Search Results for author:
Found 7 papers, 1 papers with code
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments.
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.
Generalized Conflict-directed Search for Optimal Ordering Problems
In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning.
Optimal Mixed Discrete-Continuous Planningfor Linear Hybrid Systems
Planning in hybrid systems with both discrete and continuous control variables is important for dealing with real-world applications such as extra-planetary exploration and multi-vehicle transportation systems.
Continuous Control
Robotics
Systems and Control
Systems and Control
Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates
We propose a novel joint-learning framework that can be implemented in a decentralized fashion, with generalization guarantees for certain function classes.
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances.
Motion Planning
Robotics
Multiagent Systems
Efficiently Exploring Ordering Problems through Conflict-directed Search
In this paper, we present Conflict-directed Incremental Total Ordering (CDITO), a conflict-directed search method to incrementally and systematically generate event total orders given ordering relations and conflicts returned by sub-solvers.
