun
HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems
Yun Long, Xueyuan She, Saibal Mukhopadhyay
Including Uncertainty when Learning from Human Corrections
Dylan P. Losey, Marcia K. O’Malley
Inferring geometric constraints in human demonstrations
Guru Subramani, Michael Zinn, Michael Gleicher
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
Yanfu Zhang, Wenshan Wang, Rogerio Bonatti et al.
IntentNet: Learning to Predict Intention from Raw Sensor Data
Sergio Casas, Wenjie Luo, Raquel Urtasun
Interpretable Latent Spaces for Learning from Demonstration
Yordan Hristov, Alex Lascarides, Subramanian Ramamoorthy
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Fan Wang, Bo Zhou, Ke Chen et al.
Learning 6-DoF Grasping and Pick-Place Using Attention Focus
Marcus Gualtieri, Robert Platt