45 citations · 47 across the 11 of their papers we have counts for
10 papers · 1 filter
Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers
Snehal Jauhri, Vignesh Prasad, Georgia Chalvatzaki
Mobile Manipulation (MoMa) of articulated objects, such as opening doors, drawers, and cupboards, demands simultaneous, whole-body coordination between a robot's base and arms. Cla…
MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
Abhay Deshpande, Maya Guru, Rose Hendrix +23
A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or…
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Yejin Kim, Wilbert Pumacay, Omar Rayyan +23
Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that charac…
UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene
Christian Maurer, Snehal Jauhri, Sophie Lueth +1
Comprehensive visual, geometric, and semantic understanding of a 3D scene is crucial for successful execution of robotic tasks, especially in unstructured and complex environments.…
Active-Perceptive Motion Generation for Mobile Manipulation
Snehal Jauhri, Sophie Lueth, Georgia Chalvatzaki
Mobile Manipulation (MoMa) systems incorporate the benefits of mobility and dexterity, due to the enlarged space in which they can move and interact with their environment. However…
Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering
Snehal Jauhri, Ishikaa Lunawat, Georgia Chalvatzaki
A significant challenge for real-world robotic manipulation is the effective 6DoF grasping of objects in cluttered scenes from any single viewpoint without the need for additional…
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction
Puze Liu, Kuo Zhang, Davide Tateo +4
Safety is a crucial property of every robotic platform: any control policy should always comply with actuator limits and avoid collisions with the environment and humans. In reinfo…
Regularized Deep Signed Distance Fields for Reactive Motion Generation
Puze Liu, Kuo Zhang, Davide Tateo +3
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and man…
Robot Learning of Mobile Manipulation with Reachability Behavior Priors
Snehal Jauhri, Jan Peters, Georgia Chalvatzaki
Mobile Manipulation (MM) systems are ideal candidates for taking up the role of a personal assistant in unstructured real-world environments. Among other challenges, MM requires ef…
Interactive Imitation Learning in State-Space
Snehal Jauhri, Carlos Celemin, Jens Kober
Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available…