[HumanoidBench] Simulating The Future Of Humanoid Robots

Robot 24/06/2024

3 main points
✔️ This study developed HumanoidBench, which uses advanced simulation technology. The benchmark evaluates the performance of different algorithms using humanoid robots that include a wide variety of tasks, such as dexterous hands and complex whole-body manipulation.
✔️ evaluates the performance of reinforcement learning (RL) algorithms and identifies challenges for humanoid robots in learning tasks. Four major RL methods were used for this, including DreamerV3, TD-MPC2, SAC, and PPO. Results showed that the baseline algorithm fell below the success threshold for many tasks.
✔️ In future research, it is important to study the interactions between different sensing modalities. Also, incorporating more realistic objects and environments with real-world variety and high-quality rendering will be considered.

HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation
Written by Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee, Pieter Abbeel
(Submitted on 15 Mar 2024)
Comments: Published on arxiv.
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

code:

The images used in this article are from the paper, the introductory slides, or were created based on them.

Summary

Humanoid robots are expected to support humans in a variety of environments and tasks by having a human-like form. However, expensive and fragile hardware has been a challenge in this research. Therefore, this study developed HumanoidBench, which uses advanced simulation technology. This benchmark evaluates the performance of different algorithms using humanoid robots that include a wide variety of tasks, such as dexterous hands and complex whole-body manipulation. The study results show that state-of-the-art reinforcement learning algorithms struggle on many tasks, while hierarchical learning algorithms perform better on basic movements such as walking and touching objects.HumanoidBench is a valuable tool for the robotics community to identify challenges faced by humanoid robots and to develop algorithms and ideas that can be used to improve the performance of humanoid robots. challenges faced by humanoid robots and provides a platform for rapid validation of algorithms and ideas.

Introduction

Humanoid robots are expected to integrate seamlessly into our daily lives. However, their controls are designed manually for specific tasks, and new tasks require significant engineering work. To solve this problem, a benchmark called HumanoidBench has been developed to facilitate the learning of humanoid robots. This includes complex control, body coordination, long-term tasks, and many other challenges. The platform provides a safe and inexpensive place to test robot learning algorithms in a safe environment and includes a variety of tasks related to everyday human tasks.HumanoidBench can easily incorporate a variety of humanoid robots and end-effectors, 15 whole body manipulation tasks and 12 movement tasks. This allows state-of-the-art RL algorithms to control the complex dynamics of humanoid robots, and provides direction for future research.

Related Research

Deep reinforcement learning (RL) is advancing rapidly with the advent of standardized, simulated benchmarks. However, existing simulation environments for robot operations focus primarily on static, short-term skills and do not address complex operations. In contrast, benchmarks have been proposed that focus on a variety of long-term operations. However, most benchmarks are designed for specific tasks and many use simplified models. This creates a need for comprehensive benchmarks based on real hardware.

Simulation Environment

A Unitree H1 humanoid robot with two dexterous shadow hands2 will be used as the primary robotic agent. This robot is simulated via MuJoCo. The simulated environment supports a variety of observations, including robot state, object state, visual observation, and whole-body tactile sensing. The humanoid robot is also controlled by positional control.

HumanoidBench

To perform human-like tasks, robots must be able to understand their environment and perform appropriate actions. However, experimenting with robots in the real world is difficult due to cost and safety concerns. Therefore, simulation environments have become an important tool for robot learning and control.

HumanoidBench consists of 27 tasks with a high-dimensional movement space (up to 61 actuators). The movement task includes basic movements such as walking and running. The manipulation task, on the other hand, includes advanced tasks such as pushing, pulling, lifting, and catching objects.

The purpose of benchmarking is to evaluate the extent to which state-of-the-art algorithms can accomplish these tasks. The robot must observe the state of the environment and choose appropriate actions accordingly. Through the reward function, the robot learns the optimal strategy for performing the task.

For example, in a walking task, the robot must walk without falling while maintaining forward speed. Optimization of balance and gait pattern is important in such tasks. On the other hand, in the manipulation task, the robot must accurately manipulate an object. This requires an understanding of the object's position and posture, as well as proper force control.

The goal of HumanoidBench is to facilitate progress in the field of robot learning and control through these tasks. The simulation environment allows researchers to safely conduct experiments and evaluate robot performance in many different scenarios. This will enable the development of better control algorithms and learning methods, which will facilitate the future use of humanoid robots in the real world.

Experiment

The performance of reinforcement learning (RL) algorithms is evaluated to identify challenges in learning tasks for humanoid robots. Four major RL methods were used for this, including DreamerV3, TD-MPC2, SAC, and PPO. Results showed that the baseline algorithms fell below the success threshold for many tasks.

In particular, current RL algorithms struggle with high-dimensional action spaces and complex tasks. Humanoid robots have particular difficulty with tasks that require dexterous hands and complex body coordination. In addition to this, manipulation tasks are particularly challenging and tend to be less rewarding.

A common failure in humanoid benchmarks is that robots have difficulty learning expected behaviors in tasks such as high bars, doors, and hurdles. This is due to the difficulty in finding appropriate policies for complex behaviors.

A tiered RL approach is being considered to address these challenges. Training low-level skills and combining them through high-level planning policies could facilitate task resolution. However, the current algorithm still has room for improvement.

Conclusion

This study introduced a benchmark for high-dimensional humanoid robot control called HumanoidBench. This benchmark provides a comprehensive humanoid environment that includes a variety of movement and manipulation tasks, from toys to practical applications. The authors of the paper hope to challenge these complex tasks and facilitate the development of whole-body algorithms for humanoid robots.

In future research, it is important to study the interactions between different sensing modalities. Consideration will also be given to incorporating more realistic objects and environments with real-world variety and high-quality renderings. Additionally, the focus will be on other means to bootstrap learning in environments where physical demonstrations are difficult to collect.