46 research outputs found
Robust Temporal Logic Model Predictive Control
Control synthesis from temporal logic specifications has gained popularity in
recent years. In this paper, we use a model predictive approach to control
discrete time linear systems with additive bounded disturbances subject to
constraints given as formulas of signal temporal logic (STL). We introduce a
(conservative) computationally efficient framework to synthesize control
strategies based on mixed integer programs. The designed controllers satisfy
the temporal logic requirements, are robust to all possible realizations of the
disturbances, and optimal with respect to a cost function. In case the temporal
logic constraint is infeasible, the controller satisfies a relaxed, minimally
violating constraint. An illustrative case study is included.Comment: This work has been accepted to appear in the proceedings of 53rd
Annual Allerton Conference on Communication, Control and Computing,
Urbana-Champaign, IL (2015
Robust Motion Planning employing Signal Temporal Logic
Motion planning classically concerns the problem of accomplishing a goal
configuration while avoiding obstacles. However, the need for more
sophisticated motion planning methodologies, taking temporal aspects into
account, has emerged. To address this issue, temporal logics have recently been
used to formulate such advanced specifications. This paper will consider Signal
Temporal Logic in combination with Model Predictive Control. A robustness
metric, called Discrete Average Space Robustness, is introduced and used to
maximize the satisfaction of specifications which results in a natural
robustness against noise. The comprised optimization problem is convex and
formulated as a Linear Program.Comment: 6 page
Automata guided hierarchical reinforcement learning for zero-shot skill composition
An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large amount of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new task. We present a framework that combines methods in formal methods with hierarchical reinforcement learning (HRL). The set of techniques we provide allows for convenient specification of tasks with complex logic, learn hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards using any RL methods and is able to construct new skills from existing ones without additional learning. We evaluate the proposed methods in a simple grid world simulation as well as simulation on a Baxter robot
Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks
Signal temporal logic (STL) provides a user-friendly interface for defining
complex tasks for robotic systems. Recent efforts aim at designing control laws
or using reinforcement learning methods to find policies which guarantee
satisfaction of these tasks. While the former suffer from the trade-off between
task specification and computational complexity, the latter encounter
difficulties in exploration as the tasks become more complex and challenging to
satisfy. This paper proposes to combine the benefits of the two approaches and
use an efficient prescribed performance control (PPC) base law to guide
exploration within the reinforcement learning algorithm. The potential of the
method is demonstrated in a simulated environment through two sample
navigational tasks.Comment: This is the extended version of the paper accepted to the 2019
American Control Conference (ACC), Philadelphia (to be published
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks
Reinforcement learning has been applied to many interesting problems such as
the famous TD-gammon and the inverted helicopter flight. However, little effort
has been put into developing methods to learn policies for complex persistent
tasks and tasks that are time-sensitive. In this paper, we take a step towards
solving this problem by using signal temporal logic (STL) as task
specification, and taking advantage of the temporal abstraction feature that
the options framework provide. We show via simulation that a relatively easy to
implement algorithm that combines STL and options can learn a satisfactory
policy with a small number of training case
A hierarchical reinforcement learning method for persistent time-sensitive tasks
Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper, we take a step towards solving this problem by using signal temporal logic (STL) as task specification, and taking advantage of the temporal abstraction feature that the options framework provide. We show via simulation that a relatively easy to implement algorithm that combines STL and options can learn a satisfactory policy with a small number of training cases
Prescribed Performance Control for Signal Temporal Logic Specifications
Motivated by the recent interest in formal methods-based control for dynamic
robots, we discuss the applicability of prescribed performance control to
nonlinear systems subject to signal temporal logic specifications. Prescribed
performance control imposes a desired transient behavior on the system
trajectories that is leveraged to satisfy atomic signal temporal logic
specifications. A hybrid control strategy is then used to satisfy a finite set
of these atomic specifications. Simulations of a multi-agent system, using
consensus dynamics, show that a wide range of specifications, i.e., formation,
sequencing, and dispersion, can be robustly satisfied.Comment: 9 pages - this an extended version of the 56th IEEE Conference on
Decision and Control (2017) versio