DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library
- Published
- Source
- arXiv
- Paper number
- 1134
- Field
- Robotics
- arXiv ID
- 2609.35318
Key points
- Generated physically grounded robot trajectory data from a single egocentric human video and a task prompt.
- At each of four pipeline stages, selected or newly developed tools and required property-specific verifiers to pass before advancing, preventing error propagation.
- Stored newly developed skills and verifiers in a self-evolving library, reducing processing time as more videos are handled.
- Achieved a 3.5x higher success rate than competing baselines across eleven real-world tasks spanning rigid, articulated, and deformable objects.
- Varied object and robot states in simulation and retextured rendered observations to facilitate sim-to-real transfer.
Paper links
External research summaries. These are not HDATF publications or measured product results.