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Found 8 papers, 4 papers with code
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
In conclusion, VidEgoThink reflects a research trend towards employing MLLMs for egocentric vision, akin to human capabilities, enabling active observation and interaction in the complex real-world environments.
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Knowledge from animals and humans inspires robotic innovations.
NEURAL MARIONETTE: A Transformer-based Multi-action Human Motion Synthesis System
Along with the novel system, we also present a new dataset dedicated to the multi-action motion synthesis task, which contains both action tags and their contextual information.
Online Motion Style Transfer for Interactive Character Control
Motion style transfer is highly desired for motion generation systems for gaming.
Learning Hierarchical Control for Robust In-Hand Manipulation
Finally, we show how our approach generalizes to objects of other shapes.
Learning to Solve a Rubik's Cube with a Dexterous Hand
We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand.
Robotics
HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots
As one of the most promising areas, mobile robots draw much attention these years.
Robotics
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration
Our architecture has two key components: options, represented by existing human-designed methods, can significantly speed up the training process and interruption mechanism, based on learnable termination functions, enables our system to quickly respond to the external environment.
