# Rapid Motor Adaptation for Robotic Manipulator Arms

arXiv: https://arxiv.org/abs/2312.04670 · HTML: https://mlsanity.com/paper/2312.04670/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 45 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills](https://mlsanity.com/paper/2302.04659/) (2023)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality](https://mlsanity.com/paper/2210.13702/) (2022)
- [Preparing for the Unknown: Learning a Universal Policy with Online System Identification](https://mlsanity.com/paper/1702.02453/) (2017)
- [In-Hand Object Rotation via Rapid Motor Adaptation](https://mlsanity.com/paper/2210.04887/) (2022)
- [Sim-to-Real Transfer for Vision-and-Language Navigation](https://mlsanity.com/paper/2011.03807/) (2020)
- [Data-efficient Deep Reinforcement Learning for Dexterous Manipulation](https://mlsanity.com/paper/1704.03073/) (2017)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [Policy Transfer with Strategy Optimization](https://mlsanity.com/paper/1810.05751/) (2018)
- [DeceptionNet: Network-Driven Domain Randomization](https://mlsanity.com/paper/1904.02750/) (2019)
- [One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors](https://mlsanity.com/paper/1509.06841/) (2015)
- [3DP3: 3D Scene Perception via Probabilistic Programming](https://mlsanity.com/paper/2111.00312/) (2021)
- [Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1905.04100/) (2019)
- [Pick and Place Without Geometric Object Models](https://mlsanity.com/paper/1707.05615/) (2017)

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

- [VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning](https://mlsanity.com/paper/2410.23156/) (2024)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Towards Cross-View Point Correspondence in Vision-Language Models](https://mlsanity.com/paper/2512.04686/) (2025)
- [Efficient Imitation under Misspecification](https://mlsanity.com/paper/2503.13162/) (2025)
- [Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation](https://mlsanity.com/paper/2510.11689/) (2025)
- [Following the Human Thread in Social Navigation](https://mlsanity.com/paper/2404.11327/) (2024)
- [Action Flow Matching for Continual Robot Learning](https://mlsanity.com/paper/2504.18471/) (2025)
- [Situationally-Aware Dynamics Learning](https://mlsanity.com/paper/2505.19574/) (2025)
- [CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation](https://mlsanity.com/paper/2605.22082/) (2026)
- [Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation](https://mlsanity.com/paper/2604.22102/) (2026)
- [Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation](https://mlsanity.com/paper/2606.02280/) (2026)
- [TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation](https://mlsanity.com/paper/2606.06218/) (2026)
- [Minimal Recurrent Behavioral Memory for Imitation under Partial Observability](https://mlsanity.com/paper/2609.25757/) (2026)
- [Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation](https://mlsanity.com/paper/2610.00729/) (2026)

## Abstract (arXiv)

Developing generalizable manipulation skills is a core challenge in embodied AI. This includes generalization across diverse task configurations, encompassing variations in object shape, density, friction coefficient, and external disturbances such as forces applied to the robot. Rapid Motor Adaptation (RMA) offers a promising solution to this challenge. It posits that essential hidden variables influencing an agent's task performance, such as object mass and shape, can be effectively inferred from the agent's action and proprioceptive history. Drawing inspiration from RMA in locomotion and in-hand rotation, we use depth perception to develop agents tailored for rapid motor adaptation in a variety of manipulation tasks. We evaluated our agents on four challenging tasks from the Maniskill2 benchmark, namely pick-and-place operations with hundreds of objects from the YCB and EGAD datasets, peg insertion with precise position and orientation, and operating a variety of faucets and handles, with customized environment variations. Empirical results demonstrate that our agents surpass state-of-the-art methods like automatic domain randomization and vision-based policies, obtaining better generalization performance and sample efficiency.