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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 holds immense value. However, traditional robotic systems have struggled to adapt to the demands of production environments characterized by high-mix, low-volume manufacturing. Robotic learning presents a potential solution to this challenge by enabling robots to acquire assembly skills through demonstration rather than scripted trajectories, thus…

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SELMA: A Novel AI Approach to Enhance Text-to-Image Generation Models Using Auto-Generated Data and Skill-Specific Learning Techniques

Text-to-image (T2I) models have seen rapid progress in recent years, allowing the generation of complex images based on natural language inputs. However, even state-of-the-art T2I models need help accurately capture and reflect all the semantics in given prompts, leading to images that may miss crucial details, such as multiple subjects or specific spatial relationships. For…

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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 real world is a potential approach to enable generalist robots, and solve complex decision-making tasks. However, the challenge to this approach is to address simulation-to-reality (sim-to-real) gaps. Also, a huge amount of data is needed while learning to solve these tasks, and the load of…

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