What is Psi R0
Psi R0 is the first end-to-end embodied model based on reinforcement learning released by Lingchu Intelligence. It supports coordinated complex operations with dual dexterous hands, can mix-train multiple skills in sequence, generates an intelligent agent with reasoning capabilities, and completes and closes the loop on long-horizon dexterous operation tasks. Psi R0 can achieve cross-object and cross-scene generalization, with strong generalization ability and high robustness.
Main Functions of Psi R0
- Coordinated Dual Dexterous Hand Operation: Psi R0 supports coordinated complex operations with dual dexterous hands and can complete multi-step long-horizon dexterous tasks.
- Multi-Skill Sequential Mixed Training: The model can mix-train multiple skills in sequence, generate an intelligent agent with reasoning capabilities, and complete and close the loop on long-horizon dexterous operation tasks.
- Cross-Object and Cross-Scene Generalization: Psi R0 can achieve cross-object and cross-scene level generalization, with strong generalization ability and high robustness.
- Training Based on Simulation Data: Psi R0 uses massive simulation data to train a dual-hand operation intelligent agent, connects multiple skills through a bidirectional training framework, and is the first in the industry to complete long-horizon tasks in open environments.
- Solving the Difficulty of Reward Function Design: This skill training framework abstracts key information from the spatiotemporal trajectories of objects to construct a general objective function, solving the problem of difficult reward function design.
- Post-Training Stage Optimization: In the post-training stage, a small amount of high-quality real-machine data is used for alignment to further improve the success rate of long-horizon tasks.
- Autonomous Skill Switching Capability: The transition feasibility function in the bidirectional training framework can fine-tune skills to improve the success rate and generalization of sequencing, while also giving the model the ability to autonomously switch skills, allowing it to quickly adjust strategies when encountering operation failures and ensuring a high success rate.
Technical Principles of Psi R0
- Reinforcement Learning (RL): Psi R0 is an end-to-end embodied model based on reinforcement learning, using massive simulation data to train a dual-hand operation intelligent agent.
- Skill Training Framework: This skill training framework abstracts key information from the spatiotemporal trajectories of objects to construct a general objective function, solving the problem of difficult reward function design.
Application Scenarios of Psi R0
- E-Commerce Scenarios: Psi R0 can be applied to product packaging operations in the e-commerce industry, involving multiple operations such as grasping, scanning, placing, and tying plastic bags for tens of thousands of products. Psi R0 can smoothly complete this series of actions using dual dexterous hands, replacing an entire workstation on the customer site.
- Factory Production Line Assembly: In manufacturing, Psi R0 can be used for assembly work on factory production lines, completing complex long-horizon task operations such as grasping, assembling, and placing components.
- Picking and Packaging in the Service Industry: Psi R0 is also suitable for picking and packaging tasks in the service industry, and can handle long-horizon task operations such as grasping, scanning, and placing.
- Home Environment Cleaning and Organizing: Psi R0 can also perform cleaning and organizing work in home environments, handling daily household tasks.