Posted on X by Hang Liu Pixels in, contacts out...

Perception, interaction, autonomy - next agenda for humanoids.

We learn a multi-task humanoid world model from offline datasets and use MPC to plan contact-aware behaviors from ego-vision in the real-world.

Project and Code: https:// ego-vcp.github.io

https://ego-vcp.github.io/

Research Notes: Pixels in, contacts out... Perception, Interaction, Autonomy - Next Agenda for Humanoids

Overview

The post discusses advancements in humanoid robotics focused on perception, interaction, and autonomy. It highlights the use of multi-task world models learned from offline datasets and Model Predictive Control (MPC) to enable contact-aware behaviors in real-world environments using ego-vision. The project aims to integrate visual data with autonomous decision-making to improve dexterity and adaptability in humanoid robots [1-5].

Technical Analysis

The post emphasizes the importance of combining perception (pixels) with autonomous behavior planning (contacts out). This approach leverages offline datasets to train multi-task world models, which are then used by MPC to plan contact-aware movements. The integration of ego-vision ensures real-time adaptability in dynamic environments [2].

1. Humanoid Dynamics and Locomotion: Recent advancements in humanoid robot dynamics focus on improving locomotion and balance. These improvements enable more fluid and adaptive movement, which is crucial for tasks requiring precise contact interactions [1].

   • Citation: Result #1 highlights the importance of dynamic models in humanoid robotics.

2. Self-Learning AI: Self-learning frameworks allow robots to adapt to new environments without extensive retraining. This aligns with the post's focus on autonomy and real-world applicability [3].

   • Citation: Result #3 emphasizes the role of self-learning algorithms in achieving generalized behavior for humanoid robots.

3. Full-Body Autonomy: Systems like Helix 02 demonstrate the feasibility of full-body autonomous navigation, which is a key component of the post's vision [4].

   • Citation: Result #4 showcases advancements in full-body autonomy using MPC and visual data.

4. Dexterous Manipulation: The integration of dexterous whole-body control systems (e.g., OmniH2O) enhances the ability to perform complex tasks, such as object manipulation and navigation [5].

   • Citation: Result #5 highlights the importance of universal control frameworks for humanoid robotics.

Implementation Details

The post references a GitHub project (https://ego-vcp.github.io), which likely includes code and tools for training multi-task world models and implementing MPC-based planning. Specific details from search results include:

• Code Frameworks: The GitHub repository (Result #2) lists open-source projects and resources related to humanoid robot learning, including frameworks for perception and control.
• Tools: The implementation likely uses tools like PyTorch or other deep learning libraries for training world models and MPC controllers.

Related Technologies

1. Reinforcement Learning: Many humanoid robotics systems rely on reinforcement learning (RL) to train policies for autonomous behavior. RL frameworks are often integrated with offline datasets to improve sample efficiency [3,5].

   • Citation: Result #3 discusses the use of RL in self-learning AI for humanoids.

2. Ego-Vision and Perception: The integration of ego-vision systems enables robots to process visual data in real time, which is critical for contact-aware planning [4].

   • Citation: Result #4 highlights the role of visual data in full-body autonomy.

3. Dexterous Manipulation Systems: Advanced manipulation systems like OmniH2O demonstrate the potential for humanoid robots to perform complex tasks requiring fine motor skills and adaptability [5].

   • Citation: Result #5 explores universal control frameworks for dexterous manipulation.

Key Takeaways

• The integration of multi-task world models and MPC enables contact-aware behaviors in humanoid robots, enhancing their autonomy and adaptability [2-5].
• Self-learning AI frameworks are crucial for enabling generalized behavior in dynamic environments [3].
• Full-body autonomy systems like Helix 02 demonstrate the potential for real-world applications of humanoid robotics [4].

Note: All citations refer to the provided search results and external links.

Further Research

Here's a 'Further Reading' section based on the provided search results:

• Advancements in humanoid robot dynamics and learning-based locomotion: This journal article explores the latest developments in humanoid robot dynamics and learning-based approaches to locomotion, providing a comprehensive overview of the field.

• GitHub: YanjieZe/awesome-humanoid-robot-learning: A curated repository on GitHub that aggregates resources related to humanoid robot learning, offering practical insights and community-driven content.

• Self-Learning AI for Adaptive Humanoid Robotics (PDF): This paper discusses self-learning AI applications in adaptive robotics, focusing on generalized capabilities in humanoids through machine learning techniques.

• Helix 02: Full-Body Autonomy: An article introducing Figure.ai's Helix 02, highlighting advancements in full-body autonomy and practical applications of humanoid robotics.

• OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Interaction (PDF): This research paper explores human-to-humanoid interaction, emphasizing dexterity and whole-body skills, which is essential for understanding broader applications in humanoid robotics.