With the rapid development of artificial intelligence and robotics, humanoid robots are gradually moving from science fiction to reality. They are expected to provide assistance and services to humans in various complex environments, but how to achieve efficient, flexible and natural human-machine interaction and control has always been one of the key challenges in this field . Recently, the OmniH2O  project jointly developed by  Carnegie Mellon University ( CMU ) and Shanghai Jiao Tong University has brought new breakthroughs and ideas to this problem . This article will deeply explore  the technical principles, core functions, application scenarios and how to quickly get started with OmniH2O  , and take you to appreciate the charm of this cutting-edge technology.

1. Project Overview

The full-body control of humanoid robots is a major challenge. Existing research focuses on lower-body control or decoupled control of the upper and lower bodies, which makes it difficult to achieve the unity of dexterous manipulation and robust motion . In addition, traditional remote manipulation interfaces rely on expensive equipment, which limits large-scale data collection. The OmniH2O  project aims to develop a learning-based full-body humanoid robot remote manipulation and autonomy system, using kinematic gestures as a universal control interface to enable humanoid robots to operate as flexibly as humans, and achieve full autonomy through remote manipulation demonstration learning or integration of cutting-edge models, promoting the development of humanoid robot technology.

2. Technical Principle

1. Whole-body remote operation technology

OmniH2O  uses kinematic gestures as a universal control interface, allowing humans to remotely control full-size humanoid robots in real time in a variety of ways. Specifically, the operator can wear a virtual reality ( VR ) headset and control the robot's full-body movements through body movements; or use voice commands to issue task commands to the robot; in addition,  the operator's gesture information can be captured using an RGB  camera to achieve remote control of the robot. This diverse control method greatly improves the flexibility and naturalness of human-machine interaction, allowing operators to choose the most suitable control method according to different task requirements and environmental conditions.

2. Reinforcement Learning and Strategy Distillation

In order to enable robots to better adapt to complex real-world tasks, OmniH2O  has developed a simulation-to-real ( sim-to-real ) pipeline based on reinforcement learning. First, a large amount of training data is generated through large-scale redirection and enhancement of human motion datasets, covering a variety of different motion modes and task scenarios. Then, this data is used to train a privileged teacher policy that performs well in simulated environments and can complete complex tasks. Then, through policy distillation technology, the knowledge of the privileged teacher policy is transferred to the actual deployment policy, so that the robot can also achieve efficient and stable motion control in real environments. In addition, OmniH2O  has also designed a special reward function to enhance the robustness and stability of the robot , so that it can still maintain good performance in the face of various interferences and uncertainties.

3. Dexterous hand control technology

Hand dexterity is crucial in the manipulation tasks of humanoid robots. OmniH2O  has also conducted in-depth research and development in dexterous hand control. It uses  VR  -estimated hand postures and directly calculates the joint targets of low-level hand controllers based on inverse kinematics, thereby achieving high-precision hand manipulation . This control method can accurately control the robot's finger movements, enabling it to complete complex tasks such as grasping and manipulating small objects, greatly improving the practicality and flexibility of humanoid robots.

3. Core Functions

1. Integration of multiple control methods

OmniH2O  supports seamless integration of multiple control methods, and operators can choose the most suitable control method according to the complexity of the task and personal preference.

For example, when performing simple mobile tasks, voice commands can be used to quickly issue commands; when performing delicate operation tasks, precise control can be achieved with the help of  VR  headsets and hand tracking devices. This integration of multiple control methods not only improves the efficiency of human-computer interaction, but also enhances the flexibility and adaptability of operations.

2. Autonomous learning and task adaptation

OmniH2O  has strong self-learning capabilities and can  achieve full autonomy by learning from remote operation demonstrations or integrating with cutting-edge models such as GPT-4o . This means that the robot can automatically adjust its behavior strategy according to different task requirements without human intervention.

For example, when faced with a new task, the robot can first observe human operation demonstrations, and then quickly master the key steps and operation skills of the task through autonomous learning, thereby achieving efficient task completion. This autonomous learning ability greatly improves the intelligence level of humanoid robots, enabling them to better adapt to various complex and changing task scenarios.

