Temporal ImplicitRDP

# Temporal ImplicitRDP ## Project Title Temporal ImplicitRDP: Enhancing Real-Time Adaptability in Dynamic Environments ## Research Hypothesis Integrating a temporal prediction module with the ImplicitRDP framework can enhance its ability to anticipate environmental changes in real-time, improving performance in dynamic environments. ## Implementation Approach This project implements a temporal prediction module using recurrent neural networks (RNNs) or transformers to anticipate future states based on current visual and force inputs. The module is trained alongside the existing ImplicitRDP framework using datasets containing sequences of dynamic manipulation tasks. ## Setup Instructions 1. Clone the repository: ```bash git clone cd temporal_implicit_rdp ``` 2. Install Python dependencies: ```bash pip install -r requirements.txt ``` ## Usage Examples ### Training To train the model, run: ```bash python src/train.py ``` ### Evaluation To evaluate the model in a simulated environment, run: ```bash python src/evaluate.py ``` ## Expected Results The project aims to improve the adaptability and robustness of the ImplicitRDP framework in dynamic environments, potentially benefiting applications such as autonomous vehicles or robotic surgery. ## References - ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning [Paper URL](http://arxiv.org/abs/2512.10946v1)