AI Olympics Competition for IROS 2024 – The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) will be held October 14

Motivation

As artificial intelligence gains new capabilities, it becomes important to evaluate it on real-world tasks. While software such as ChatGPT has recently revolutionized certain areas of AI, athletic intelligence seems to still be elusive in the AI community. To have better robots in the future which can perform a wide variety of dynamic tasks in uncertain environments, the physical or athletic intelligence of robots must be improved. However, this is quite challenging. In particular, the fields of robotics and reinforcement learning (RL) lack standardized benchmarking tasks on real hardware. To facilitate reproducibility and stimulate algorithmic advancements, the 3rd AI Olympics competition is being proposed to be held at ICRA 2025 in Atlanta following the inaugural run at IJCAI 2023 in Macau (video summary), based on the RealAIGym project and the 2nd edition of this competition at IROS 2024 in Abu Dhabi.

While the inaugural run of this competition focussed on comparing the performance of various learning and control methods on the real acrobot/pendubot systems, the second edition at IROS focussed not only on achieving the best scores but also on achieving robustness to disturbances during the execution (external torques shown in the figure on the right).

At ICRA 2025, the focus of the challenge will be to develop a global policy that can solve the swing up problem from any configuration in the state space, thus improving the controllers in the 3rd edition of this challenge. This will be tested by randomly disturbing the system with very large (compared to IROS 2024) external disturbances while it performs the swingup task.

The challenge will involve two stages: simulation and real-robot experiments where teams (and their agents) can compete to get the highest score to win some cool prizes! We invite people from all communities (AI/ML/RL, Optimal Control, Heuristics, etc.) to participate in this competition on a set of standardized dynamic tasks on well-known prototypical systems using standardized hardware.

The Challenge

For the challenge, we will use a canonical 2-link robot system with two different configurations. When the actuator in the shoulder joint isactive and the elbow is passive, it functions as a Pendubot. And when the shoulder actuator is passive and the elbow is active, it functions as an Acrobot (inspired by the acrobat athlete seen above). The challenge consists of the following task that has to be carried out first in simulation and then the best teams will be selected to carry out the experiments on real robots: Swing-up and Stabilize an Underactuated 2-link System Acrobot and/or Pendubot. The swing-up is carried out from an initial position which is the robot pointing straight down. The participating teams can decide to either work on the Acrobot swing-up or the Pendubot swing-up or both. For scoring and prizes, Acrobot and Pendubot will be treated as 2 separate tracks i.e. the Acrobot scores/papers will be compared only against other Acrobot teams. For each track, upto 4 teams will be selected from the simulation stage to participate in the real robot stage. One final winner will be selected for each track. The performance and robustness of the swing-up and stabilize controllers will be judged based on a custom scoring system. The final score is the average of the performance score and the robustness score for the acrobot/pendubot system. The final scores of the submissions will be added to the RealAIGym leaderboard.

The competition consists of two stages: simulation stage and real-robot stage. We provide a realistic simulation (with identified system parameters) along with several baseline controllers. The participants are asked to develop new or improve existing algorithms for this task which may exploit state of the art machine learning, optimal control and heuristic methods or any of their possible combinations. They should compete against the performance of the already available baseline controller for the benchmarking criteria set by the organizing committee. The algorithms will be evaluated on those criteria and the final score will be published on a leaderboard. The participants are encouraged to submit their contributions in the form of 2–4-page paper with a link to their GitHub code. The best performers will receive prizes and special recognition.

System Description

Our dual-purpose setup implements a double pendulum platform built using two quasi-direct drive actuators (QDDs). Due to high mechanical transparency offered by QDDs, one of the actuators can be used as a passive encoder. When the shoulder motor is passive and elbow motor is active, the system is an acrobot and when the shoulder is active and elbow is passive, the system is a pendubot.

The acrobot/pendubot is simulated with a Runge-Kutta 4 integrator with a timestep of $dt=0.002s$ for $T=60s$ .

Detailed system description can be found in the related article and its supplementary material.

