update readme

Author: xuanlinli17

README.md

@@ -19,7 +19,6 @@ We hope that our work guides and inspires future real-to-sim evaluation efforts.
   - [Installation](#installation)
   - [Examples](#examples)
   - [Current Environments](#current-environments)
-  - [Customizing Evaluation Configs](#customizing-evaluation-configs)
   - [Metrics for Assessing the Effectiveness of Simulated Evaluation Pipelines](#metrics-for-assessing-the-effectiveness-of-simulated-evaluation-pipelines)
   - [Code Structure](#code-structure)
   - [Adding New Policies](#adding-new-policies)
@@ -101,7 +100,7 @@ pip install -e .
 - Environment interactive visualization and manual control: see [`ManiSkill2_real2sim/mani_skill2_real2sim/examples/demo_manual_control_custom_envs.py`](https://github.com/simpler-env/ManiSkill2_real2sim/blob/main/mani_skill2_real2sim/examples/demo_manual_control_custom_envs.py)
 - Simple RT-1 and Octo evaluation script on prepackaged environments with visual matching evaluation setup: see [`simpler_env/simple_inference_visual_matching_prepackaged_envs.py`](https://github.com/simpler-env/SimplerEnv/blob/main/simpler_env/simple_inference_visual_matching_prepackaged_envs.py).
 - Colab notebook for RT-1 and Octo inference: see [this link](https://colab.research.google.com/github/simpler-env/SimplerEnv/blob/main/example.ipynb).
-- Policy inference scripts to reproduce our Google Robot and WidowX real-to-sim evaluation results with advanced loggings. These contain both visual matching and variant aggregation evaluation setups along with RT-1, RT-1-X, and Octo policies. See [`scripts/`](https://github.com/simpler-env/SimplerEnv/tree/main/scripts).
+- Policy inference scripts to reproduce our Google Robot and WidowX real-to-sim evaluation results with sweeps over object / robot poses and advanced loggings. These contain both visual matching and variant aggregation evaluation setups along with RT-1, RT-1-X, and Octo policies. See [`scripts/`](https://github.com/simpler-env/SimplerEnv/tree/main/scripts).
 - Real-to-sim evaluation videos from running `scripts/*.sh`: see [this link](https://huggingface.co/datasets/xuanlinli17/simpler-env-eval-example-videos/tree/main).
 
 ## Current Environments
@@ -128,10 +127,6 @@ We also support creating sub-tasks variations such as `google_robot_pick_{horizo
 
 By default, Google Robot environments use a control frequency of 3hz, and Bridge environments use a control frequency of 5hz. Simulation frequency is ~500hz.
 
-## Customizing Evaluation Configs
-
-Please see `scripts/` for examples of how to customize evaluation configs. The inference script `simpler_env/main_inference.py` supports advanced environment building and logging. For example, you can perform a sweep over object and robot poses for evaluation. (Note, however, varying robot poses is not meaningful under the visual matching evaluation setup.)
-
 ## Metrics for Assessing the Effectiveness of Simulated Evaluation Pipelines
 
 In our paper, we use the Mean Maximum Rank Violation (MMRV) metric and the Pearson Correlation Coefficient metric to assess the correlation between real and simulated evaluation results. You can reproduce the metrics in `tools/calc_metrics.py` and assess your own real-to-sim evaluation pipeline.