Update README.md
Browse files
README.md
CHANGED
|
@@ -18,6 +18,8 @@ Notes:
|
|
| 18 |
2. Partial Observabilty in front camera. During motion some parts of the robot go off camera's frame. Can the model handle that?
|
| 19 |
3. Pick location fixed and drop location has slight variations (considering the difficulty to learn to grasp a tape)
|
| 20 |
|
|
|
|
|
|
|
| 21 |
|
| 22 |
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
|
| 23 |
|
|
|
|
| 18 |
2. Partial Observabilty in front camera. During motion some parts of the robot go off camera's frame. Can the model handle that?
|
| 19 |
3. Pick location fixed and drop location has slight variations (considering the difficulty to learn to grasp a tape)
|
| 20 |
|
| 21 |
+
Train logs: https://api.wandb.ai/links/ramachandranaadarsh-indian-institute-of-technology-madras/bf8lft8i
|
| 22 |
+
s
|
| 23 |
|
| 24 |
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
|
| 25 |
|