Inspired by Alan Turing's idea of a child machine, in this article, we introduce the formal definition of a robotic baby, an integrated system with minimal world knowledge at birth, capable of learning incrementally and interactively, and adapting to the world. Within the definition, fundamental capabilities and system characteristics of the robotic baby are identified and presented as the system-level requirements. As a minimal viable prototype, theBabyarchitecture is proposed with a systems engineering design approach to satisfy the system-level requirements, which has been verified and validated with simulations and experiments on a robotic system. We demonstrate the capabilities of the robotic baby in natural language acquisition and semantic parsing in English and Chinese, as well as in natural language grounding, natural language reinforcement learning, natural language programming, and system introspection for explainability. The education and evolution of the robotic baby are illustrated with real-world robotic demonstrations. Inspired by the genetic inheritance in human beings, knowledge inheritance in robotic babies and its benefits regarding evolution are discussed.

Applications that require multirobot systems to operate independently for extended periods of time in unknown or unstructured environments face a broad set of challenges, such as hardware degradation, changing weather patterns, or unfamiliar terrain. To operate effectively under these changing conditions, algorithms developed for long-term autonomy applications require a stronger focus on robustness. Consequently, this work considers the ability to satisfy the operation-critical constraints of a disturbed system in a modular fashion, which means compatibility with different system objectives and disturbance representations. Toward this end, this article introduces a controller-synthesis approach to constraint satisfaction for disturbed control-affine dynamical systems by utilizing control barrier functions (CBFs). The aforementioned framework is constructed by modeling the disturbance as a union of convex hulls and leveraging previous work on CBFs for differential inclusions. This method of disturbance modeling grants compatibility with different disturbance-estimation methods. For example, this work demonstrates how a disturbance learned via a Gaussian process may be utilized in the proposed framework. These estimated disturbances are incorporated into the proposed controller-synthesis framework which is then tested on a fleet of robots in different scenarios.

In this paper, we investigate how heterogeneous multi-robot systems with different sensing capabilities can observe a domain with an a priori unknown density function. Common coverage control techniques are targeted towards homogeneous teams of robots and do not consider what happens when the sensing capabilities of the robots are vastly different. This work proposes an extension to Lloyd’s algorithm that fuses coverage information from heterogeneous robots with differing sensing capabilities to effectively observe a domain. Namely, we study a bimodal team of robots consisting of aerial and ground agents. In our problem formulation we use aerial robots with coarse domain sensors to approximate the number of ground robots needed within their sensing region to effectively cover it. This information is relayed to ground robots, who perform an extension to Lloyd’s algorithm that balances a locally focused coverage controller with a globally focused distribution controller. The stability of the Lloyd’s algorithm extension is proven and its performance is evaluated through simulation and experiments using the Robotarium, a remotely-accessible, multi-robot testbed.

In this paper, we propose a graph-based search method to optimally allocate tasks to a team of robots given a global task specification. In particular, we define these agents as discrete transition systems. In order to allocate tasks to the team of robots, we decompose finite linear temporal logic (LTL) specifications and consider agent specific cost functions. We propose to use the stochastic optimization technique, cross entropy, to optimize over this cost function. The multi-agent task allocation cross-entropy (MTAC-E) algorithm is developed to determine both when it is optimal to switch to a new agent to complete a task and minimize the costs associated with individual agent trajectories. The proposed algorithm is verified in simulation and experimental results are included.

Writing dependently typed functional programs that capture non-trivial program properties is difficult in current systems due to lack of proof automation. We identify proof patterns that occur when programming with dependent types and detail how automating such patterns allow us to work more comfortably with types that capture, for example, membership, ordering and non-linear arithmetic properties. We describe the role of the rippling heuristic, both for inductive and non-inductive proofs, and generalisation in providing such automation. We then discuss an implementation of our ideas in Coq with practical examples of dependently typed programs, that capture useful program properties, which can be verified automatically. We demonstrate that our proof automation is generic in that it can provide support for working with theorems involving user-defined functions and inductive data types.