5 accepted papers
Reinforcement Learning (RL) has been shown to be effective in many scenarios. However, it typically requires the exploration of a sufficiently large number of state-action pairs, some of which may be unsafe. Consequently, its application to safety-critical systems remains a challenge. An increasingl
Multi-robot task allocation is a ubiquitous problem in robotics due to its applicability in a variety of scenarios. Adaptive task-allocation algorithms account for unknown disturbances and unpredicted phenomena in the environment where robots are deployed to execute tasks. However, this adaptivity t…
The cost, in terms of both time and money, of instantiating a physical testbed can be prohibitive. To help resolve this issue, the Robotarium offers a free, remotely accessible robotics lab to users around the world. Since allowing the general public to use it, hundreds of users have submitted thous
For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot…
This letter presents the SlothBot, a wire-traversing robot envisioned for long-term environmental monitoring applications. The SlothBot is a solar-powered, slow-paced, energy-efficient robot-hence its name-capable of moving on a mesh of wires by switching between branching wires. Unlike ground mobil