234 citations · 712 across the 22 of their papers we have counts for
4 papers · 1 filter
Learning Object Manipulation Skills via Approximate State Estimation from Real Videos
Vladimír Petrík, Makarand Tapaswi, Ivan Laptev +1
Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and err…
Learning Obstacle Representations for Neural Motion Planning
Robin Strudel, Ricardo Garcia, Justin Carpentier +3
Motion planning and obstacle avoidance is a key challenge in robotics applications. While previous work succeeds to provide excellent solutions for known environments, sensor-based…
Learning visual policies for building 3D shape categories
Alexander Pashevich, Igor Kalevatykh, Ivan Laptev +1
Manipulation and assembly tasks require non-trivial planning of actions depending on the environment and the final goal. Previous work in this domain often assembles particular ins…
Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning
Yann Labbé, Sergey Zagoruyko, Igor Kalevatykh +4
We address the problem of visually guided rearrangement planning with many movable objects, i.e., finding a sequence of actions to move a set of objects from an initial arrangement…