12 citations · 23 across the 5 of their papers we have counts for
6 papers
SUGAR: Pre-training 3D Visual Representations for Robotics
Shizhe Chen, Ricardo Garcia, Ivan Laptev +1
Learning generalizable visual representations from Internet data has yielded promising results for robotics. Yet, prevailing approaches focus on pre-training 2D representations, be…
PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation
Shizhe Chen, Ricardo Garcia, Cordelia Schmid +1
The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics. The dominant approaches for language-gui…
Robust Visual Sim-to-Real Transfer for Robotic Manipulation
Ricardo Garcia, Robin Strudel, Shizhe Chen +3
Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained po…
Instruction-driven history-aware policies for robotic manipulations
Pierre-Louis Guhur, Shizhe Chen, Ricardo Garcia +3
In human environments, robots are expected to accomplish a variety of manipulation tasks given simple natural language instructions. Yet, robotic manipulation is extremely challeng…
Segmenter: Transformer for Semantic Segmentation
Robin Strudel, Ricardo Garcia, Ivan Laptev +1
Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a…
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…