2 citations · 3 across the 8 of their papers we have counts for
8 papers
Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning
Yoonwoo Kim, Raghav Arora, Roberto Martín-Martín +3
Robot planning in partially observable environments, where not all objects are known or visible, is a challenging problem, as it requires reasoning under uncertainty through partia…
CAVER: Curious Audiovisual Exploring Robot
Luca Macesanu, Boueny Folefack, Samik Singh +3
Multimodal audiovisual perception can enable new avenues for robotic manipulation, from better material classification to the imitation of demonstrations for which only audio signa…
SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation
Arpit Bahety, Arnav Balaji, Ben Abbatematteo +1
For robots to become efficient helpers in the home, they must learn to perform new mobile manipulation tasks simply by watching humans perform them. Learning from a single video de…
Learning to Look: Seeking Information for Decision Making via Policy Factorization
Shivin Dass, Jiaheng Hu, Ben Abbatematteo +2
Many robot manipulation tasks require active or interactive exploration behavior in order to be performed successfully. Such tasks are ubiquitous in embodied domains, where agents…
KinScene: Model-Based Mobile Manipulation of Articulated Scenes
Cheng-Chun Hsu, Ben Abbatematteo, Zhenyu Jiang +3
Sequentially interacting with articulated objects is crucial for a mobile manipulator to operate effectively in everyday environments. To enable long-horizon tasks involving articu…
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang, Ben Abbatematteo, Jiaheng Hu +3
Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications,…
ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection
Arpit Bahety, Priyanka Mandikal, Ben Abbatematteo +1
Bimanual manipulation is a longstanding challenge in robotics due to the large number of degrees of freedom and the strict spatial and temporal synchronization required to generate…
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
Shivin Dass, Wensi Ai, Yuqian Jiang +6
A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder tha…