Abstract
We present a primitive-informed sampling-based model predictive control (MPC) framework for multi-fingered dexterous manipulation. Sampling-based MPC evaluates candidate control trajectories through forward simulation without requiring gradients through complex contact dynamics. However, directly sampling these trajectories in the high-dimensional joint space of a dexterous hand is inefficient and makes performance strongly dependent on the sampling distribution. Our framework biases sampling using low-dimensional manipulation primitives that encode coordinated finger motions, while simultaneously optimizing joint-level residuals to adapt these motions to the current hand-object configuration. Task-related rollout constraints reject infeasible trajectories during forward simulation, improving the effective use of the sampling budget. We evaluate the approach on a 16 DoF Allegro hand using a synchronized MuJoCo digital twin. Ablations show that both the primitive and residual are necessary for reliable continuous in-hand rotation, that increasing the sampling budget alone does not recover this coordination, and that rollout constraints substantially improve success rate. A primitive extracted for one object size transfers to other sizes and remains effective under model mismatch. The framework further supports grasping, object reorientation, and coordinated arm-hand manipulation, using primitives extracted from both a simulation-trained policy and human hand-motion data.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Küçüktabak, E. B., Patel, K., Cui, J., Yang, Z., Sasabuchi, K., & Takamatsu, J. (2026). Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation. https://omanscience.com/en/articles/primitive-informed-sampling-based-mpc-for-multi-fingered-dexterous-manipulation
MLA 9
Küçüktabak, Emek Barış, et al. "Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation." https://omanscience.com/en/articles/primitive-informed-sampling-based-mpc-for-multi-fingered-dexterous-manipulation.
Chicago (author–date)
Küçüktabak, Emek Barış, Karankumar Patel, Jinda Cui, Zhaodong Yang, Kazuhiro Sasabuchi, and Jun Takamatsu. 2026. "Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation." https://omanscience.com/en/articles/primitive-informed-sampling-based-mpc-for-multi-fingered-dexterous-manipulation.
Harvard
Küçüktabak, E. B., Patel, K., Cui, J., Yang, Z., Sasabuchi, K. and Takamatsu, J. (2026) 'Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation', Available at: https://omanscience.com/en/articles/primitive-informed-sampling-based-mpc-for-multi-fingered-dexterous-manipulation.
Vancouver
Küçüktabak EB, Patel K, Cui J, Yang Z, Sasabuchi K, Takamatsu J. Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation. https://omanscience.com/en/articles/primitive-informed-sampling-based-mpc-for-multi-fingered-dexterous-manipulation
IEEE
E. B. Küçüktabak, K. Patel, J. Cui, Z. Yang, K. Sasabuchi, and J. Takamatsu, "Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation," https://omanscience.com/en/articles/primitive-informed-sampling-based-mpc-for-multi-fingered-dexterous-manipulation.