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Found 6 papers, 4 papers with code
SUGAR: Pre-training 3D Visual Representations for Robotics
SUGAR employs a versatile transformer-based model to jointly address five pre-training tasks, namely cross-modal knowledge distillation for semantic learning, masked point modeling to understand geometry structures, grasping pose synthesis for object affordance, 3D instance segmentation and referring expression grounding to analyze cluttered scenes.
PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation
The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics.
Ranked #5 on
Robot Manipulation
on RLBench
Robust Visual Sim-to-Real Transfer for Robotic Manipulation
While previous work mainly evaluates DR for disembodied tasks, such as pose estimation and object detection, here we systematically explore visual domain randomization methods and benchmark them on a rich set of challenging robotic manipulation tasks.
Instruction-driven history-aware policies for robotic manipulations
In human environments, robots are expected to accomplish a variety of manipulation tasks given simple natural language instructions.
Ranked #2 on
Robot Manipulation
on RLBench
(Succ. Rate (10 tasks, 100 demos/task) metric)
Segmenter: Transformer for Semantic Segmentation
In this paper we introduce Segmenter, a transformer model for semantic segmentation.
Ranked #15 on
Semantic Segmentation
on PASCAL Context
Learning Obstacle Representations for Neural Motion Planning
Motion planning and obstacle avoidance is a key challenge in robotics applications.
Robotics
