43 research outputs found
Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks
Autonomous robots are increasingly utilized in realistic scenarios with
multiple complex tasks. In these scenarios, there may be a preferred way of
completing all of the given tasks, but it is often in conflict with optimal
execution. Recent work studies preference-based planning, however, they have
yet to extend the notion of preference to the behavior of the robot with
respect to each task. In this work, we introduce a novel notion of preference
that provides a generalized framework to express preferences over individual
tasks as well as their relations. Then, we perform an optimal trade-off
(Pareto) analysis between behaviors that adhere to the user's preference and
the ones that are resource optimal. We introduce an efficient planning
framework that generates Pareto-optimal plans given user's preference by
extending A* search. Further, we show a method of computing the entire Pareto
front (the set of all optimal trade-offs) via an adaptation of a
multi-objective A* algorithm. We also present a problem-agnostic search
heuristic to enable scalability. We illustrate the power of the framework on
both mobile robots and manipulators. Our benchmarks show the effectiveness of
the heuristic with up to 2-orders of magnitude speedup.Comment: 8 pages, 4 figures, to appear in International Conference on
Intelligent Robots and Systems (IROS) 202
Efficient Symbolic Approaches for Quantitative Reactive Synthesis with Finite Tasks
This work introduces efficient symbolic algorithms for quantitative reactive
synthesis. We consider resource-constrained robotic manipulators that need to
interact with a human to achieve a complex task expressed in linear temporal
logic. Our framework generates reactive strategies that not only guarantee task
completion but also seek cooperation with the human when possible. We model the
interaction as a two-player game and consider regret-minimizing strategies to
encourage cooperation. We use symbolic representation of the game to enable
scalability. For synthesis, we first introduce value iteration algorithms for
such games with min-max objectives. Then, we extend our method to the
regret-minimizing objectives. Our benchmarks reveal that our symbolic framework
not only significantly improves computation time (up to an order of magnitude)
but also can scale up to much larger instances of manipulation problems with up
to 2x number of objects and locations than the state of the art.Comment: Submitted to IROS 202
Promises of Deep Kernel Learning for Control Synthesis
Deep Kernel Learning (DKL) combines the representational power of neural
networks with the uncertainty quantification of Gaussian Processes. Hence, it
is potentially a promising tool to learn and control complex dynamical systems.
In this work, we develop a scalable abstraction-based framework that enables
the use of DKL for control synthesis of stochastic dynamical systems against
complex specifications. Specifically, we consider temporal logic specifications
and create an end-to-end framework that uses DKL to learn an unknown system
from data and formally abstracts the DKL model into an Interval Markov Decision
Process (IMDP) to perform control synthesis with correctness guarantees.
Furthermore, we identify a deep architecture that enables accurate learning and
efficient abstraction computation. The effectiveness of our approach is
illustrated on various benchmarks, including a 5-D nonlinear stochastic system,
showing how control synthesis with DKL can substantially outperform
state-of-the-art competitive methods.Comment: 9 pages, 4 figures, 3 table
Correct-by-Construction Advanced Driver Assistance Systems based on a Cognitive Architecture
Research into safety in autonomous and semi-autonomous vehicles has, so far,
largely been focused on testing and validation through simulation. Due to the
fact that failure of these autonomous systems is potentially life-endangering,
formal methods arise as a complementary approach. This paper studies the
application of formal methods to the verification of a human driver model built
using the cognitive architecture ACT-R, and to the design of
correct-by-construction Advanced Driver Assistance Systems (ADAS). The novelty
lies in the integration of ACT-R in the formal analysis and an abstraction
technique that enables finite representation of a large dimensional, continuous
system in the form of a Markov process. The situation considered is a
multi-lane highway driving scenario and the interactions that arise. The
efficacy of the method is illustrated in two case studies with various driving
conditions.Comment: Proceedings at IEEE CAVS 201
Pareto Optimal Strategies for Event Triggered Estimation
Although resource-limited networked autonomous systems must be able to
efficiently and effectively accomplish tasks, better conservation of resources
often results in worse task performance. We specifically address the problem of
finding strategies for managing measurement communication costs between agents.
