# DROP: Dexterous Reorientation via Online Planning

arXiv: https://arxiv.org/abs/2409.14562 · HTML: https://mlsanity.com/paper/2409.14562/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2024.

## Built on by (most cited first, 14 of 14)

- [SPIDER: Scalable Physics-Informed Dexterous Retargeting](https://mlsanity.com/paper/2511.09484/) (2025)
- [Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization](https://mlsanity.com/paper/2502.20382/) (2025)
- [V-HOP: Visuo-Haptic 6D Object Pose Tracking](https://mlsanity.com/paper/2502.17434/) (2025)
- [Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Tasks](https://mlsanity.com/paper/2502.13406/) (2025)
- [Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulation](https://mlsanity.com/paper/2510.14643/) (2025)
- [Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds](https://mlsanity.com/paper/2511.21666/) (2025)
- [Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design](https://mlsanity.com/paper/2607.12105/) (2026)
- [Real-World Reinforcement Learning with MPC Scaffolding for Dexterous Manipulation](https://mlsanity.com/paper/2609.14878/) (2026)
- [Model Tensor Planning](https://mlsanity.com/paper/2505.01059/) (2025)
- [Trajectory First: A Curriculum for Discovering Diverse Policies](https://mlsanity.com/paper/2506.01568/) (2025)
- [Tac2Motion: Contact-Aware Reinforcement Learning with Tactile Feedback for Robotic Hand Manipulation](https://mlsanity.com/paper/2509.17812/) (2025)
- [Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets](https://mlsanity.com/paper/2601.10827/) (2026)
- [Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing](https://mlsanity.com/paper/2605.16520/) (2026)
- [ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim](https://mlsanity.com/paper/2609.34010/) (2026)

## Abstract (arXiv)

Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (RL), one popular approach has been to use massively-parallelized, domain-randomized simulations to learn a policy offline over a vast array of contact conditions, allowing robust sim-to-real transfer. Inspired by recent advances in real-time parallel simulation, this work considers instead the viability of online planning methods for contact-rich manipulation by studying the well-known in-hand cube reorientation task. We propose a simple architecture that employs a sampling-based predictive controller and vision-based pose estimator to search for contact-rich control actions online. We conduct thorough experiments to assess the real-world performance of our method, architectural design choices, and key factors for robustness, demonstrating that our simple sampling-based approach achieves performance comparable to prior RL-based works. Supplemental material: https://caltech-amber.github.io/drop.