GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive
cs.RO, cs.AI, cs.CV
Submitted: 2026-10-02
Updated: 2026-10-08
Comments: Project page: https://dex-gott.github.io/
Project page: https://dex-gott.github.io
License: http://creativecommons.org/licenses/by/4.0/
The gist: Foundation models and large-scale human data provide rich sources of manipulation intent, but translating this intent into multi-fingered robot behavior remains difficult.
Abstract
Foundation models and large-scale human data provide rich sources of manipulation intent, but translating this intent into multi-fingered robot behavior remains difficult. Dexterous hands still lack a reusable low-level primitive that reliably establishes contact across tasks and embodiments. We propose GOTT, a reach-acquire-move framework built around a single cross-embodiment contact-acquisition primitive. Given a robot-agnostic object trajectory and a reach specification, GOTT first brings the hand near a task-relevant contact region. The shared closed-loop primitive then establishes stable contact from this approximate initialization, and a pose-conditioned controller tracks the desired object motion. Reach specifications may come from future-aware planning, external models, or human demonstrations, while the primitive and tracking backend remain unchanged. Simulation and real-world experiments show that GOTT is able to establish robust contact across diverse objects, arm-hand platforms, and seen and unseen hand morphologies. It also consistently improves end-to-end task success over open-loop grasp execution.
Sources
- Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking
- AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency
- DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
- Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
- Embodied Hands: Modeling and Capturing Hands and Bodies Together
- Proximal Policy Optimization Algorithms
- DiffMorphGrasp: A Diffusion Framework Conditioned on Hand Morphology for Cross-Embodiment Grasp Generation
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