20 accepted papers
This article discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emp
We consider the interaction among agents engaging in a driving task and we model it as general-sum game. This class of games exhibits a plurality of different equilibria posing the issue of equilibrium selection. While selecting the most efficient equilibrium (in term of social cost) is often imprac
In recent years, driving context perception has emerged as one of the key aspects to design driving assistance algorithms and user interfaces that are effective in adapting to different traffic situations or environments. To this aim, we introduce the Anonymous Driving Scene Perception (ADSP) Model,…
Dynamic games feature a state-space complexity that scales superlinearly with the number of players. This makes this class of games often intractable even for a handful of players. We introduce the factorization process of dynamic games as a transformation leveraging the independence of players at e…
Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which each player expresses a preference over the outcomes via a partially ordered set of metrics. This allows one to combine hie
Visual navigation for insect-scale robots is very challenging because in such a small scale, the size, weight, and power (SWaP) constraints do not appear to permit visual navigation techniques such as SLAM (Simultaneous Localization and Mapping) because they are likely to be too power-hungry. We pro…
We consider the problem of co-designing embodied intelligence as a whole in a structured way, from hardware components such as propulsion systems and sensors to software modules such as control and perception pipelines. We propose a principled approach to formulate and solve complex embodied intelli…
In many situations it is either impossible or impractical to develop and evaluate agents entirely on the target domain on which they will be deployed. This is particularly true in robotics, where doing experiments on hardware is much more arduous than in simulation. This has become arguably more so…
Plastic neural networks have the ability to adapt to new tasks. However, in a continual learning setting, the configuration of parameters learned in previous tasks can severely reduce the adaptability to future tasks. In particular, we show that, when using weight decay, weights in successive layers…
We describe Urban Driving Games (UDGs) as a particular class of differential games that model the interactions and incentives of the urban driving task. The drivers possess a “communal” interest, such as not colliding with each other, but are also self-interested in fulfilling traffic rules and pers
As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible. Compared to other sciences, there are specific challenges to benchmarking autonomy, such as the complexity of the software stacks, the variability of the hardware and the rel…
Robots that need to act in an uncertain, populated, and varied world need heterogeneous sensors to be able to perceive and act robustly. For example, self-driving cars currently on the road are equipped with dozens of sensors of several types (lidar, radar, sonar, cameras, ...). All of this existing…
Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duckieboat. Duckieboats are designed to be easily reproducible with parts from a 3D printer and other commercially available parts, wit…
The behavior of self-driving cars must be compatible with an enormous set of conflicting and ambiguous objectives, from law, from ethics, from the local culture, and so on. This paper describes a new way to conveniently define the desired behavior for autonomous agents, which we use on the self-driv…
One of the challenges of developing autonomous vehicles is planning in an inhabited environment under sensing uncertainty as well as limited perception and computational resources. Besides reasoning about the behaviour of traffic participants that are within the vehicles' field of view, safe autonom…
Typically, to enlarge the operating domain of an object detector, more labeled training data is required. We describe a method called wormhole learning, which allows to extend the operating domain without additional data, but only with temporary access to an auxiliary sensor with certain invariance…
Co-design problems in the field of robotics involve the tradeoff of “resources” usage, such as cost, execution time, and energy, with mission performance, under recursive constraints that involve energetics, mechanics, computation, and communication. This letter shows that a large class of codesign
Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies…
This work contributes to a compositional theory of “codesign” that allows to optimally design a robotic platform. In this framework, the user describes each subsystem as a monotone relation between “functionality” provided and “resources” required. These models can be easily composed to express the
There is considerable freedom in choosing the sensors to be equipped on a robot. Currently many sensing technologies are available (radar, lidar, vision sensors, time-of-flight cameras, etc.). For each class, there are additional choices regarding the exact sensor parameters (spatial resolution, fra…