A well understood technique for trading off communication costs with estimation
accuracy is event triggering (ET), where measurements are only communicated
when useful, e.g., when Kalman filter innovations exceed some threshold. In the
absence of measurements, agents can use implicit information to achieve results
almost as well as when explicit data is always communicated. However, there are
no methods for setting this threshold with formal guarantees on task
performance. We fill this gap by developing a novel belief space discretization
technique to abstract a continuous space dynamics model for ET estimation to a
discrete Markov decision process, which scalably accommodates
threshold-sensitive ET estimator error covariances. We then apply an existing
probabilistic trade-off analysis tool to find the set of all optimal trade-offs
between resource consumption and task performance. From this set, an ET
threshold selection strategy is extracted. Simulated results show our approach
identifies non-trivial trade-offs between performance and energy savings, with
only modest computational effort.Comment: 8 pages, accepted to IEEE Conference on Decision and Control 202
Probabilistically safe vehicle control in a hostile environment
In this paper we present an approach to control a vehicle in a hostile environment with static obstacles and moving adversaries. The vehicle is required to satisfy a mission objective expressed as a temporal logic specification over a set of properties satisfied at regions of a partitioned environment. We model the movements of adversaries in between regions of the environment as Poisson processes. Furthermore, we assume that the time it takes for the vehicle to traverse in between two facets of each region is exponentially distributed, and we obtain the rate of this exponential distribution from a simulator of the environment. We capture the motion of the vehicle and the vehicle updates of adversaries distributions as a Markov Decision Process. Using tools in Probabilistic Computational Tree Logic, we find a control strategy for the vehicle that maximizes the probability of accomplishing the mission objective. We demonstrate our approach with illustrative case studies
Formal Abstraction of General Stochastic Systems via Noise Partitioning
Verifying the performance of safety-critical, stochastic systems with complex
noise distributions is difficult. We introduce a general procedure for the
finite abstraction of nonlinear stochastic systems with non-standard (e.g.,
non-affine, non-symmetric, non-unimodal) noise distributions for verification
purposes. The method uses a finite partitioning of the noise domain to
construct an interval Markov chain (IMC) abstraction of the system via
transition probability intervals. Noise partitioning allows for a general class
of distributions and structures, including multiplicative and mixture models,
and admits both known and data-driven systems. The partitions required for
optimal transition bounds are specified for systems that are monotonic with
respect to the noise, and explicit partitions are provided for affine and
multiplicative structures. By the soundness of the abstraction procedure,
verification on the IMC provides guarantees on the stochastic system against a
temporal logic specification. In addition, we present a novel refinement-free
algorithm that improves the verification results. Case studies on linear and
nonlinear systems with non-Gaussian noise, including a data-driven example,
demonstrate the generality and effectiveness of the method without introducing
excessive conservatism.Comment: 6 pages, 6 figures, submitted jointly to IEEE Control Systems Letters
and 2024 AC
Introducing Delays in Multi-Agent Path Finding
We consider a Multi-Agent Path Finding (MAPF) setting where agents have been
assigned a plan, but during its execution some agents are delayed. Instead of
replanning from scratch when such a delay occurs, we propose delay
introduction, whereby we delay some additional agents so that the remainder of
the plan can be executed safely. We show that the corresponding decision
problem is NP-Complete in general. However, in practice we can find optimal
delay-introductions using CBS for very large numbers of agents, and both
planning time and the resulting length of the plan are comparable, and
sometimes outperform, the state-of-the-art heuristics for replanning. We also
examine the benefits of our method from an explainability point of view.Comment: 10 pages, 8 figures, and 2 table
Stochastic Robustness Interval for Motion Planning with Signal Temporal Logic
In this work, we present a novel robustness measure for continuous-time
stochastic trajectories with respect to Signal Temporal Logic (STL)
specifications. We show the soundness of the measure and develop a monitor for
reasoning about partial trajectories. Using this monitor, we introduce an STL
sampling-based motion planning algorithm for robots under uncertainty. Given a
minimum robustness requirement, this algorithm finds satisfying motion plans;
alternatively, the algorithm also optimizes for the measure. We prove
probabilistic completeness and asymptotic optimality, and demonstrate the
effectiveness of our approach on several case studies