How to train your robot with deep reinforcement learning: lessons we have learned
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Revisiting self-supervised visual representation learning
Unsupervised visual representation learning remains a largely unsolved problem in
computer vision research. Among a big body of recently proposed approaches for …
computer vision research. Among a big body of recently proposed approaches for …
Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation
Despite decades of research, general purpose in-hand manipulation remains one of the
unsolved challenges of robotics. One of the contributing factors that limit current robotic …
unsolved challenges of robotics. One of the contributing factors that limit current robotic …
Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks
General-purpose robots coexisting with humans in their environment must learn to relate
human language to their perceptions and actions to be useful in a range of daily tasks …
human language to their perceptions and actions to be useful in a range of daily tasks …
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …
conclude three key tasks during vision-based robotic grasping, which are object localization …
[HTML][HTML] Multibench: Multiscale benchmarks for multimodal representation learning
Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …
Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach
CC Beltran-Hernandez, D Petit, IG Ramirez-Alpizar… - Applied Sciences, 2020 - mdpi.com
Featured Application Assembly tasks with industrial robot manipulators. Abstract Industrial
robot manipulators are playing a significant role in modern manufacturing industries …
robot manipulators are playing a significant role in modern manufacturing industries …
A review of robot learning for manipulation: Challenges, representations, and algorithms
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …
interacting with the world around them to achieve their goals. The last decade has seen …
Incomplete multimodality-diffused emotion recognition
Human multimodal emotion recognition (MER) aims to perceive and understand human
emotions via various heterogeneous modalities, such as language, vision, and acoustic …
emotions via various heterogeneous modalities, such as language, vision, and acoustic …