6 accepted papers
Solving the Simultaneous Localization and Mapping (SLAM) problem is essential for most mobile robotics applications that do not have an a priori environment representation. The SLAM problem is well-studied, with previous works demonstrating impressive results with a variety of robots, sensors, and e
Performing a manipulation contact task in an unknown and unstructured environment is still a challenge. Learning from Demonstration (LfD) techniques provide an intuitive means to define difficult-to-model contact tasks, but have attributes that make them undesirable for novice users in uncertain env…
Recent studies utilizing Affordance Templates to perform remote contact manipulation tasks with mobile manipulators have demonstrated their usefulness for modeling complex tasks allowing robots to work in uncertain environments. These efforts largely fall into the “supervised autonomy” paradigm wher
Affordance models are widely used in robotics to represent a robot's possible interactions with its environment. However, robot affordance models are inherently quantitative, making them difficult for humans to understand and interact with. To address this problem, previous works have constructed af…
Using robotic manipulators to remotely perform real-world complex contact tasks is challenging whether tasks are known (due to uncertainty) or unknown a priori (lack of motion waypoints, force profiles, etc.). For known tasks we can integrate and utilize Affordance Templates with a selective complia…
As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This pape