63,771 research outputs found
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
On-Line Monitoring for Temporal Logic Robustness
In this paper, we provide a Dynamic Programming algorithm for on-line
monitoring of the state robustness of Metric Temporal Logic specifications with
past time operators. We compute the robustness of MTL with unbounded past and
bounded future temporal operators MTL over sampled traces of Cyber-Physical
Systems. We implemented our tool in Matlab as a Simulink block that can be used
in any Simulink model. We experimentally demonstrate that the overhead of the
MTL robustness monitoring is acceptable for certain classes of practical
specifications
Reinforcement Learning With Temporal Logic Rewards
Reinforcement learning (RL) depends critically on the choice of reward
functions used to capture the de- sired behavior and constraints of a robot.
Usually, these are handcrafted by a expert designer and represent heuristics
for relatively simple tasks. Real world applications typically involve more
complex tasks with rich temporal and logical structure. In this paper we take
advantage of the expressive power of temporal logic (TL) to specify complex
rules the robot should follow, and incorporate domain knowledge into learning.
We propose Truncated Linear Temporal Logic (TLTL) as specifications language,
that is arguably well suited for the robotics applications, together with
quantitative semantics, i.e., robustness degree. We propose a RL approach to
learn tasks expressed as TLTL formulae that uses their associated robustness
degree as reward functions, instead of the manually crafted heuristics trying
to capture the same specifications. We show in simulated trials that learning
is faster and policies obtained using the proposed approach outperform the ones
learned using heuristic rewards in terms of the robustness degree, i.e., how
well the tasks are satisfied. Furthermore, we demonstrate the proposed RL
approach in a toast-placing task learned by a Baxter robot
Multi-Agent Robust Control Synthesis from Global Temporal Logic Tasks
This paper focuses on the heterogeneous multi-agent control problem under
global temporal logic tasks. We define a specification language, called
extended capacity temporal logic (ECaTL), to describe the required global
tasks, including the number of times that a local or coupled signal temporal
logic (STL) task needs to be satisfied and the synchronous requirements on task
satisfaction. The robustness measure for ECaTL is formally designed. In
particular, the robustness for synchronous tasks is evaluated from both the
temporal and spatial perspectives. Mixed-integer linear constraints are
designed to encode ECaTL specifications, and a two-step optimization framework
is further proposed to realize task-satisfied motion planning with high spatial
robustness and synchronicity. Simulations are conducted to demonstrate the
expressivity of ECaTL and the efficiency of the proposed control synthesis
approach.Comment: 7 pages, 3 figure
Robust Multi-Agent Coordination from CaTL+ Specifications
We consider the problem of controlling a heterogeneous multi-agent system
required to satisfy temporal logic requirements. Capability Temporal Logic
(CaTL) was recently proposed to formalize such specifications for deploying a
team of autonomous agents with different capabilities and cooperation
requirements. In this paper, we extend CaTL to a new logic CaTL+, which is more
expressive than CaTL and has semantics over a continuous workspace shared by
all agents. We define two novel robustness metrics for CaTL+: the traditional
robustness and the exponential robustness. The latter is sound, differentiable
almost everywhere and eliminates masking, which is one of the main limitations
of the traditional robustness metric. We formulate a control synthesis problem
to maximize CaTL+ robustness and propose a two-step optimization method to
solve this problem. Simulation results are included to illustrate the increased
expressivity of CaTL+ and the efficacy of the proposed control synthesis
approach.Comment: Submitted to ACC 202
Robust satisfaction of temporal logic specifications via reinforcement learning
We consider the problem of steering a system with unknown, stochastic dynamics to satisfy a rich, temporally-layered task given as a signal temporal logic formula. We represent the system as a finite-memory Markov decision process with unknown transition probabilities and whose states are built from a partition of the state space. We present provably convergent reinforcement learning algorithms to maximize the probability of satisfying a given specification and to maximize the average expected robustness, i.e. a measure of how strongly the formula is satisfied. Robustness allows us to quantify progress towards satisfying a given specification. We demonstrate via a pair of robot navigation simulation case studies that, due to the quantification of progress towards satisfaction, reinforcement learning with robustness maximization performs better than probability maximization in terms of both probability of satisfaction and expected robustness with a low number of training examples
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