Humanoid robots are designed to look and move like people, with human-like bodies, arms, legs and joints. In the future, these machines could help humans perform physical tasks in homes, factories, hospitals, warehouses and other real-world environments.
But there is one major challenge: making robots move naturally and handle many different movements.
Although modern humanoid robots have become increasingly capable, most can reliably perform only a limited number of movements. Teaching a robot to walk is one thing. Teaching it to run, jump, spin, kick, crawl, balance and perform a cartwheel is much harder.
Now, researchers from the University of California, Berkeley (UC Berkeley) and Stanford University have developed an artificial intelligence framework called BeyondMimic that could help change this.
The system is designed to give humanoid robots a much broader movement repertoire without requiring engineers to separately train and fine-tune the robot for every new movement.
The researchers introduced BeyondMimic in a paper published in Science Robotics.
Why Teaching Robots Human Movement Is Difficult
Humans can perform thousands of movements without consciously calculating the position of every joint and muscle.
A person can suddenly start walking, change direction, jump over an object or perform a spin without needing to solve a complicated mathematical problem first.
Robots are different.
A humanoid robot must continuously calculate how its joints, limbs and body should move while maintaining balance and avoiding dangerous positions. Even a seemingly simple movement can involve many coordinated actions.
One common approach is to train robots using human demonstrations. Researchers provide examples of people walking, running, jumping or performing other movements, and the robot learns to reproduce them.
This can work, but it has limitations.
A system trained specifically for one movement may struggle when asked to perform something completely different. It can also produce movements that look unnatural or require engineers to manually adjust the controller for individual actions.
BeyondMimic attempts to solve this problem by giving the robot a more general understanding of movement.
Learning From Human Motion
To train BeyondMimic, the researchers used around 2.5 hours of human motion data.
The demonstrations included a wide variety of agile activities, including:
Walking and running
Dancing
Jumping
Martial arts
Kicking
Crawling
Cartwheels
Spinning movements
Other dynamic actions
The researchers then adapted these human movements to match the body proportions and joint structure of the G1 humanoid robot developed by Unitree Robotics.
This is important because a human body and a robot do not have exactly the same proportions, joints or physical capabilities. The movement therefore has to be translated into something the robot can physically perform.
Reinforcement Learning Helps the Robot Improve
The researchers used a machine-learning technique called reinforcement learning to teach the robot how to reproduce these movements.
The basic idea is similar to learning through rewards and penalties.
When the robot successfully follows the desired movement, the AI receives a positive reward. When it makes an undesirable movement, it receives a penalty.
For BeyondMimic, the system was encouraged to accurately track the position, orientation and speed of different parts of the robot's body.
At the same time, penalties discouraged behaviors that could make the robot unstable or unsafe. These included jerky movements, dangerous joint positions and unwanted contact between different parts of the robot.
Through repeated training, the control system learned how to reproduce a wide range of movements while keeping the robot stable.
Compressing Complex Movements
Another important part of BeyondMimic is the way it represents movements.
The researchers used a variational autoencoder, or VAE, to compress complex movement information into a smaller representation known as a latent space.
Think of it like creating a compact movement language.
Instead of storing every tiny detail of a movement separately, the AI learns a more efficient representation that captures the important characteristics of the action.
A decoder can then take this compressed information and convert it back into commands that the robot can execute.
This approach allows many different movements to be represented within the same framework.
The Role of the Diffusion Model
The researchers then introduced another AI technique: a diffusion model.
Diffusion models are widely known for generating images, but they can also be used to generate other types of data.
In BeyondMimic, the diffusion model learned sequences containing the robot's predicted physical states together with its compressed movement representations.
During training, noise was added to these sequences. The model then learned how to gradually reconstruct the original movement information.
This gives the system a way to generate and modify movement sequences rather than simply copying a single demonstration.
The researchers also used a technique called classifier guidance, allowing the system to steer generated movements toward objectives that were not directly included in its original training.
This is one of the most important features of the framework.
Beyond Simply Copying Humans
The name BeyondMimic reflects the researchers' larger goal.
The system is not designed simply to make a robot copy a human movement. Instead, it aims to allow the robot to combine and modify learned movements to deal with new situations.
For example, the researchers demonstrated capabilities involving motion inpainting, joystick-based teleoperation and obstacle avoidance.
Motion inpainting can be thought of as filling in missing parts of a movement sequence. This could potentially allow a robot to continue moving naturally even when the exact movement it needs was not included in its training examples.
Joystick teleoperation allows a human operator to influence the robot's movements, while obstacle avoidance can help the robot adjust its behavior when something blocks its path.
Importantly, the researchers reported that these capabilities could be transferred to real hardware without additional task-specific training, a process often referred to as zero-shot transfer.
From Simulation to a Real Robot
The team first tested BeyondMimic in simulation before deploying 30 representative movement clips on a real Unitree G1 humanoid robot.
The results included a surprisingly broad range of behaviors.
The robot demonstrated balancing poses, dancing, crawling, sprinting, kicks, spinning jumps and even aerial cartwheels.
These are significantly more complicated than basic walking because they require the robot to coordinate multiple joints while controlling its balance and momentum.
The experiments suggest that a single AI framework can potentially support a much larger library of agile behaviors.
Did the Movements Actually Look Human?
The researchers also wanted to know whether people could notice a difference between BeyondMimic and the robot's standard controller.
They conducted a study involving 77 participants.
Participants compared walking and running movements produced by BeyondMimic with movements generated using Unitree's standard controller.
In 70.8% of the comparisons, participants judged the BeyondMimic movements to be more humanlike and natural.
That result is significant because robotic movement is not only about whether a machine reaches its destination. How it moves can also affect how people perceive and interact with it.
A robot that moves more naturally could potentially be easier and more comfortable for humans to work around.
A Step Toward More Versatile Humanoid Robots
BeyondMimic represents an important direction for humanoid robotics: instead of creating a separate control system for every possible action, researchers are exploring ways to build general-purpose movement systems.
If such approaches continue to improve, humanoid robots could eventually learn to combine different movements depending on the situation.
A robot might walk toward an object, step over an obstacle, change direction, crouch down and pick something up without requiring a completely separate controller for each action.
However, the technology is still at an early stage. More testing will be needed to determine how well the approach works across different environments, robots and real-world tasks.
The researchers plan to explore broader motion datasets and potentially adapt BeyondMimic to other humanoid platforms.
If successful, this kind of technology could help move humanoid robots beyond rigid, pre-programmed movements and toward machines capable of more flexible, natural and adaptable physical behavior.
The ultimate goal is not simply to make robots look human.
It is to give them something much more useful: the ability to move, adapt and respond more like humans in the real world.
Reference:
- Qiayuan Liao et al.
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