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Want to Make Robots Smarter? Tsinghua Team Discovers the Secret to Accelerated Robot Learning

The rapid development of deep learning relies on the scale of datasets, models, and computational power. In the fields of natural language processing and computer vision, researchers have already discovered a power-law relationship between model performance and data scale. However, in the field of robotics, especially in robot manipulation, such scalable patterns have yet to be established. A research team from Tsinghua University recently published a paper exploring data scaling laws in robot imitation learning and proposed an efficient data collection strategy that collected sufficient data in just one afternoon, enabling...

10.3k 11 hours ago

Want to Make Robots Smarter? Tsinghua Team Discovers the Secret to Accelerated Robot Learning

MIT Unveils New Robot Training Model to Tackle Problems in a Simpler, More Direct Way

The Massachusetts Institute of Technology (MIT) showcased a brand new robot training model this week that abandons the previous focus on training methods centered around specific datasets, opting instead for an approach that utilizes vast amounts of information similar to that used in training large language models (LLMs). Researchers noted that imitation learning—where agents learn by mimicking individuals performing tasks—can fail when facing minor challenges. These challenges may include varying lighting conditions, different environmental setups, or new obstacles. In such cases, the robots lack sufficient data to adapt to these changes.

9.7k 6 days ago

MIT Unveils New Robot Training Model to Tackle Problems in a Simpler, More Direct Way

MIT Unveils a Method Inspired by Large Language Models to Teach Robots New Skills for the First Time

This week, MIT showcased a new model for training robots, aimed at addressing the challenges faced by imitation learning when introduced to small obstacles. Researchers pointed out that imitation learning can fail in conditions such as varying lighting, different environments, or new obstacles because robots simply do not have enough data to adapt. The team sought powerful data methods, like those used in models such as GPT-4, to tackle the problem. They introduced a new architecture called the Heterogeneous Pretrained Transformer (HPT), which aggregates information from different sensors and environments.

11.5k 11-01

MIT Unveils a Method Inspired by Large Language Models to Teach Robots New Skills for the First Time

Is ChatGPT's Mysterious Power Holding Back LLMs? Karpathy and LeCun Jointly Critique RLHF Technology

Andrej Karpathy suggested that Reinforcement Learning from Human Feedback (RLHF) may not be the ultimate solution for AI to achieve human-level problem-solving capabilities. Using AlphaGo as an example, he pointed out that true reinforcement learning techniques optimize neural networks through self-play, ultimately surpassing humans without human intervention. In contrast, RLHF resembles imitation of human preferences rather than solving problems, being effective in closed environments with clearly defined reward mechanisms, such as Go, but less so in open-ended tasks like article summarization and code rewriting.

10.9k 2 days ago

Is ChatGPT's Mysterious Power Holding Back LLMs? Karpathy and LeCun Jointly Critique RLHF Technology

AI Products

RoboticsDiffusionTransformer

A diffusion-based model designed for bimanual manipulation.

AI model

10.2k

Surgical Robot Transformer

A robot that performs surgical tasks through imitation learning.

AI Healthcare

6.5k

HumanPlus

Human Action Imitation and Autonomous Skill Learning System

AI robot

7.4k

LeRobot

LeRobot offers state-of-the-art machine learning models, datasets, and tools for real-world robots.

AI development assistant

12.6k

Models

Roboengine Sam

michaelyuanqwq

RoboEngine is the first plug-and-play visual robot data augmentation toolkit. Users can generate robot scenarios with physical and task perception capabilities with just a few lines of code, solving the problem that existing methods are limited by camera calibration or controlled environments and significantly improving the visual robustness of imitation learning.

Rdt 170m

robotics-diffusion-transformer

RDT-170M is a 170-million-parameter imitation learning diffusion Transformer model designed for robot vision-language-action tasks.

Multimodal

TransformersEnglish

robotics-diffusion-transformer

278

Rdt 1b

robotics-diffusion-transformer

A 1-billion-parameter imitation learning diffusion Transformer model pretrained on 1M+ multi-robot operation data, supporting multi-view visual-language-action prediction

Multimodal

TransformersEnglish

robotics-diffusion-transformer

2.6k

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