90 citations · 99 across the 4 of their papers we have counts for
8 papers · 1 filter
Open-vocabulary Queryable Scene Representations for Real World Planning
Boyuan Chen, Fei Xia, Brian Ichter +5
Large language models (LLMs) have unlocked new capabilities of task planning from human instructions. However, prior attempts to apply LLMs to real-world robotic tasks are limited…
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
Eric Jang, Alex Irpan, Mohi Khansari +5
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach th…
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
Mohi Khansari, Daniel Kappler, Jianlan Luo +2
This paper introduces Action Image, a new grasp proposal representation that allows learning an end-to-end deep-grasping policy. Our model achieves grasp success on re…
Scalable Multi-Task Imitation Learning with Autonomous Improvement
Avi Singh, Eric Jang, Alexander Irpan +5
While robot learning has demonstrated promising results for enabling robots to automatically acquire new skills, a critical challenge in deploying learning-based systems is scale:…
Leveraging Contact Forces for Learning to Grasp
Hamza Merzic, Miroslav Bogdanovic, Daniel Kappler +2
Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enab…
Grasp success prediction with quality metrics
Carlos Rubert, Daniel Kappler, Jeannette Bohg +1
Current robotic manipulation requires reliable methods to predict whether a certain grasp on an object will be successful or not prior to its execution. Different methods and metri…