Search Results for author:
Found 12 papers, 5 papers with code
GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
We present GR-2, a state-of-the-art generalist robot agent for versatile and generalizable robot manipulation.
Vision-Language Foundation Models as Effective Robot Imitators
We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy.
InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions
Ambiguity is ubiquitous in human communication.
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?
However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these choices have not been extensively discussed in the literature, making it difficult to quantify progress in this field.
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter
In this paper, we present a new dataset named REGRAD for the learning of relationships among objects and grasps.
REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds
Reliable robotic grasping in unstructured environments is a crucial but challenging task.
Robotics
Hindsight Trust Region Policy Optimization
We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hindsight} to tackle the challenge of sparse rewards.
A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes
Autonomous robotic grasping plays an important role in intelligent robotics.
Robotics
A Real-time Robotic Grasp Approach with Oriented Anchor Box
The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors.
Robotics
ROI-based Robotic Grasp Detection for Object Overlapping Scenes
Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset.
Robotics
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
The feature extractor is a deep convolutional neural network.
