End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled
bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a …

Many solutions to address the challenge of robot learning have been devised, namely
through exploring novel ways for humans to communicate complex goals and tasks in …

The varying significance of distinct primitive behaviors during the policy learning process
has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore …

The advancement of robots, particularly those functioning in complex human-centric
environments, relies on control solutions that are driven by machine learning …

Despite the impressive performance of Reinforcement Learning (RL), the black-box neural
network backbone hinders users from trusting and deploying trained agents in real-world …

The advancement of robots, particularly those functioning in complex human-centric
environments, relies on control solutions that are driven by machine learning …

In this paper, we propose a novel framework for synthesizing a single multimodal control
policy capable of generating diverse behaviors (or modes) and emergent inherent transition …

Vehicle-to-Everything communications-assisted Autonomous Driving (V2X-AD) has
witnessed remarkable advancements in recent years, with pragmatic communications …

Autonomous robots have the potential to supplement human capabilities while reducing
cognitive and physical burden. However, deploying such systems in natural settings is …