SkelWAM: A Skeleton-Guided World-Action Model for Zero-Shot Cross-Embodiment Manipulation
SkelWAM achieves zero-shot cross-embodiment manipulation with a 25-D skeleton model, reaching 43.3% success.
Key Findings
Methodology
SkelWAM uses a 25-D skeleton model to unify perception and control, employing a video-action mixture of transformers to predict skeleton action chunks. Embodiment-specific decoders convert these chunks into joint or continuum-robot controls. This approach requires no one-to-one joint correspondence or target task demonstrations.
Key Results
- On the LIBERO-Cross10 benchmark, SkelWAM trained on Franka achieves 43.3% success over 1,000 episodes, outperforming the best baseline by 36.2 percentage points.
- A policy trained on JAKA mini2 was successfully deployed on the Feagine A03 continuum robot for three tabletop tasks, demonstrating real-world potential.
- Ablation studies confirmed the effectiveness of the shared representation and whole-body execution, especially across different task subsets.
Significance
SkelWAM offers a novel approach to cross-embodiment manipulation without target task demonstrations, significantly enhancing the scalability of robot learning and reducing repetitive data collection. It addresses visual appearance, action dimensionality, and semantic differences caused by embodiment changes, providing a new pathway for experience reuse across different robot morphologies.
Technical Contribution
SkelWAM achieves a unified representation of vision and action through a shared 25-D skeleton model, eliminating the need for one-to-one joint correspondence. Its innovation lies in combining predictive visual supervision with embodiment-specific decoding for cross-embodiment action generation and execution.
Novelty
SkelWAM is the first to address cross-embodiment manipulation by bridging visual and action differences through a shared skeleton representation, offering a new approach without target task demonstrations compared to existing methods.
Limitations
- In certain task subsets, success rates are lower, possibly due to coordination difficulties between different morphologies.
- High precision camera calibration and background filling are required to ensure accurate visual input.
- In complex scenarios, more computational resources may be needed for real-time processing.
Future Work
Future research could explore enhancing SkelWAM's adaptability in more complex scenarios, particularly in multi-robot collaboration and dynamic environments. Further optimization of decoder efficiency and accuracy is also a key direction.
AI Executive Summary
In the field of robot learning, cross-embodiment manipulation has been a challenge, with traditional methods often requiring extensive task-specific data. SkelWAM addresses this issue by introducing a 25-D skeleton model that unifies vision and action in a shared geometric representation, allowing robots to reuse manipulation experience across different embodiments.
The core of SkelWAM lies in using a video-action mixture of transformers to predict skeleton action chunks, which are then converted into robot control signals by embodiment-specific decoders. This method requires no target task demonstrations or policy updates, significantly improving learning efficiency. On the LIBERO-Cross10 benchmark, SkelWAM achieved a success rate of 43.3%, far surpassing other baseline methods.
Despite its success, SkelWAM still has room for improvement in certain task subsets. Future research could explore its adaptability in more complex scenarios. By further optimizing decoders and processing visual inputs, SkelWAM holds promise for greater impact in multi-robot collaboration and dynamic environments.
Deep Analysis
Background
The field of robotic manipulation has seen significant advancements, particularly in multi-robot learning and world-action models. Traditional methods often rely on extensive task-specific data, limiting their application across different robot embodiments. SkelWAM introduces a shared skeleton representation, offering a novel approach to cross-embodiment manipulation without target task demonstrations.
Core Problem
The core problem of cross-embodiment manipulation lies in the visual and action differences between different robot morphologies. Traditional methods require one-to-one joint correspondence and extensive target task demonstrations, which are time-consuming and difficult to scale. SkelWAM addresses this issue through a shared skeleton representation.
Innovation
SkelWAM's innovation lies in its 25-D skeleton model, which unifies vision and action in a shared geometric representation. By using a video-action mixture of transformers to predict skeleton action chunks, embodiment-specific decoders convert these chunks into robot control signals. This method requires no target task demonstrations, significantly improving learning efficiency.
Methodology
- �� Use a 25-D skeleton model to unify vision and action representation.
- �� Employ a video-action mixture of transformers to predict skeleton action chunks.
- �� Convert action chunks into robot control signals using embodiment-specific decoders.
- �� No target task demonstrations or policy updates required.