Task

Perform swing up and upright stabilization using a single actuator either in acrobot or pendubot configuration in presence of large disturbances.

Controller requirements:

The controller must inherit from the AbstractController class provided in the repository.
Friction compensation on both joints is allowed in both pendubot and acrobot. The authors are free to choose a friction compensation model of their choice but the utilized torque on the passive joint must not exceed 0.5 Nm.
The following hardware restrictions must be respected by the controller:
- Control loop frequency: 700 Hz maximum
- Torque limit: 6 Nm
- Velocity limit: 30 rad/s
- Position limits: ± 540 degrees for both joints

The following rules apply:

Each attempt has an attempt of a total time duration of 60s (swing ups + stabilizations).
Whenever the pendulum is in the goal region (end-effector above the line) for 4s, the controller is deactivated for 1s and the pendulum is driven to a random state. The controller has to swing up again from that random state.

Performance Score

The performance score is measured as the total time the system stays in the topmost/swing-up position within the goal region during the 60s trial.

The benchmarking/grading class will be realeased with the start of the competition on January 15 2025.

Hint for the real system: Real Robots do not like non-smooth torque and velocity. It helps to have smooth torque and velocity on the real-system to increase chances of success. Check earlier compeititon (IROS2024 and IJCAI2023) scoring metrics for inspiration.

The performance leaderboards for the acrobot and pendubot systems can be found here

Protocol

The two stages of the challenge are as follows:

Simulation Stage

For the simulation stage of the competition, we use the following repository from the RealAIGym Project: Double Pendulum. The documentation of the project for installation, double pendulum dynamics, repository structure, hardware, and controllers can be found here. Please follow the installation instructions to start developing your controllers.

You have to develop a new controller or improve an existing controller for the given simulator (plant). The controller can then be tested for the leaderboard using the instructions given for the Acrobot here: Performance Scoring. Similar Pendubot scoring scripts are available here.

To develop a new controller, you can use any of the many many examples given in the repo. A good starting point would be to look at the controllers given here. Your controller must inherit from the AbstractController class provided in the repository. See here for the documentation on how to write your controller using the AbstractController class.

The double pendulum model parameters for the simulation stage are those from ‘design_C.1/model_1.1‘.

Once you’ve developed a new controller and are happy with the results, please follow the following submission guidelines:

Create a fork of the repository.
Add a Dockerfile to your forked repository that includes all the custom libraries you’ve installed/used that are not part of the double pendulum dependencies. This allows us to use the Dockerfile to recreate your environment with the correct libraries to run the submitted controller. For a tutorial on how to make a Dockerfile, we can recommend the official Docker website.
Add your developed controllers to the forked repository. Important: Do not change the plant/dynamics/integrator (This may result in an outright disqualification of the team)!! Remember to use the AbstractController class.
Submit the URL of the fork along with a 2-4 page paper about the method developed and the results to Dennis.Mronga@dfki.de with [AI Olympics] in the email subject. Please follow the following guidelines for the paper:
- Page Limit: 2-4 Pages including references
- Include the standard plots for position, velocity, and torque with respect to time in the paper. For an example, see timeseries.png here. These plots are generated after simulation if you use the provided function plot_timeseries(T, X, U).
- Include the tables for performance metrics against the baseline controllers made available on the RealAIGym leaderboards.
- Use the following template: IEEEConfig.zip

The submitted code and papers will be reviewed and the leaderboard benchmarks will be re-run by us to compute the final scores. The scores as well as the paper reviews will be used to determine the best 4 teams which will carry out the experiments using their controllers on the real systems at ICRA 2025 AI Olympics!

Real-Robot Stage

Coming soon….

Schedule/Important Dates

The on-site competition will require 2 full days: first day for teams to prepare and setup with the hardware, and second day the competition is open to public. Prior to the ICRA, the teams will have both access to the simulation and to the real system via remote experiments, therefore they will already come with developed controllers which will be showcased at ICRA.

Additionally, we will organize a workshop where we invite all participating teams to present their algorithms and discuss the solutions.

Important Dates

Competition Day Schedule

Important Dates

Competition Day Schedule