Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …

Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General
Intelligence (AGI) and serves as a foundation for various applications that bridge cyberspace …

Biological systems understand the world by simultaneously processing high-dimensional
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …

The ability to associate touch with other modalities has huge implications for humans and
computational systems. However multimodal learning with touch remains challenging due to …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

We propose a sim-to-real framework for dexterous manipulation which can generalize to
new objects of the same category in the real world. The key of our framework is to train the …

For robots to be useful outside labs and specialized factories we need a way to teach them
new useful behaviors quickly. Current approaches lack either the generality to onboard new …

With the rapid development of machine learning, its powerful function in the machine vision
field is increasingly reflected. The combination of machine vision and robotics to achieve the …

We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL)
with a Transformer-based model that learns to combine proprioceptive information and high …

A key challenge in learning task and motion planning in open domains is how to acquire
diverse and generalizable skills for robots. Recent research in one-shot imitation learning …