TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
TopoRetarget enhances RL policy training success by 40.6% by preserving hand-object interaction.
Key Findings
Methodology
TopoRetarget is an interaction-preserving retargeting framework using sparse interaction graphs and distance-weighted Laplacian deformation optimization, incorporating directional consistency, kinematic constraints, and penetration handling. It employs a single parameter set across diverse retargeting conditions, maintaining task-relevant hand-object interaction and adapting human demonstrations to dexterous robot hands.
Key Results
- TopoRetarget achieved the best contact precision and alignment on the ContactPose Dataset, with contact precision at 7.71 mm and alignment error at 15.67°.
- Improved Pen-Spin training success by 40.6 percentage points over existing baseline methods.
- Enabled zero-shot transfer to Wuji Hand hardware for cube reorientation and pen spinning.
Significance
TopoRetarget addresses the challenge of preserving hand-object interaction during retargeting, significantly enhancing RL policy training for dexterous manipulation. This method holds substantial academic and industrial significance, particularly in applications requiring precise hand-object interaction.
Technical Contribution
TopoRetarget introduces a novel interaction-preserving retargeting framework, using a unified parameter set for various retargeting conditions. It surpasses current state-of-the-art methods in contact precision and alignment, supporting real-time retargeting.
Novelty
TopoRetarget is the first to achieve interaction-preserving retargeting for dexterous manipulation, differing from previous methods that focused primarily on hand pose, emphasizing the preservation of hand-object interaction.
Limitations
- TopoRetarget relies on the quality of upstream human reference motion; its effectiveness is reduced if the source motion has virtual contact issues.
- Handling complex multi-finger contact modes may require further optimization.
Future Work
Future work could involve preprocessing source motions to correct missing-contact cases and exploring optimization strategies for complex multi-finger contact modes.
AI Executive Summary
Training reinforcement learning (RL) policies for dexterous manipulation often relies on human hand-object demonstrations, but preserving hand-object interaction during retargeting is a key challenge. TopoRetarget introduces an interaction-preserving retargeting framework that addresses this issue by constructing sparse interaction graphs and optimizing distance-weighted Laplacian deformation.
In experiments, TopoRetarget achieved the best contact precision and alignment on the ContactPose Dataset and improved Pen-Spin training success by 40.6 percentage points. Additionally, the method supports zero-shot transfer to Wuji Hand hardware, demonstrating its potential in real-world applications.
While TopoRetarget makes significant progress in preserving hand-object interaction, its effectiveness still depends on the quality of upstream human reference motion. Future research could focus on preprocessing source motions and optimizing for more complex multi-finger contact modes to further expand its applicability and effectiveness.
Deep Analysis
Background
Research in dexterous manipulation has been a crucial topic in robotics. Early studies focused on hand pose retargeting, such as DexPilot and AnyTeleop. However, as task complexity increases, merely matching hand poses is insufficient, and preserving hand-object interaction has become a new challenge.
Core Problem
The core problem in dexterous manipulation is preserving hand-object interaction during retargeting. Existing methods mostly focus on matching hand poses, neglecting the preservation of hand-object contact structure, leading to inconsistent contacts and task failures.
Innovation
TopoRetarget's core innovation lies in its interaction-preserving retargeting framework. By constructing sparse interaction graphs, the method uses a unified parameter set across diverse retargeting conditions, maintaining task-relevant hand-object interaction.
Methodology
- �� Construct sparse interaction graphs covering hand and object keypoints.
- �� Optimize distance-weighted Laplacian deformation with directional consistency and kinematic constraints.
- �� Handle penetration issues to ensure accurate hand-object contact.
Experiments
Experiments used the ContactPose Dataset and a self-collected MoCap Pen-Spin Dataset. Baselines included OmniRetarget, Mink, DexPilot, and GeoRT. Evaluation metrics included contact precision, alignment error, and penetration depth.
Results
TopoRetarget achieved 7.71 mm contact precision and 15.67° alignment error on the ContactPose Dataset, outperforming baseline methods. In the Pen-Spin task, success rate improved by 40.6 percentage points, demonstrating its advantage in complex tasks.
Applications
TopoRetarget can be applied to dexterous manipulation tasks requiring precise hand-object interaction, such as fine assembly in industrial automation and gesture interaction in virtual reality.
Limitations & Outlook
TopoRetarget relies on the quality of upstream human reference motion; its effectiveness is reduced if the source motion has virtual contact issues. Future research could focus on preprocessing source motions and optimizing for more complex multi-finger contact modes.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, needing to pick up and manipulate various tools. TopoRetarget acts like a smart assistant, ensuring your fingers accurately grasp the right spot on each tool without slipping or misplacing. Even if your hand shape doesn't perfectly match the tool, it adjusts your grip to ensure you complete tasks smoothly.
ELI14 Explained like you're 14
Hey, imagine you're playing a super cool VR game where you need to grab and spin objects with your hands. TopoRetarget is like a super smart game assistant, making sure your virtual hands can precisely grab and manipulate objects just like in real life. Even if your hands don't exactly match the game's hands, it helps you adjust, so you can play more smoothly!
Glossary
Motion Retargeting
The process of mapping human hand movements to robotic hand movements.
Used to convert human demonstrations into robot reference trajectories.
Reinforcement Learning
A machine learning method that trains agents through rewards and penalties.
Used to train policies for dexterous manipulation.
Dexterous Manipulation
The ability of robotic hands to perform complex, fine tasks.
The main application scenario of the study.
Trajectory Optimization
Generating optimal motion paths through optimization algorithms.
Used to generate precise robot motion references.
Human-Robot Interaction
The interaction process between humans and robots.
A key interaction to maintain in the study.
Open Questions Unanswered questions from this research
- 1 How to preserve hand-object interaction in virtual contact scenarios? Current methods struggle with this, requiring new preprocessing strategies.
- 2 How to optimize retargeting strategies for complex multi-finger contact modes to improve precision?
Applications
Immediate Applications
Industrial Automation
TopoRetarget can be used for fine assembly tasks, ensuring robotic hands accurately grasp and manipulate small components.
Long-term Vision
Virtual Reality Interaction
Achieving more natural gesture interaction in VR, enhancing user experience.
Abstract
Human hand-object demonstrations provide dense reference motions for training dexterous manipulation reinforcement learning (RL) policies through reference tracking. However, to use such demonstrations for RL policy learning, retargeting must preserve hand pose and task-relevant hand-object contact structure. Otherwise, contact and feasibility artifacts can degrade downstream RL policy performance. We introduce TopoRetarget, an interaction-preserving retargeting framework that uses a single set of parameters across diverse retargeting conditions while maintaining task-relevant hand-object interaction and adapting human demonstrations to dexterous robot hands. The method constructs a sparse interaction graph over hand and object keypoints and optimizes distance-weighted Laplacian deformation with directional consistency, kinematic constraints, and penetration handling. Evaluations show that the generated references improve both interaction fidelity and policy learning: TopoRetarget achieves the best contact precision and alignment over all baselines on the ContactPose Dataset, improves Pen-Spin training success by 40.6 percentage points over the existing baseline methods, and enables zero-shot transfer to Wuji Hand hardware on cube reorientation and pen spinning.