Mello Teleop

.gitmodules

@@ -1,6 +1,3 @@
 [submodule "gcu_objects"]
 	path = gcu_objects
 	url = https://github.com/personalrobotics/gcu_objects.git
-[submodule "rsl_rl"]
-	path = rsl_rl
-	url = https://github.com/kaikwan/rsl_rl.git

README.md

@@ -9,18 +9,6 @@
 ---
 
 ### Key Features
-#### PPO packing agent
-Notes: Currently uses multiprocessing for placement calculation, recommended `--num_envs 100`,
-This will be adapted to use depth camera (#13)
-
-**Command:**
-```bash
-# For training
-python scripts/rsl_rl/train.py --task=Isaac-Pack-NoArm-v0  --num_envs 20
-# For inference
-python scripts/rsl_rl/play.py --task=Isaac-Pack-NoArm-v0  --num_envs 20
-```
-
 
 #### Heuristic-scoring based search packing agent
 Packing agent based on *"Stable bin packing of non-convex 3D objects with a robot manipulator"* by Fan Wang and Kris Hauser.
@@ -31,15 +19,15 @@ Defaults to DBLF heuristic
 
 **Command:**
 ```bash
-python scripts/bpp_agents/fanwang_bpp_agent.py --task=Isaac-Pack-NoArm-v0 --num_envs 100
+python scripts/fanwang_bpp/fanwang_bpp_agent.py --task=Isaac-Pack-NoArm-v0 --num_envs 100
 ```
 
 #### Pack Task
 Demo for Amazon Packing Task
 
 **Command:**
 ```bash
-python scripts/bpp_agents/test_placement_agent.py --task=Isaac-Pack-NoArm-v0 --num_envs 5
+python scripts/test_placement_agent.py --task=Isaac-Pack-NoArm-v0 --num_envs 5
 ```
 
 ---
@@ -70,11 +58,9 @@ Using a Python interpreter with Isaac Lab installed, run:
 python -m pip install -e source/tote_consolidation
 python -m pip install -e source/gculab
 python -m pip install -e source/gculab_assets
-python -m pip install -e source/gculab_rl
 ```
 
 ---
-## Running Tasks with RL Agents
 
 ## Running Tasks with Dummy Agents
 
@@ -98,6 +84,25 @@ python scripts/random_agent.py --task=<TASK_NAME>
 
 ---
 
+### Test Placement Agent
+**Command:**
+```bash
+# Use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda
+python scripts/test_placement_agent.py --task=Isaac-Pack-NoArm-v0 --num_envs 5
+```
+
+---
+
+## Teleoperation and Imitation Learning
+**Command:**
+```bash
+python scripts/teleop_se3_agent.py --task Isaac-Pack-UR5-Teleop-v0 --num_envs 1 --teleop_device mello
+```
+### Teleop Device
+**Mello** is a teleoperation device designed for intuitive robot control, similar in concept to Gello. It mimics the robot's joint structure, allowing users to control the robot by physically moving the device. Joint positions are sent directly to the robot, eliminating the need for inverse kinematics or physics-based computation. Mello is especially useful for imitation learning, where human demonstrations collected via teleoperation are used to train models. Because Mello closely matches the robot’s kinematics, it enables efficient and accurate data collection for learning from demonstration.
+
+---
+
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