3. High-precision whole-body operation

OmniH2O  is capable of high-precision full-body manipulation and supports complex two-handed manipulation tasks. Whether it is organizing and manipulating daily items in an indoor environment or performing complex tasks in a field environment, the robot is able to perform well. Its high-precision manipulation capability is due to advanced motion control algorithms and dexterous hand control technology, which enables the robot to precisely control the movement of each joint and finger when performing tasks, thereby achieving efficient and accurate task completion.

4. Quick Use

Make sure you have the following software installed: Python 3.8 , PyTorch , Isaac Gym , Legged Gym , RSL RL

1. Environmental preparation

1. Create a Python environment 

conda create -n omnih2o python=3.8conda activate omnih2opip install torch torchvision torchaudio

2. Install Isaac Gym

Download and install [Isaac Gym](https://developer.nvidia.com/isaac-gym) . 

After decompression, run:

cd isaacgym/python && pip install -e .

3. Install OmniH2O

git clone https://github.com/LeCAR-Lab/human2humanoid.gitcd human2humanoidpip install -r requirements.txt

2. Training and Operation

1. Training privileged teachers strategy

python legged_gym/scripts/train_hydra.py --config-name=config_teleop task=h1:teleop run_name=OmniH2O_TEACHER

2. Run the privileged teacher policy

python legged_gym/scripts/play_hydra.py --config-name=config_teleop task=h1:teleop load_run=OmniH2O_TEACHER checkpoint=XXXX

3. Training student strategies ( Sim2Real)

python legged_gym/scripts/train_hydra.py --config-name=config_teleop task=h1:teleop run_name=OmniH2O_STUDENT train.distill=True

4. Implement student strategies

python legged_gym/scripts/play_hydra.py --config-name=config_teleop task=h1:teleop load_run=OmniH2O_STUDENT checkpoint=XXXX

For more details, please see the open source address: https://github.com/LeCAR-Lab/human2humanoid

5. Application Scenarios

1. Family Services

OmniH2O  can provide people with various services in the home environment, such as housework, moving items, and accompanying the elderly and children. It can assist people in completing tedious housework tasks such as cleaning, tidying up the room, and carrying heavy objects, reducing people's labor burden. At the same time, it can also provide companionship and entertainment for the elderly and children through interaction with humans, enriching their lives.

2. Industrial production

In the field of industrial production, OmniH2O  can be used as an auxiliary robot to help workers complete some dangerous, repetitive or delicate operation tasks. For example, in an electronics manufacturing factory, it can assist workers in the assembly and testing of electronic components; in an automobile manufacturing factory, it can complete tasks such as the handling and installation of automobile parts. Its high-precision operation ability and autonomous learning ability enable it to quickly adapt to different production tasks and process flows, improving production efficiency and quality.

3. Medical care

OmniH2O  also has broad application prospects in the field of medical care. It can assist medical staff in patient care, rehabilitation training , etc. For example, in hospital wards, it can provide patients with daily care services, such as feeding, giving medicine, and helping patients turn over; in rehabilitation centers, it can assist patients in rehabilitation training, helping patients to better restore their physical functions through precise motion control and real-time feedback.

4. Wilderness Exploration and Rescue

OmniH2O  's high-precision full-body manipulation capabilities and autonomous learning capabilities enable it to perform exploration and rescue missions in field environments. It can replace humans in entering dangerous areas to conduct environmental monitoring, resource exploration, and other tasks, providing important data support for humans. When natural disasters occur, it can also assist rescuers in search and rescue work, improving rescue efficiency and success rate.

VI. Conclusion

The OmniH2O  project has brought new breakthroughs to the development of artificial intelligence and robotics with its universal and dexterous humanoid robot remote operation and learning system. It has achieved efficient, flexible and autonomous operation of humanoid robots through the integration of multiple control methods, reinforcement learning and policy distillation technology, and dexterous hand control technology, laying a solid foundation for the widespread application of humanoid robots in the future.