4 papers
More Structure, Not More Capacity: Object-Centric Representations for Visuomotor Imitation Learning
Yi Li, Alexandre Chapin, Liming Chen +2
Robotic manipulation policies rely on pre-trained vision models that give either a global scene embedding or a dense patch grid. Both mix task-relevant and task-irrelevant features…
Apple: Toward General Active Perception via Reinforcement Learning
Tim Schneider, Cristiana de Farias, Roberto Calandra +2
Active perception is a fundamental skill that enables us humans to deal with uncertainty in our inherently partially observable environment. For senses such as touch, where the inf…
SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics
Mengzhen Liu, Enshen Zhou, Cheng Chi +6
Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven active perception with robust, viewpoi…
Tactile MNIST: Benchmarking Active Tactile Perception
Tim Schneider, Guillaume Duret, Cristiana de Farias +3
Tactile perception has the potential to significantly enhance dexterous robotic manipulation by providing rich local information that can complement or substitute for other sensory…