Update README.md

README.md

@@ -24,7 +24,7 @@ pretty_name: Community Dataset v3
 
 A large-scale robotics dataset for vision-language-action learning, featuring **791 datasets** across **46 robot types**, enabling cross-embodiment pretraining for generalist robot policies.
 
-![Community Dataset v3](./comm_v3.png)
+![3](https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/5v1NlxUHR_HV0tXYTK0ux.png)
 
 ## Overview
 
@@ -40,7 +40,7 @@ The dataset spans **46+ robot embodiments** including single-arm, bimanual, mobi
 | **Total Datasets** | 791 |
 | **Total Episodes** | 50,622 |
 | **Total Frames** | 25,971,082 |
-| **Total Duration** | 240.47 hours (10.02 days) |
+| **Total Duration** | 251.5 hours (10.5 days) |
 | **Contributors** | 235 |
 | **Robot Types** | 46 different embodiments |
 | **Action Dimensions** | 12 different configurations |
@@ -49,11 +49,11 @@ The dataset spans **46+ robot embodiments** including single-arm, bimanual, mobi
 ## 🤖 Robot Type Distribution
 
 ### By Category
-- **Single-arm manipulators**: 72% (571 datasets)
-- **Bimanual systems**: 12% (95 datasets)
-- **Mobile manipulation**: 8% (63 datasets)
-- **Humanoid platforms**: 1% (8 datasets)
-- **Other configurations**: 7% (54 datasets)
+- **Single-arm manipulators**: 88.4% (699 datasets)
+- **Bimanual systems**: 6.7% (53 datasets)
+- **Mobile manipulation**: 3.4% (27 datasets)
+- **Humanoid platforms**: 1.3% (10 datasets)
+- **Other configurations**: 0.3% (2 datasets)
 
 ### Top 10 Robot Types
 
@@ -70,7 +70,8 @@ The dataset spans **46+ robot embodiments** including single-arm, bimanual, mobi
 | **so100_bimanual** | 12 | 1.5% | Bimanual |
 | **koch_follower** | 8 | 1.0% | Single-arm |
 
-![Screenshot 2025-12-09 at 17.50.51](https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/uVr2KrVm2crT7evSFCaEA.png)
+
+![6](https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/Trg81uewHlGHH30NZukK5.png)
 
 ## 🗂️ Dataset Structure
 
@@ -160,7 +161,7 @@ accelerate launch --config_file accelerate_configs/multi_gpu.yaml \
     --wandb.project="smolvla2-cross-embodiment"
 ```
 
-## 🔧 Training Challenges with Cross-Embodiment Data
+##  Training Challenges with Cross-Embodiment Data
 
 ### The Reality of Community-Contributed Data
 
@@ -170,7 +171,7 @@ This dataset includes 791 datasets recorded by community members under different
 
 Starting with 851 datasets, we systematically debugged and cleaned the collection. Here's what we found:
 
-#### Missing Video Files (Primary Removal Reason)
+#### 1. Missing Video Files (Primary Removal Reason)
 Some datasets had incomplete episode recordings where video files were missing:
 ```
 ERROR Failed to load video for key 'observation.images.image' at episode X: 
@@ -179,7 +180,7 @@ ERROR Failed to load video for key 'observation.images.image' at episode X:
 **Impact:** Training crashes when these episodes were sampled  
 **Action:** Removed ~15-20 datasets with missing files
 
-#### Data Type Incompatibilities
+#### 2. Data Type Incompatibilities
 Certain datasets returned inconsistent data types during batch formation:
 ```
 RuntimeError: Could not infer dtype of dict
@@ -188,7 +189,7 @@ AttributeError: 'list' object has no attribute 'device'
 **Impact:** Random crashes during forward pass  
 **Action:** Removed ~10-15 problematic datasets, implemented resilient batch collation
 
-#### Multi-Camera Configuration Issues
+#### 3. Multi-Camera Configuration Issues
 Different datasets had varying numbers of camera views, causing tensor shape mismatches:
 
 **Root cause:** The `max_num_images` parameter wasn't properly propagated in the codebase, leading to inconsistent image tensor shapes when datasets had different numbers of cameras (some had 2, others had 4+ views).
@@ -196,7 +197,7 @@ Different datasets had varying numbers of camera views, causing tensor shape mis
 **Impact:** Thousands of dimension/channel erros for the datasets with more than 3 images. 
 **Action:** Set `config.max_num_images = 3` to standardize input. This number balances multi-view information (essential for spatial reasoning) while being compatible with most datasets in the collection - the majority of community datasets use 2-3 camera views for manipulation tasks.
 
-#### Video Timing Misalignments
+#### 4. Video Timing Misalignments
 Frame timestamps occasionally violated tolerance thresholds:
 ```
 Some query timestamps violate tolerance (tensor([2.0667]) > tolerance_s=0.0001)
@@ -204,7 +205,7 @@ Some query timestamps violate tolerance (tensor([2.0667]) > tolerance_s=0.0001)
 **Impact:** Minor temporal inconsistency, but training continued  
 **Action:** Automatic fallback to closest frames
 
-#### Final Dataset Cleaning Results
+### Final Dataset Cleaning Results
 - **Original datasets:** 851
 - **Datasets with missing files:** ~15-20 (removed)
 - **Datasets with data type issues:** ~10-15 (removed)
@@ -239,7 +240,7 @@ This dataset enables:
 | **Ryosei2** | 17 | 2.1% |
 | **kyomangold** | 16 | 2.0% |
 | **psg777** | 16 | 2.0% |
-| Others (225) | 521 | 65.9% |
+
 
 ## 🤝 Contributing