# On the Feasibility of A Mixed-Method Approach for Solving Long Horizon Task-Oriented Dexterous Manipulation arXiv: https://arxiv.org/abs/2410.07403 ยท HTML: https://mlsanity.com/paper/2410.07403/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. - Builds on 28 of its 40 references that are in the map. - First posted on arXiv 9 Oct 2024. ## Placed in - Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/) ## Builds on (most cited first, 28 of 28) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023) - [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017) - [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021) - [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018) - [DexMV: Imitation Learning for Dexterous Manipulation from Human Videos](https://mlsanity.com/paper/2108.05877/) (2021) - [AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System](https://mlsanity.com/paper/2307.04577/) (2023) - [DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality](https://mlsanity.com/paper/2210.13702/) (2022) - [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022) - [Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes](https://mlsanity.com/paper/2211.11744/) (2022) - [EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning](https://mlsanity.com/paper/1807.08364/) (2018) - [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023) - [Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention](https://mlsanity.com/paper/2104.11203/) (2021) - [Data Quality in Imitation Learning](https://mlsanity.com/paper/2306.02437/) (2023) - [Deep Learning Approaches to Grasp Synthesis: A Review](https://mlsanity.com/paper/2207.02556/) (2022) - [Holo-Dex: Teaching Dexterity with Immersive Mixed Reality](https://mlsanity.com/paper/2210.06463/) (2022) - [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023) - [Learning Generalizable Dexterous Manipulation from Human Grasp Affordance](https://mlsanity.com/paper/2204.02320/) (2022) - [Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands](https://mlsanity.com/paper/2208.12250/) (2022) - [Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation](https://mlsanity.com/paper/2309.00987/) (2023) - [Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger](https://mlsanity.com/paper/2108.09779/) (2021) - [Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled Hand](https://mlsanity.com/paper/2204.03698/) (2022) - [Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction](https://mlsanity.com/paper/2207.03395/) (2022) - [On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing](https://mlsanity.com/paper/2109.12720/) (2021) - [Enhancing Dexterity in Robotic Manipulation via Hierarchical Contact Exploration](https://mlsanity.com/paper/2307.00383/) (2023) - [Waypoint-Based Reinforcement Learning for Robot Manipulation Tasks](https://mlsanity.com/paper/2403.13281/) (2024) - [Online augmentation of learned grasp sequence policies for more adaptable and data-efficient in-hand manipulation](https://mlsanity.com/paper/2304.02052/) (2023) - [Hybrid Learning- and Model-Based Planning and Control of In-Hand Manipulation](https://mlsanity.com/paper/2209.10040/) (2022) ## Built on by (most cited first, 1 of 1) - [Modality-Driven Design for Multi-Step Dexterous Manipulation: Insights from Neuroscience](https://mlsanity.com/paper/2412.11337/) (2024) ## Abstract (arXiv) In-hand manipulation of tools using dexterous hands in real-world is an underexplored problem in the literature. In addition to more complex geometry and larger size of the tools compared to more commonly used objects like cubes or cylinders, task oriented in-hand tool manipulation involves many sub-tasks to be performed sequentially. This may involve reaching to the tool, picking it up, reorienting it in hand with or without regrasping to reach to a desired final grasp appropriate for the tool usage, and carrying the tool to the desired pose. Research on long-horizon manipulation using dexterous hands is rather limited and the existing work focus on learning the individual sub-tasks using a method like reinforcement learning (RL) and combine the policies for different subtasks to perform a long horizon task. However, in general a single method may not be the best for all the sub-tasks, and this can be more pronounced when dealing with multi-fingered hands manipulating objects with complex geometry like tools. In this paper, we investigate the use of a mixed-method approach to solve for the long-horizon task of tool usage and we use imitation learning, reinforcement learning and model based control. We also discuss a new RL-based teacher-student framework that combines real world data into offline training. We show that our proposed approach for each subtask outperforms the commonly adopted reinforcement learning approach across different subtasks and in performing the long horizon task in simulation. Finally we show the successful transferability to real world.