# Context is Everything: Implicit Identification for Dynamics Adaptation

arXiv: https://arxiv.org/abs/2203.05549 · HTML: https://mlsanity.com/paper/2203.05549/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 10 Mar 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https://mlsanity.com/paper/1709.07857/) (2017)
- [Preparing for the Unknown: Learning a Universal Policy with Online System Identification](https://mlsanity.com/paper/1702.02453/) (2017)
- [EPOpt: Learning Robust Neural Network Policies Using Model Ensembles](https://mlsanity.com/paper/1610.01283/) (2016)
- [Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2005.06800/) (2020)
- [Evolving Reinforcement Learning Algorithms](https://mlsanity.com/paper/2101.03958/) (2021)
- [Environment Probing Interaction Policies](https://mlsanity.com/paper/1907.11740/) (2019)
- [Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads](https://mlsanity.com/paper/2004.11345/) (2020)
- [One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors](https://mlsanity.com/paper/1509.06841/) (2015)
- [Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors](https://mlsanity.com/paper/2003.04663/) (2020)
- [Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system](https://mlsanity.com/paper/1803.10371/) (2018)

## Built on by (most cited first, 27 of 27)

- [PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videos](https://mlsanity.com/paper/2503.17973/) (2025)
- [AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation](https://mlsanity.com/paper/2407.07889/) (2024)
- [Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/2503.24361/) (2025)
- [Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling](https://mlsanity.com/paper/2410.18912/) (2024)
- [The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects](https://mlsanity.com/paper/2306.00956/) (2023)
- [PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation](https://mlsanity.com/paper/2504.16693/) (2025)
- [AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer](https://mlsanity.com/paper/2302.04903/) (2023)
- [Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control](https://mlsanity.com/paper/2210.12583/) (2022)
- [That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation](https://mlsanity.com/paper/2210.01116/) (2022)
- [Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications](https://mlsanity.com/paper/2410.20357/) (2024)
- [PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis](https://mlsanity.com/paper/2510.21447/) (2025)
- [Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning](https://mlsanity.com/paper/2404.09521/) (2024)
- [Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization](https://mlsanity.com/paper/2508.20294/) (2025)
- [NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos](https://mlsanity.com/paper/2511.08310/) (2025)
- [LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations](https://mlsanity.com/paper/2508.17547/) (2025)
- [Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policies](https://mlsanity.com/paper/2511.04249/) (2025)
- [Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics](https://mlsanity.com/paper/2304.12289/) (2023)
- [Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2406.08238/) (2024)
- [Learning Physics-Guided Residual Dynamics for Deformable Object Simulation](https://mlsanity.com/paper/2607.13451/) (2026)
- [Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation](https://mlsanity.com/paper/2209.12029/) (2022)
- [Beyond Context: Large Language Models' Failure to Grasp Users' Intent](https://mlsanity.com/paper/2512.21110/) (2025)
- [DADP: Domain Adaptive Diffusion Policy](https://mlsanity.com/paper/2602.04037/) (2026)
- [Contextual Intelligence The Next Leap for Reinforcement Learning](https://mlsanity.com/paper/2604.02348/) (2026)
- [One-shot World Models Using a Transformer Trained on a Synthetic Prior](https://mlsanity.com/paper/2409.14084/) (2024)
- [AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control](https://mlsanity.com/paper/2510.05443/) (2025)
- [MATE: Solving Contextual Markov Decision Processes with Memory of Accumulated Transition Embeddings](https://mlsanity.com/paper/2605.17431/) (2026)
- [Dynamic Context Scheduling: Learning Beyond the Static Universe](https://mlsanity.com/paper/2608.20799/) (2026)

## Abstract (arXiv)

Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables such as environment parameters cannot necessarily be precisely measured or inferred, even during training. We propose Implicit Identification for Dynamics Adaptation (IIDA), a simple method to allow predictive models to adapt to changing environment dynamics. IIDA assumes no access to the true variations in the world and instead implicitly infers properties of the environment from a small amount of contextual data. We demonstrate IIDA's ability to perform well in unseen environments through a suite of simulated experiments on MuJoCo environments and a real robot dynamic sliding task. In general, IIDA significantly reduces model error and results in higher task performance over commonly used methods. Our code and robot videos are at https://bennevans.github.io/iida/