4 citations · 6 across the 3 of their papers we have counts for
7 papers
LLaMAR: Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have +10
The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditio…
Satellite Navigation and Coordination with Limited Information Sharing
Sydney Dolan, Siddharth Nayak, Hamsa Balakrishnan
We explore space traffic management as an application of collision-free navigation in multi-agent systems where vehicles have limited observation and communication ranges. We inves…
Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
Siddharth Nayak, Kenneth Choi, Wenqi Ding +3
We consider the problem of multi-agent navigation and collision avoidance when observations are limited to the local neighborhood of each agent. We propose InforMARL, a novel archi…
Routing with Privacy for Drone Package Delivery Systems
Geoffrey Ding, Alex Berke, Karthik Gopalakrishnan +3
Unmanned aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. To safely conduct these operations at scale, drones are required…
NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming
Luke Kenworthy, Siddharth Nayak, Christopher Chin +1
Integer programs provide a powerful abstraction for representing a wide range of real-world scheduling problems. Despite their ability to model general scheduling problems, solving…
When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes
Devansh Jalota, Kiril Solovey, Karthik Gopalakrishnan +3
Congestion pricing has long been hailed as a means to mitigate traffic congestion; however, its practical adoption has been limited due to the resulting social inequity issue, e.g.…