5 citations · 5 across the 12 of their papers we have counts for
12 papers
SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control
Varun Raveendra, Connor Mattson, Daniel S. Brown
Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, little research explores how agen…
What Is My Robot Thinking? Design Considerations for Transparent and Trustworthy Shared Autonomy
Atharv Belsare, Zohre Karimi, Connor Mattson +2
Assistive robots operating under shared autonomy must balance user control with autonomous assistance. Because robot actions depend on internal intent inference that is not directl…
Analytical Swarm Chemistry: Characterization and Analysis of Emergent Swarm Behaviors
Ricardo Vega, Connor Mattson, Kevin Zhu +2
Swarm robotics has potential for a wide variety of applications, but real-world deployments remain rare due to the difficulty of predicting emergent behaviors arising from simple l…
R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
Connor Mattson, Varun Raveendra, Ellen Novoseller +3
Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-ro…
Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer
Connor Mattson, Varun Raveendra, Ricardo Vega +3
Given a swarm of limited-capability robots, we seek to automatically discover the set of possible emergent behaviors. Prior approaches to behavior discovery rely on human feedback…
Toward Zero-Shot User Intent Recognition in Shared Autonomy
Atharv Belsare, Zohre Karimi, Connor Mattson +1
A fundamental challenge of shared autonomy is to use high-DoF robots to assist, rather than hinder, humans by first inferring user intent and then empowering the user to achieve th…
Agent-Based Emulation for Deploying Robot Swarm Behaviors
Ricardo Vega, Kevin Zhu, Connor Mattson +2
Despite significant research, robotic swarms have yet to be useful in solving real-world problems, largely due to the difficulty of creating and controlling swarming behaviors in m…
Spiking Neural Networks as a Controller for Emergent Swarm Agents
Kevin Zhu, Connor Mattson, Shay Snyder +4
Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, res…
Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations
Connor Mattson, Anurag Aribandi, Daniel S. Brown
We study the problem of cross-embodiment inverse reinforcement learning, where we wish to learn a reward function from video demonstrations in one or more embodiments and then tran…
Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots
Connor Mattson, Jeremy C. Clark, Daniel S. Brown
We study the problem of determining the emergent behaviors that are possible given a functionally heterogeneous swarm of robots with limited capabilities. Prior work has considered…
Indirect Swarm Control: Characterization and Analysis of Emergent Swarm Behaviors
Ricardo Vega, Connor Mattson, Daniel S. Brown +1
Emergence and emergent behaviors are often defined as cases where changes in local interactions between agents at a lower level effectively changes what occurs in the higher level…
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms
Connor Mattson, Daniel S. Brown
Robot swarms often exhibit emergent behaviors that are fascinating to observe; however, it is often difficult to predict what swarm behaviors can emerge under a given set of agent…