Experiments
Experiments were conducted on the LIBERO-Cross10 benchmark, covering ten tasks and ten target embodiments. The model was trained on a Franka robot and validated on a Feagine A03 continuum robot. Results showed a 43.3% success rate over 1,000 episodes.
Results
Results showed that SkelWAM achieved a 43.3% success rate on the LIBERO-Cross10 benchmark, significantly outperforming other baseline methods. Ablation studies confirmed the effectiveness of the shared representation and whole-body execution, especially across different task subsets.
Applications
SkelWAM can be applied in multi-robot collaboration and dynamic environments. Its ability to operate without target task demonstrations makes it highly applicable in industrial automation and service robotics.
Limitations & Outlook
SkelWAM shows lower performance in certain task subsets, possibly due to coordination difficulties between different morphologies. High precision camera calibration and background filling are required to ensure accurate visual input. Future research could explore its adaptability in more complex scenarios.
Plain Language Accessible to non-experts
Imagine you're in a kitchen with a universal recipe that can be used to make various dishes. SkelWAM is like this recipe, allowing different robots to reuse experience across tasks. By using a shared skeleton model, SkelWAM unifies vision and action, much like putting all ingredients into one big pot. This way, whether it's frying or boiling, robots can handle it easily without preparing ingredients separately for each task.
ELI14 Explained like you're 14
Hey there! Imagine you have a super cool game controller that can control all gaming consoles. SkelWAM is like this controller, letting different robots reuse experience across tasks. With a shared skeleton model, SkelWAM is like having all game rules in one manual, so whether it's fighting monsters or solving puzzles, robots can handle it easily! Isn't that awesome?
Glossary
SkelWAM (Skeleton Action Model)
A model that achieves cross-embodiment manipulation through a shared skeleton representation.
Used to unify vision and action, enabling manipulation across different robot embodiments.
LIBERO-Cross10 (Cross-Embodiment Benchmark)
A benchmark for evaluating cross-embodiment manipulation performance.
Includes ten tasks and ten target embodiments to validate SkelWAM's effectiveness.
Transformer
A deep learning model for processing sequential data.
Used to predict skeleton action chunks, bridging vision and action.
TCP (Tool Center Point)
The central point of a robot's end-effector.
Defines the interaction goal of the robot in tasks.
PCC (Piecewise Constant Curvature)
A method for modeling the kinematics of continuum robots.
Used in the decoder for the Feagine A03 continuum robot.
Open Questions Unanswered questions from this research
- 1 How to enhance SkelWAM's adaptability in more complex scenarios, especially in multi-robot collaboration and dynamic environments.
- 2 How to further optimize decoder efficiency and accuracy to improve real-time processing capabilities.
Applications
Immediate Applications
Industrial Automation
SkelWAM can be used for experience reuse among industrial robots, increasing production efficiency and reducing data collection costs.
Long-term Vision
Service Robotics
SkelWAM holds promise for cross-embodiment task execution in service robotics, offering more flexible service solutions.
Abstract
Reusing manipulation experience across robot embodiments is important for scaling robot learning and reducing repeated task-specific data collection. However, changes in embodiment alter visual appearance, action dimensionality and semantics, and the whole-body configurations that can realize the same tool pose. We present SkelWAM, a skeleton-guided world-action model that couples perception and control through one explicit geometric representation for single-source cross-embodiment manipulation. Arm centerline geometry, tool-center-point (TCP) pose, and parallel-jaw commands form a shared 25-D state. The same definition underlies canonical third-person and wrist observations and future whole-body action targets. Trained with predictive visual supervision, a video-action mixture of transformers predicts canonical skeleton action chunks, which embodiment-specific constrained decoders convert into joint or continuum-robot controls. This formulation requires no one-to-one joint correspondence and uses no target-task demonstrations or target policy updates. We introduce LIBERO-Cross10, a source-only cross-embodiment transfer benchmark covering ten tasks and ten target embodiments across four morphological groups. On this benchmark, Franka-trained SkelWAM achieves 43.3% success over 1,000 episodes, exceeding the best-performing evaluated baseline by 36.2 percentage points. We further deploy a JAKA mini2-trained policy on the Feagine A03 continuum robot for three tabletop manipulation tasks, illustrating the approach's potential for real-world cross-embodiment manipulation. Project page: http://www.liukepku.com/skelwam/index.html