Robot System and Mobility

 

Motion Generation

  • Symmetry-aware Policy Network

    We propose a symmetry-assisted, general-purpose DRL framework for morphologically symmetric robots that enables stable and robust learning. The framework models the environment as a symmetric Markov decision process (MDP) and constructs a full-body policy from a single-sided base policy using symmetry operators. We further propose a symmetric PPO objective with a coupled importance-sampling ratio. This objective aligns the policy optimization process with the imposed symmetry and serves as a principled alternative to MAPPO-style multi-agent formulations.

 

SLAM and Navigation

Sensor

 

Image processing

 

Depth Estimation

Teleoperation

City modeling