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Found 8 papers, 1 papers with code
Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
In this study, we are interested in imbuing robots with the capability of physically-grounded task planning.
Imitation Learning from Observation with Automatic Discount Scheduling
Nonetheless, we identify that tasks characterized by a progress dependency property pose significant challenges for such approaches; in these tasks, the agent needs to initially learn the expert's preceding behaviors before mastering the subsequent ones.
Policy Contrastive Imitation Learning
However, the performance of AIL is still unsatisfactory on the more challenging tasks.
A Universal Semantic-Geometric Representation for Robotic Manipulation
To this end, we present $\textbf{Semantic-Geometric Representation} (\textbf{SGR})$, a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning.
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
Our study yields a series of intriguing results, including the discovery that the effectiveness of pre-training is highly dependent on the choice of the downstream policy learning algorithm.
Semantic-Aware Fine-Grained Correspondence
Establishing visual correspondence across images is a challenging and essential task.
A Multi-channel Training Method Boost the Performance
Deep convolutional neural network has made huge revolution and shown its superior performance on computer vision tasks such as classification and segmentation.
Adversarial Data Encryption
In the big data era, many organizations face the dilemma of data sharing.
