Where to Touch, How to Contact: Hierarchical RL–MPC Framework for Geometry-Aware Long-Horizon Dexterous Manipulation

1Arizona State University 2University of Texas at Dallas 3University of Pennsylvania

Overview: we propose a hierarchical RL–MPC framework: the reinforcement learning (RL) performs geometric–kinematic reasoning at a higher level, while offloading the complex dynamic contact planning to contact-implicit model predictive control (MPC) at a lower level. The two levels are linked by a contact intention—a novel object-centric interface that specifies a surface contact location (geometry) together with a post-contact object-level subgoal (kinematics).



Comprehensive Video Demo:
real-world execution




How does it work?


Description of the image

Our proposed framework consists of (1) a higher-level policy for object-centric geometric and kinematic reasoning and (2) a lower-level policy for dynamic contact planning. The higher-level policy, trained using RL, predicts contact intention, which parameterizes the lower-level policy. The lower-level policy seeks to realize the contact intention through local contact-implicit model predictive control (MPC). The output of the lower-level policy is the robot action command.



Real-World Results


Geometry-generalized Pushing Task

In this task, the goal is to learn a unified policy that pushes a diverse set of letter-shaped objects from arbitrary initial poses to arbitrary planar target pose (position and orientation). 12 English letters are used in total: 6 letters (E, H, L, N, T, X) are used for training, and the remaining 6 (F, I, K, V, Y, Z) are held out for evaluation.


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N_acc
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X_acc
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Z_acc

3D Object Reorientation Task

In this task, the goal is to reorient a cube from a randomized initial pose to a randomized 3D target pose.


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More Simulation Results


Geometry-generalized Pushing Task


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3D Object Reorientation Task


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BibTeX

@article{TBD,
  author    = {Xie, Zhixian and Xiang, Yu and Michael Posa and Jin, Wanxin},
  title     = {Where to Touch, How to Contact: A Hierarchical RL–MPC Framework for Geometry-Aware Long-Horizon Dexterous Manipulation},
  journal   = {tbd},
  year      = {2026},
}