Context is Everything: Implicit Identification for Dynamics Adaptation

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, video of the method, and latest paper is available here https://bennevans.github.io/icra-iida/

Authors

• Ben Evans
• Abitha Thankaraj
• Lerrel Pinto

Keywords

• Training
• Adaptation models
• Codes
• Automation
• Predictive models
• Task analysis
• Robots
• Implicit Identification
• Prediction Model
• Environmental Parameters
• Environmental Dimensions
• Small Amount Of Data
• Real Robot
• Neural Network
• Recurrent Neural Network
• Unknown Parameters
• System Identification
• Feed-forward Network
• Test Environment
• Average Pooling
• Current Environment
• Transformer Model
• Linker Length
• Domain Adaptation
• End Position
• Training Environment
• Meta Learning
• Explicit Identification
• Context Size
• Identification Of Modules
• Robot Experiments
• Humanoid
• Explicit Model
• Prediction Module
• Context For Example
• Friction