5 papers
PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations
Anmol Gupta, Weiwei Gu, Omkar Patil +2
PokeNet is an end-to-end system that learns the kinematic models of unknown articulated objects from a single human demonstration, predicting joint parameters, manipulation order,…
ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning
Byung-ju Kim, Jinu Pahk, Chungwoo Lee +6
Behavior-cloning based visuomotor policies enable precise manipulation but often inherit the slow, cautious tempo of human demonstrations, limiting practical deployment. However, p…
Habilis-: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model
Tommoro Robotics, :, Jesoon Kang +22
We introduce Habilis-, a fast-motion and long-lasting on-device vision-language-action (VLA) model designed for real-world deployment. Current VLA evaluation remains largely co…
CLIP-RT: Learning Language-Conditioned Robotic Policies from Natural Language Supervision
Gi-Cheon Kang, Junghyun Kim, Kyuhwan Shim +2
Teaching robots desired skills in real-world environments remains challenging, especially for non-experts. A key bottleneck is that collecting robotic data often requires expertise…
Learning Sequential Kinematic Models from Demonstrations for Multi-Jointed Articulated Objects
Anmol Gupta, Weiwei Gu, Omkar Patil +2
As robots become more generalized and deployed in diverse environments, they must interact with complex objects, many with multiple independent joints or degrees of freedom (DoF) r…