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Scaling Robot Skills with Web Language and Video Annotations

Scaling Robot Skills with Web Language and Video Annotations - Leveraging Language Models for Robot Skill Acquisition

However, this information is not directly related to the specific topics mentioned." This approach involves using large language models to help robots learn new skills by processing natural language instructions and video demonstrations from web-based sources.

By leveraging the vast amount of information available online, robots can acquire new capabilities efficiently and expand their versatility in performing a wide range of tasks.

This approach has the potential to significantly enhance the adaptability and effectiveness of robotic systems.

The framework for robot skill acquisition leverages language-guided data generation and language-conditioned policies to efficiently scale up data and effectively distill it into a robust multi-task language-conditioned visuomotor policy.

The use of large-scale pretrained language models (LLMs) and vision-language models (VLMs) has shown significant success in the application of robotic skill acquisition, as they enable robots to learn new skills quickly and efficiently by leveraging the vast amount of information available on the web.

A comprehensive survey of language-conditioned approaches within the context of robotic manipulation analyzes these approaches based on their learning paradigms, which encompass reinforcement learning, imitation learning, and the integration of foundational models such as large language models.

The method of data-driven instruction augmentation for language-conditioned control (DIAL) is a specific approach for scaling up and distilling down language-guided robot skill acquisition, leveraging the semantic understanding of CLIP to propagate knowledge onto large datasets of unlabelled demonstration data.

By processing natural language instructions and video demonstrations, robots can acquire new skills without requiring explicit programming or human demonstration, enabling them to learn complex tasks such as cooking, assembly, and maintenance from diverse sources like YouTube videos, blogs, and instruction manuals.

This language-based approach to robot skill acquisition has the potential to greatly increase the versatility and adaptability of robots, as they can generalize learned skills to new situations and environments, expanding the range of tasks they can perform in various domains.

Scaling Robot Skills with Web Language and Video Annotations - Scaling Up Diverse Robot Exploration Data

The "Scaling Up and Distilling Down Language-Guided Robot Skill Acquisition" framework, which aims to efficiently generate large volumes of language-labeled robot data and distill it into a robust multi-task language-conditioned visuomotor policy.

The "Gen2Sim" method, which automates the generation of 3D environments and accompanying robot scenarios, providing a viable path for scaling up reinforcement learning for robot manipulators in simulation.

By combining these approaches, the research suggests the potential for more efficient and adaptable robot skill acquisition, leveraging the strengths of language models, sampling-based planners, and policy learning.

Language-Guided Robot Skill Acquisition" framework aims to efficiently generate large volumes of language-labeled robot data and effectively distill it into a robust multi-task language-conditioned visuomotor policy.

The Generation to Simulation (Gen2Sim) method is a technique that automates the generation of 3D environments and accompanying robot scenarios, enabling the scaling up of robot skill learning in simulation.

By combining the strengths of large language models (LLMs), sampling-based planners, and policy learning, the framework can automatically generate, label, and distill diverse robot exploration experience into a multi-task visuo-linguo-motor policy.

The data-driven instruction augmentation for language-conditioned control (DIAL) approach leverages the semantic understanding of CLIP to propagate knowledge onto large datasets of unlabeled demonstration data, further scaling up the language-guided robot skill acquisition process.

The language-based approach to robot skill acquisition has the potential to greatly increase the versatility and adaptability of robots, as they can generalize learned skills to new situations and environments, expanding the range of tasks they can perform in various domains.

Language-Guided Robot Skill Acquisition" framework and the Gen2Sim method could potentially enable more efficient and scalable robot skill acquisition and learning, paving the way for more advanced and capable robotic systems.

Scaling Robot Skills with Web Language and Video Annotations - Visual Guidance Through Video Annotations

Recent studies have explored the use of video annotations to enhance robot learning and performance.

Approaches like GR1, a GPT-style transformer, and RT2, a vision-language-action model, leverage video annotations and language models to enable robots to learn complex skills from web-based resources.

Additionally, new frameworks have been proposed to automate and scale up the video annotation process, facilitating efficient training of computer vision models for robotic applications.

Recent studies have proposed using a GPT-style transformer called GR1 that can take language instructions, observation images, and robot states as input, and output actions and future images in an end-to-end manner for visual robot manipulation learning.

Researchers have developed a selection-and-refinement strategy to automatically improve preliminary video annotations generated by tracking algorithms for bounding box annotations in video sequences.

The vision-language-action model RT2 co-finetuned large VLMs, LLMs, and robotic trajectory data to enable emergent semantic reasoning capabilities in robotics.

Large language models (LLMs) have been utilized to obtain fine-grained video descriptions aligned with videos, where an LLM is prompted to create plausible video descriptions based on ASR narrations for a large-scale instructional video dataset.

Large-scale video generative pre-training has been shown to benefit visual robot manipulation learning, and a flexible GPT-style transformer model, GR-1, has been introduced to enable this approach.

A new method called LAVILA repurposes pre-trained LLMs to be conditioned on visual input and finetuned to create automatic video narrators for video annotations.

Researchers have proposed a method to automate the overlay of visual annotations on videos of robots' execution to capture information underlying their reasoning, specifically for the complex robot soccer domain.

Studies have demonstrated that web-scale video-text data can be used to learn video-language representations using LLMs, revealing remarkable ability for "zero-shot" generalization in video understanding tasks.

Scaling Robot Skills with Web Language and Video Annotations - Data Efficiency in Robot Learning

Data efficiency is a crucial challenge in robot learning, where gathering real-world data can be expensive and time-consuming.

