7 citations · 10 across the 12 of their papers we have counts for
12 papers
Split over resource sharing problem: Are fewer capable agents better than many simpler ones?
Karthik Soma, Mohamed S. Talamali, Genki Miyauchi +3
In multi-agent systems, should limited resources be concentrated into a few capable agents or distributed among many simpler ones? This work formulates the split over resource…
Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
Maeva Guerrier, Karthik Soma, Jana Pavlasek +1
Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations. Despite growing real-world deployment, existing evaluati…
Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned
Pierre-Yves Lajoie, Karthik Soma, Haechan Mark Bong +4
Decentralized collaborative simultaneous localization and mapping (C-SLAM) is essential to enable multirobot missions in unknown environments without relying on preexisting localiz…
The Heterogeneous Multi-Agent Challenge
Charles Dansereau, Junior-Samuel Lopez-Yepez, Karthik Soma +1
Multi-Agent Reinforcement Learning (MARL) is a growing research area which gained significant traction in recent years, extending Deep RL applications to a much wider range of prob…
Guided by Guardrails: Control Barrier Functions as Safety Instructors for Robotic Learning
Maeva Guerrier, Karthik Soma, Hassan Fouad +1
Safety stands as the primary obstacle preventing the widespread adoption of learning-based robotic systems in our daily lives. While reinforcement learning (RL) shows promise as an…
The Hive Mind is a Single Reinforcement Learning Agent
Karthik Soma, Yann Bouteiller, Heiko Hamann +1
Decision-making is an essential attribute of any intelligent agent or group. Natural systems are known to converge to effective strategies through at least two distinct mechanisms:…
Evolution of Societies via Reinforcement Learning
Yann Bouteiller, Karthik Soma, Giovanni Beltrame
The universe involves many independent co-learning agents as an ever-evolving part of our observed environment. Yet, in practice, Multi-Agent Reinforcement Learning (MARL) applicat…
Hierarchies define the scalability of robot swarms
Vivek Shankar Varadharajan, Karthik Soma, Sepand Dyanatkar +2
The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarms are viewed as egalitarian, with robots…
Decentralized shape formation and force-based interactive formation control in robot swarms
Akshaya C S, Karthik Soma, Visweswaran B +2
Swarm robotic systems utilize collective behaviour to achieve goals that might be too complex for a lone entity, but become attainable with localized communication and collective d…
Congestion and Scalability in Robot Swarms: a Study on Collective Decision Making
Karthik Soma, Vivek Shankar Vardharajan, Heiko Hamann +1
One of the most important promises of decentralized systems is scalability, which is often assumed to be present in robot swarm systems without being contested. Simple limitations,…
A Complete Set of Connectivity-aware Local Topology Manipulation Operations for Robot Swarms
Koresh Khateri, Karthik Soma, Mahdi Pourgholi +3
The topology of a robotic swarm affects the convergence speed of consensus and the mobility of the robots. In this paper, we prove the existence of a complete set of local topology…
Collective transport via sequential caging
Vivek Shankar Vardharajan, Karthik Soma, Giovanni Beltrame
We propose a decentralized algorithm to collaboratively transport arbitrarily shaped objects using a swarm of robots. Our approach starts with a task allocation phase that sequenti…