# Learning Dexterous Manipulation Policies from Experience and Imitation

arXiv: https://arxiv.org/abs/1611.05095 · HTML: https://mlsanity.com/paper/1611.05095/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2016.

## Built on by (most cited first, 30 of 33)

- [ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations](https://mlsanity.com/paper/2107.14483/) (2021)
- [A System for General In-Hand Object Re-Orientation](https://mlsanity.com/paper/2111.03043/) (2021)
- [DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality](https://mlsanity.com/paper/2210.13702/) (2022)
- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://mlsanity.com/paper/1802.09564/) (2018)
- [In-Hand Object Rotation via Rapid Motor Adaptation](https://mlsanity.com/paper/2210.04887/) (2022)
- [UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning](https://mlsanity.com/paper/2304.00464/) (2023)
- [Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes](https://mlsanity.com/paper/2211.11744/) (2022)
- [Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention](https://mlsanity.com/paper/2104.11203/) (2021)
- [COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning](https://mlsanity.com/paper/2010.14500/) (2020)
- [Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation](https://mlsanity.com/paper/2203.13251/) (2022)
- [A Workflow for Offline Model-Free Robotic Reinforcement Learning](https://mlsanity.com/paper/2109.10813/) (2021)
- [Holo-Dex: Teaching Dexterity with Immersive Mixed Reality](https://mlsanity.com/paper/2210.06463/) (2022)
- [Learning Generalizable Dexterous Manipulation from Human Grasp Affordance](https://mlsanity.com/paper/2204.02320/) (2022)
- [Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning](https://mlsanity.com/paper/2111.03062/) (2021)
- [Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations](https://mlsanity.com/paper/2207.05053/) (2022)
- [Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps](https://mlsanity.com/paper/2209.11221/) (2022)
- [Lessons from Learning to Spin "Pens"](https://mlsanity.com/paper/2407.18902/) (2024)
- [Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance](https://mlsanity.com/paper/2212.09902/) (2022)
- [SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation](https://mlsanity.com/paper/2310.16838/) (2023)
- [DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation](https://mlsanity.com/paper/2207.01971/) (2022)
- [REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation](https://mlsanity.com/paper/2309.03322/) (2023)
- [Vegetable Peeling: A Case Study in Constrained Dexterous Manipulation](https://mlsanity.com/paper/2407.07884/) (2024)
- [Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming](https://mlsanity.com/paper/2106.11417/) (2021)
- [All the Feels: A dexterous hand with large-area tactile sensing](https://mlsanity.com/paper/2210.15658/) (2022)
- [Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum](https://mlsanity.com/paper/2303.12726/) (2023)
- [RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands](https://mlsanity.com/paper/2408.11048/) (2024)
- [Diffusion-Informed Probabilistic Contact Search for Multi-Finger Manipulation](https://mlsanity.com/paper/2410.00841/) (2024)
- [Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control](https://mlsanity.com/paper/2510.10516/) (2025)
- [Real-World Reinforcement Learning with MPC Scaffolding for Dexterous Manipulation](https://mlsanity.com/paper/2609.14878/) (2026)
- [Design of an Affordable Prosthetic Arm Equipped with Deep Learning Vision-Based Manipulation](https://mlsanity.com/paper/2103.02099/) (2021)

## Abstract (arXiv)

We explore learning-based approaches for feedback control of a dexterous five-finger hand performing non-prehensile manipulation. First, we learn local controllers that are able to perform the task starting at a predefined initial state. These controllers are constructed using trajectory optimization with respect to locally-linear time-varying models learned directly from sensor data. In some cases, we initialize the optimizer with human demonstrations collected via teleoperation in a virtual environment. We demonstrate that such controllers can perform the task robustly, both in simulation and on the physical platform, for a limited range of initial conditions around the trained starting state. We then consider two interpolation methods for generalizing to a wider range of initial conditions: deep learning, and nearest neighbors. We find that nearest neighbors achieve higher performance. Nevertheless, the neural network has its advantages: it uses only tactile and proprioceptive feedback but no visual feedback about the object (i.e. it performs the task blind) and learns a time-invariant policy. In contrast, the nearest neighbors method switches between time-varying local controllers based on the proximity of initial object states sensed via motion capture. While both generalization methods leave room for improvement, our work shows that (i) local trajectory-based controllers for complex non-prehensile manipulation tasks can be constructed from surprisingly small amounts of training data, and (ii) collections of such controllers can be interpolated to form more global controllers. Results are summarized in the supplementary video: https://youtu.be/E0wmO6deqjo