Obstacle detection is about having autonomous vehicles perceive their surroundings, identify objects, and determine if they might conflict with the accomplishment of the robot’s task, e.g., navigating to reach a goal position.

Amongst the many applications of AI, object detection from images is arguably the one that experienced the most performance enhancement compared to “traditional approaches” such as color or blob detection. 

Images are, from the point of view of a machine, nothing but (several) “tables” of numbers, where each number represents the intensity of light, at that location, across a channel (e.g., R, G, B for colored images). 

Giving meaning to a cluster of numbers is not as easy as, for a human, it would be to identify a potential obstacle on the path. Machine learning-driven approaches have quickly outperformed traditional computer vision approaches at this task, strong of the abundant and cheap data for training made available by datasets and general imagery on the internet.

Various approaches (networks) for object detection have rapidly succeded in outperforming each other, and YOLO models particularly for their balance of computational efficiency and detection accuracy.  

Learn about robot autonomy, and the difference between traditional and machine learning approaches, from the links below!

“A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms“ by Armin Mokhtarian et al. offers a comparison of current small-scale testbeds for Connected and Automated Vehicles (CAVs), Vehicle Autonomy and Robot Swarms (RS).

As mentioned in , small-scale autonomous vehicle testbeds are paving the way to faster and more meaningful research and development in vehicle autonomy, embodied AI, and AI robotics as a whole. 

Although small-scale, often made of off-the-shelf components and relatively low-cost, these platforms provide the opportunity for deep insights into specific scientific and technological challenges of autonomy. 

Duckietown, in particular, is highlighted for its modular, miniature-scale smart-city environment, which facilitates the study of autonomous vehicle localization and traffic management through onboard sensors.

Learn about robot autonomy, traditional robotics autonomy architectures, agent training, sim2real, navigation, and other topics with Duckietown, starting from the link below!

In the field of autonomous driving, transferring policies from simulation to the real world (Sim-to-real transfer, or Sim2Real) is theoretically desirable, as it is much faster and more cost-effective to train agents in simulation rather than in the real world. 

Given simulations are just that – representations of the real world – the question of whether the trained policies will actually perform well enough in the real world is always open. This challenge is known as “Sim-to-Real gap”. 

This gap is especially pronounced in Multi-Agent Reinforcement Learning (MARL), where agent collaboration and environmental synchronization significantly complicate policy transfer.

The authors of this work propose employing “Multi-Agent Proximal Policy Optimization” (MAPPO) in conjunction with domain randomization techniques, to create a robust pipeline for training MARL policies that is not only effective in simulation but also adaptable to real-world conditions.

Through varying levels of parameter randomization—such as altering lighting conditions, lane markings, and agent behaviors— the authors enhance the robustness of trained policies, ensuring they generalize effectively across a wide range of real-world scenarios.

Learn about training, sim2real, navigation, and other robot autonomy topics with Duckietown starting from the link below!

One of the classical objections made to machine learning approaches to embeddded autonomy (i.e., to create agents that are deployed on real, physical, robots) is that training requires data, data requires experiement, and experiment are “expensive” (time, money, etc.). 

The natural counter argument to this is to use simulation to create the training data, because simulations are much less expensive than real world experiment; they can be ran continuously, with accellerated time, don’t require supervision, nobody gets tired, etc. 

But, as the experienced roboticist knows, “simulations are doomed to succeed”. This phrase encapsulates the notion that simulations do not contain the same wealth if information as the real world, because they are programmed to be what the programmer wants them to be useful for – they do not capture the complexity of the real world. Eventually things will “work” in simulation, but does that mean they will “work” in the real-world, too?

As Carl Sagan once said: “If you wish to make an applie pie from scratch, you must first reinvent the universe”. 

Domain randomization is an approach to mitigate the limitations of simulations. Instead of training an agent on one set of parameters defining the simulation, many simulations are instead ran, with different values of this parameters. E.g., in the context of a driving simulator like Duckietown, one set of parameters could make the sky purple instead of blue, or the lane markings have slightly different geometric properties, etc. The idea behind this approach is that the agent will be trained on a distribution of datasets that are all slightly different, hopefully making the agent more robust to real world nuisances once deployed in a physical body. 

In this paper,  the authors investigate specifically visual domain randomization. 

Learn about RL, navigation, and other robot autonomy topics at the link below!

What is interpretable feature discovery in reinforcement learning?

To understand this, let’s introduce a few important topics:

Reinforcement Learning (RL): A machine learning approach where an agent gains the ability to make decisions by engaging with an environment to accomplish a specific objective. Interpretable Feature Discovery in RL is an approach that aims to make the decision-making process of RL agents more understandable to humans.

The need for interpretability: In real-world applications, especially in safety-critical domains like self-driving cars, it is crucial to understand why an RL agent makes a certain decision. Interpretability helps:

• Build trust in the system
• Debug and improve the model
• Ensure compliance with regulations and ethical standards
• Understand fault if accidents arise

Feature discovery: Feature discovery in this context refers to identifying the key artifacts (features) of the environment that the RL agent is focusing on while making decisions. For example, in a self-driving car simulation, relevant features might include the position of other cars, road signs, or lane markings.

Learn about RL, navigation, and other robot autonomy topics at the link below!