Researchers are exploring novel approaches to address this issue, such as leveraging text-to-image foundation models to generate photorealistic images that represent various robot skills, reducing the need for additional robot data.

Another promising direction is the use of large-scale video generative pre-training to benefit visual robot manipulation learning, enabling more efficient scaling of robot learning.

Data efficiency is a crucial challenge in robot learning, as gathering real-world robot data can be expensive and time-consuming.

Researchers are addressing this by leveraging text-to-image foundation models to generate diverse and realistic robot action data without the need for additional robot data collection.

The "Learning from the Void" (LfVoid) framework efficiently scales up data generation by using text-guided diffusion models to create photorealistic images representing various robot skills, which are then used to train robust multi-task language-conditioned visuomotor policies.

Large-scale video generative pre-training has been shown to benefit visual robot manipulation learning, with a flexible GPT-style transformer model, GR-1, enabling this approach and allowing robots to learn from diverse web-based video data.

Researchers have proposed methods to automate the overlay of visual annotations on videos of robots' execution, capturing valuable information about their reasoning, particularly for complex domains like robot soccer.

The data-driven instruction augmentation for language-conditioned control (DIAL) approach leverages the semantic understanding of CLIP to propagate knowledge from language-labeled data onto large datasets of unlabeled robot demonstration data, further scaling up the language-guided robot skill acquisition process.

The "Gen2Sim" method automates the generation of 3D environments and accompanying robot scenarios, providing a viable path for scaling up reinforcement learning for robot manipulators in simulation, which can complement the language-guided skill acquisition approaches.

Large language models (LLMs) and vision-language models (VLMs) have shown significant success in robot skill acquisition, enabling robots to learn new skills quickly and efficiently by leveraging the vast amount of information available on the web.

The vision-language-action model RT2 co-finetuned large VLMs, LLMs, and robotic trajectory data to enable emergent semantic reasoning capabilities in robotics, further enhancing the language-guided skill acquisition process.

Scaling Robot Skills with Web Language and Video Annotations - Automating Robot Reward Annotation

Researchers have proposed approaches to enable users to teach robots novel actions through natural language input, leveraging reward functions as an interface that bridges the gap between language and low-level robot actions.

One approach is to use large language models (LLMs) to propose features and parameterization of the reward, then update the parameters through an iterative self-alignment process.

Another approach is to introduce the Large Language Model Supervised Robotics Text2Skill Autonomous Learning (ARO) framework, which aims to replace human participation in the robot skill learning process with large-scale language models that incorporate reward function design and performance evaluation.

The "Language to Rewards for Robotic Skill Synthesis" framework proposes using reward functions as an interface between high-level language instructions or corrections and low-level robot actions, enabling efficient robot skill acquisition.

Researchers have introduced the Large Language Model Supervised Robotics Text2Skill Autonomous Learning (ARO) framework, which aims to replace human participation in the robot skill learning process with large-scale language models.

A key challenge in using large language models (LLMs) for robotic control is that low-level robot actions are often underrepresented in the training corpora of these models.

The "Data-Driven Instruction Augmentation for Language-Conditioned Control (DIAL)" approach leverages the semantic understanding of CLIP to propagate knowledge onto large datasets of unlabeled robot demonstration data, further scaling up the language-guided robot skill acquisition process.

Researchers have proposed using real-time optimizers like MuJoCo MPC paired with the LLM-generated reward interface to create an interactive behavior creation experience for users teaching robots new skills.

The "Gen2Sim" method automates the generation of 3D environments and accompanying robot scenarios, providing a viable path for scaling up reinforcement learning for robot manipulators in simulation, complementing the language-guided skill acquisition approaches.

The "Learning from the Void" (LfVoid) framework efficiently scales up data generation by using text-guided diffusion models to create photorealistic images representing various robot skills, reducing the need for additional robot data collection.

Scaling Robot Skills with Web Language and Video Annotations - The Continued Need for Human Oversight

While advances in language models, vision-language models, and automated data generation techniques have shown promise in enhancing robot learning, the need for human involvement and procedural safeguards remains critical.

The user experience (UX) during human-robot interaction is also emphasized as an important consideration for ensuring effective and responsible robot skill acquisition.

A study found that the use of human oversight in artificial systems is advocated as a solution against the risks of increasing reliance on algorithmic tools and as a procedural safeguard for law.

Researchers have developed the RoboTurk platform to increase the quantity of manipulation data collected through human supervision while maintaining data quality, addressing the shortcomings of prior work.

The applications of skill transfer with reinforcement learning algorithms and the effect of user experience (UX) on learning from demonstration approaches are being reviewed, highlighting the importance of UX during human-robot interaction.

To mitigate the issue of limited robot data, an alternative approach leverages text-to-image foundation models to obtain meaningful data for robot learning without requiring additional robot data.

Language annotations and videos of humans are being studied to learn reward functions in a scalable way for robotic reinforcement learning, allowing the rewards to generalize more broadly for long-horizon manipulation tasks.

Large language models (LLMs) hold significant promise in improving human-robot interaction by offering advanced conversational skills and versatility in managing diverse, open-ended user requests.

The "Gen2Sim" method automates the generation of 3D environments and accompanying robot scenarios, providing a viable path for scaling up reinforcement learning for robot manipulators in simulation.

The "Learning from the Void" (LfVoid) framework efficiently scales up data generation by using text-guided diffusion models to create photorealistic images representing various robot skills, reducing the need for additional robot data collection.

The vision-language-action model RT2 co-finetuned large VLMs, LLMs, and robotic trajectory data to enable emergent semantic reasoning capabilities in robotics.

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