Hot-swapping robot task goals in reactive formal synthesis

We consider the problem of synthesizing robot controllers to realize a task that unpredictably changes with time. Tasks are formally expressed in the GR(1) fragment of temporal logic, in which some of the variables are set by an adversary. The task changes by the addition or removal of goals, which occurs online (i.e., at run-time). We present an algorithm for mending control strategies to realize tasks after the addition of goals, while avoiding global re-synthesis of the strategy. Experiments are presented for a planar surveillance task in which new regions of interest are incrementally added. Run-times are empirically shown to be favorable compared to re-synthesizing from scratch. We also present an algorithm for mending control strategies for the removal of goals. While in this setting the original strategy is still feasible, our algorithm provides a more satisfying solution by "tightening loose ends.'' Both algorithms are shown to yield so-called reach annotations, and thus the control strategies are easily amenable to other algorithms concerning incremental synthesis, e.g., as in previous work by the authors for navigation in uncertain environments

Warm-Starting Fixed-Point Based Control Synthesis

In this work we propose a patching algorithm to incrementally modify controllers, synthesized to satisfy a temporal logic formula, when some of the control actions become unavailable. The main idea of the proposed algorithm is to “warm-start” the synthesis process with an existing fixed-point based controller that has a larger action set. By exploiting the structure of the fixed-point based controllers, our algorithm avoids repeated computations while synthesizing a controller with restricted action set. Moreover, we show that the algorithm is sound and complete, that is, it provides the same guarantees as synthesizing a controller from scratch with the new action set. An example on synthesizing controllers for a simplified walking robot model under ground constraints is used to illustrate the approach. In this application, the ground constraints determine the action set and they might not be known a priori. Therefore it is of interest to quickly modify a controller synthesized for an unconstrained surface, when new constraints are encountered. Our simulations indicate that the proposed approach provides at least 5-times speed-up compared to synthesizing a controller from scratch.DARPAPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146730/1/warm-starting-for-deepblue.pd

Scalable Synthesis and Verification: Towards Reliable Autonomy

We have seen the growing deployment of autonomous systems in our daily life, ranging from safety-critical self-driving cars to dialogue agents. While impactful and impressive, these systems do not often come with guarantees and are not rigorously evaluated for failure cases. This is in part due to the limited scalability of tools available for designing correct-by-construction systems, or verifying them posthoc. Another key limitation is the lack of availability of models for the complex environments with which autonomous systems often have to interact with. In the direction of overcoming these above mentioned bottlenecks to designing reliable autonomous systems, this thesis makes contributions along three fronts. First, we develop an approach for parallelized synthesis from linear-time temporal logic Specifications corresponding to the generalized reactivity (1) fragment. We begin by identifying a special case corresponding to singleton liveness goals that allows for a decomposition of the synthesis problem, which facilitates parallelized synthesis. Based on the intuition from this special case, we propose a more generalized approach for parallelized synthesis that relies on identifying equicontrollable states. Second, we consider learning-based approaches to enable verification at scale for complex systems, and for autonomous systems that interact with black-box environments. For the former, we propose a new abstraction refinement procedure based on machine learning to improve the performance of nonlinear constraint solving algorithms on large-scale problems. For the latter, we present a data-driven approach based on chance-constrained optimization that allows for a system to be evaluated for specification conformance without an accurate model of the environment. We demonstrate this approach on several tasks, including a lane-change scenario with real-world driving data. Lastly, we consider the problem of interpreting and verifying learning-based components such as neural networks. We introduce a new method based on Craig's interpolants for computing compact symbolic abstractions of pre-images for neural networks. Our approach relies on iteratively computing approximations that provably overapproximate and underapproximate the pre-images at all layers. Further, building on existing work for training neural networks for verifiability in the classification setting, we propose extensions that allow us to generalize the approach to more general architectures and temporal specifications.</p

Time-annotated game graphs for synthesis from abstracted systems

The construction of discrete abstractions is a crucial part of many methods for control synthesis of hybrid systems subject to formal specifications. In general, the product of discrete abstractions may not be a discrete abstraction for the product of the underlying continuously-valued systems. Addressing this, we present a control synthesis method for transition systems that are built from components with uncertain timing characteristics. The new device, called here time-annotated game graphs, is demonstrated in a variety of examples. While it is applicable generally to parity games, we consider it in the context of control subject to GR(1) specifications. We show how a nominal strategy obtained without time knowledge can be modified to recover correctness when time information becomes available. The methods are applied to a brief case study of an aircraft electric power system

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control

Distributed, scalable, and safe control of large-scale multi-agent systems (MAS) is a challenging problem. In this paper, we design a distributed framework for safe multi-agent control in large-scale environments with obstacles, where a large number of agents are required to maintain safety using only local information and reach their goal locations. We introduce a new class of certificates, termed graph control barrier function (GCBF), which are based on the well-established control barrier function (CBF) theory for safety guarantees and utilize a graph structure for scalable and generalizable distributed control of MAS. We develop a novel theoretical framework to prove the safety of an arbitrary-sized MAS with a single GCBF. We propose a new training framework GCBF+ that uses graph neural networks (GNNs) to parameterize a candidate GCBF and a distributed control policy. The proposed framework is distributed and is capable of directly taking point clouds from LiDAR, instead of actual state information, for real-world robotic applications. We illustrate the efficacy of the proposed method through various hardware experiments on a swarm of drones with objectives ranging from exchanging positions to docking on a moving target without collision. Additionally, we perform extensive numerical experiments, where the number and density of agents, as well as the number of obstacles, increase. Empirical results show that in complex environments with nonlinear agents (e.g., Crazyflie drones) GCBF+ outperforms the handcrafted CBF-based method with the best performance by up to 20% for relatively small-scale MAS for up to 256 agents, and leading reinforcement learning (RL) methods by up to 40% for MAS with 1024 agents. Furthermore, the proposed method does not compromise on the performance, in terms of goal reaching, for achieving high safety rates, which is a common trade-off in RL-based methods.Comment: 18 pages, 12 figures, submitted to IEEE T-RO. arXiv admin note: text overlap with arXiv:2311.1301