Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Kantaros, Yiannis"'
In this paper, we consider teams of robots with heterogeneous skills (e.g., sensing and manipulation) tasked with collaborative missions described by Linear Temporal Logic (LTL) formulas. These LTL-encoded tasks require robots to apply their skills t
Externí odkaz:
http://arxiv.org/abs/2410.17188
Autor:
Kantaros, Yiannis, Wang, Jun
This paper addresses the problem of learning optimal control policies for systems with uncertain dynamics and high-level control objectives specified as Linear Temporal Logic (LTL) formulas. Uncertainty is considered in the workspace structure and th
Externí odkaz:
http://arxiv.org/abs/2410.12136
This paper addresses task planning problems for language-instructed robot teams. Tasks are expressed in natural language (NL), requiring the robots to apply their capabilities at various locations and semantic objects. Several recent works have addre
Externí odkaz:
http://arxiv.org/abs/2402.15368
Autor:
Mitta, Rohan, Hasanbeig, Hosein, Wang, Jun, Kroening, Daniel, Kantaros, Yiannis, Abate, Alessandro
This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL), such that the safety constraint violations are bounded at any point during learning. In a variety of RL applications the safety of the agent is par
Externí odkaz:
http://arxiv.org/abs/2312.11314
Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents, with unknown and stochastic dynamics, tasked with complex missions captured by Linear Temporal Logic (LTL). Due to the sample-inefficiency of
Externí odkaz:
http://arxiv.org/abs/2311.10863
This paper addresses planning problems for mobile robots. We consider missions that require accomplishing multiple high-level sub-tasks, expressed in natural language (NL), in a temporal and logical order. To formally define the mission, we treat the
Externí odkaz:
http://arxiv.org/abs/2309.10092
Uncertainty-bounded Active Monitoring of Unknown Dynamic Targets in Road-networks with Minimum Fleet
Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dynamic targets in a collaborative and efficient way is a challenging prob
Externí odkaz:
http://arxiv.org/abs/2309.08322
While ensuring stability for linear systems is well understood, it remains a major challenge for nonlinear systems. A general approach in such cases is to compute a combination of a Lyapunov function and an associated control policy. However, finding
Externí odkaz:
http://arxiv.org/abs/2305.06547
Several task and motion planning algorithms have been proposed recently to design paths for mobile robot teams with collaborative high-level missions specified using formal languages, such as Linear Temporal Logic (LTL). However, the designed paths o
Externí odkaz:
http://arxiv.org/abs/2305.05485
Deep neural networks (DNN) have become a common sensing modality in autonomous systems as they allow for semantically perceiving the ambient environment given input images. Nevertheless, DNN models have proven to be vulnerable to adversarial digital
Externí odkaz:
http://arxiv.org/abs/2304.13919