GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive

Yulin Liu, Lai Wei, Yen-Jen Wang, Akash Sharma, Pieter Abbeel, Henrik I. Christensen, Haozhi Qi · N/A · 2026

Summary

Abstract Summary

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.

Key Points

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.

Dexterous Manipulation via General Object Trajectory Tracking

We propose GOTT , a reach-acquire-move framework (Section 3.1 ) for executing rigid-object motions with dexterous hands. Its high-level interface consists of a desired object trajectory $\tau_{o}$ and a reach specification $\rho$ that brings the hand near a task-relevant contact region.

At its core, a reusable cross-embodiment contact-acquisition primitive grounds this interface by turning an approximate reach into stable contact (Section 3.2 ).

Dexterous Manipulation via General Object Trajectory Tracking — GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive — Figure 1 : Contact-acquisition training and cross-embodiment policy. (A) The RL policy starts from randomized initial configurations and learns to establish stable contact using local hand-object observations. (B) Padding and binary masks map different hands into a shared observation and action space, allowing one policy to operate across hand embodiments.

Experiments

We evaluate the contact-acquisition primitive’s robustness, cross-embodiment transfer, and contribution to end-to-end task success. We further examine its reuse across workspaces and with different high-level sources, and evaluate key design choices through ablations. Hardware setup.

We evaluate GOTT on multiple arm-hand platforms, including Franka Research 3 (7-DoF) and Tianji (7-DoF) arms paired with XHand (12-DoF) and Sharpa Wave (22-DoF) hands, as shown in Figure 4 . Object assets and perception stack.

Experiments — GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive — Figure 2 : Future-aware planning. We roll out the hand motion induced by each candidate initial configuration over the full object trajectory and solve IK along this motion. Red segments indicate joint jumps from discontinuous IK solutions. We reject candidates that are initially reachable but become infeasible later and select the remaining candidate with the least cumulative joint motion.

Sources and Demonstrations

Figures are reproduced from Liu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.