Competition Introduction
With the rapid development of the embodied intelligence sector, intellegent algorithms related to Imitation Learning (IL) , Reinforcement Learning (RL), and large-scale decision-making models have already achieved substantial advances in theoretical foundations. However, the push of evolution of algorithms towards practical usability and high robustness still requires continuous real-world interaction and feedback processes, and such process is faced with several resource-straining barriers:
High cost of entry: High-performance robotics hardware still carry an astronomically expensive price tag, ranging from hundreds of thousands to even millions of Yuan.
High dataset production costs: High-quality datasets require complex synchronisation systems and manual annotations on specialised facilities, which incurs high costs and long development cycles
Lack of a standardised testing platform: There lacks a standardised testing platform, which greatly increases the difficulty of comparing algorithms in a fair manner under real-world conditions.
Being faced with these bottlenecks, this brand-new competition promises the following:
First-in-world to release large datasets collected from our fleet of real twin-armed humanoid robots from specialised facilities
Fully open-sourcing an evaluation platform with real-world robots, along with support for participant’s remote deployment and evaluation of their algorithms
Provides various self-testing systems developed in-house, with standardised interfaces for all the tasks, as well as a full technical support community.
Provides hundreds of thousands of Yuan in compensation for both our main track and specialty tracks
Our goal is to lower the technical barrier of entry, so that every participant can efficiently access their required resources, have a quick onboarding experience, and have easy access to algorithm validations.
