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Researchers from Stanford and Cornell Introduce APRICOT: A Novel AI Approach that Merges LLM-based Bayesian Active Preference Learning with Constraint-Aware Task Planning
In the rapidly evolving field of household robotics, a significant…
Google DeepMind Researchers Propose RT-Affordance: A Hierarchical Method that Uses Affordances as an Intermediate Representation for Policies
In recent years, there has been significant development in the…
Latent Action Pretraining for General Action models (LAPA): An Unsupervised Method for Pretraining Vision-Language-Action (VLA) Models without Ground-Truth Robot Action Labels
Vision-Language-Action Models (VLA) for robotics are trained by combining large…
Google DeepMind’s SIMA Project Enhances Agent Performance in Dynamic 3D Environments Across Various Platforms
The exploration of artificial intelligence within dynamic 3D environments has…
This AI Paper Proposes a Pipeline for Improving Imitation Learning Performance with a Small Human Demonstration Budget
The practical application of robotic technology in automatic assembly processes…
Researchers at Stanford Propose TRANSIC: A Human-in-the-Loop Method to Handle the Sim-to-Real Transfer of Policies for Contact-Rich Manipulation Tasks
Learning in simulation and applying the learned policy to the…
