activity
20092023
most citedAdditive-Decomposition-Based Output Feedback Tracking Control for Systems with Measurable Nonlinearities and Unknown Disturbances

45 citations · 205 across the 65 of their papers we have counts for

collaborators
Showing 2017Show all

8 papers · 1 filter

cs.RO2017★ 1 cited

Reactive Integrated Mission and Motion planning

Alireza Partovi, Rafael Rodrigues da Silva, Hai Lin

Correct-by-construction manipulation planning in a dynamic environment, where other agents can manipulate objects in the workspace, is a challenging problem. The tight coupling of…

cs.RO2017★ 9 cited

A Learning Based Optimal Human Robot Collaboration with Linear Temporal Logic Constraints

Bo Wu, Bin Hu, Hai Lin

This paper considers an optimal task allocation problem for human robot collaboration in human robot systems with persistent tasks. Such human robot systems consist of human operat…

eess.SY2017★ 2 cited

Communication-aware Motion Planning for Multi-agent Systems from Signal Temporal Logic Specifications

Zhiyu Liu, Jin Dai, Bo Wu +1

We propose a mathematical framework for synthesizing motion plans for multi-agent systems that fulfill complex, high-level and formal local specifications in the presence of inter-…

eess.SY2017

Learning-based Formal Synthesis of Cooperative Multi-agent Systems

Jin Dai, Alessandro Benini, Hai Lin +3

We propose a formal design framework for synthesizing coordination and control policies for cooperative multi-agent systems to accomplish a global mission. The global performance r…

cs.MA2017★ 1 cited

Distributed Communication-aware Motion Planning for Multi-agent Systems from STL and SpaTeL Specifications

Zhiyu Liu, Bo Wu, Jin Dai +1

In future intelligent transportation systems, networked vehicles coordinate with each other to achieve safe operations based on an assumption that communications among vehicles and…

eess.SY2017★ 4 cited

Supervisor Synthesis of POMDP based on Automata Learning

Xiaobin Zhang, Bo Wu, Hai Lin

As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noise…