36 citations · 86 across the 13 of their papers we have counts for
20 papers
Adaptive Sampling: Algorithmic vs. Human Waypoint Selection
Stephanie Kemna, Sara Kangaslahti, Oliver Kroemer +1
Robots are used for collecting samples from natural environments to create models of, for example, temperature or algae fields in the ocean. Adaptive informative sampling is a prov…
Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models
Saumya Saxena, Alex LaGrassa, Oliver Kroemer
Humans leverage the dynamics of the environment and their own bodies to accomplish challenging tasks such as grasping an object while walking past it or pushing off a wall to turn…
Generalizing Object-Centric Task-Axes Controllers using Keypoints
Mohit Sharma, Oliver Kroemer
To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. It is often unfeasible to trai…
Playing with Food: Learning Food Item Representations through Interactive Exploration
Amrita Sawhney, Steven Lee, Kevin Zhang +2
A key challenge in robotic food manipulation is modeling the material properties of diverse and deformable food items. We propose using a multimodal sensory approach to interact an…
Relational Learning for Skill Preconditions
Mohit Sharma, Oliver Kroemer
To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill. As robots begin to operate in dynamic and unstructured env…
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
Mohit Sharma, Jacky Liang, Jialiang Zhao +2
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual s…