1 citations · 1 across the 4 of their papers we have counts for
5 papers
PolyUMI: Accessible Visual-Tactile-Audio Data Collection for Object Inference and Manipulation
Conor W. Hayes, Rickmer Krohn, Aravind Ramaswami +6
Humans typically rely on vision, touch, hearing, and proprioception to perceive contact and adapt their actions during manipulation. Providing robots with comparable responsiveness…
Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
Rickmer Krohn, Erik Helmut, Niklas Funk +3
Touch sensing is beneficial for solving a wide variety of manipulation tasks. While there exists a wide range of tactile sensors with different properties, exploiting the fusion of…
Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
Rickmer Krohn, Vignesh Prasad, Gabriele Tiboni +1
Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such…
The Role of Embodiment in Intuitive Whole-Body Teleoperation for Mobile Manipulation
Sophia Bianchi Moyen, Rickmer Krohn, Sophie Lueth +4
Intuitive Teleoperation interfaces are essential for mobile manipulation robots to ensure high quality data collection while reducing operator workload. A strong sense of embodimen…
Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient
Zechu Li, Rickmer Krohn, Tao Chen +3
Deep reinforcement learning (RL) algorithms typically parameterize the policy as a deep network that outputs either a deterministic action or a stochastic one modeled as a Gaussian…