testing adapting aloha

lerobot/common/policies/pi0fast/config.json

@@ -0,0 +1,82 @@
+{
+    "type": "pi0fast",
+    "n_obs_steps": 1,
+    "normalization_mapping": {
+        "VISUAL": "IDENTITY",
+        "STATE": "MEAN_STD",
+        "ACTION": "MEAN_STD"
+    },
+    "input_features": {
+        "observation.image": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                256,
+                256
+            ]
+        },
+        "observation.image2": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                256,
+                256
+            ]
+        },
+        "observation.image3": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                256,
+                256
+            ]
+        },
+        "observation.state": {
+            "type": "STATE",
+            "shape": [
+                8
+            ]
+        }
+    },
+    "output_features": {
+        "action": {
+            "type": "ACTION",
+            "shape": [
+                7
+            ]
+        }
+    },
+    "use_env_state": true,
+    "exclude_image_keys": "",
+    "normalize_per_robot_type": false,
+    "chunk_size": 10,
+    "n_action_steps": 5,
+    "max_state_dim": 32,
+    "max_action_dim": 32,
+    "resize_imgs_with_padding": [
+        224,
+        224
+    ],
+    "interpolate_like_pi": false,
+    "empty_cameras": 0,
+    "adapt_to_pi_aloha": false,
+    "use_delta_joint_actions_aloha": false,
+    "tokenizer_max_length": 48,
+    "proj_width": 1024,
+    "max_decoding_steps": 256,
+    "fast_skip_tokens": 128,
+    "max_input_seq_len": 256,
+    "use_cache": true,
+    "freeze_vision_encoder": true,
+    "freeze_lm_head": true,
+    "optimizer_lr": 0.0001,
+    "optimizer_betas": [
+        0.9,
+        0.95
+    ],
+    "optimizer_eps": 1e-08,
+    "optimizer_weight_decay": 1e-10,
+    "scheduler_warmup_steps": 1000,
+    "scheduler_decay_steps": 30000,
+    "scheduler_decay_lr": 2.5e-06
+}

lerobot/common/policies/pi0fast/configuration_pi0fast.py

@@ -21,8 +21,8 @@ class PEFTConfig:
 class PI0FASTConfig(PreTrainedConfig):
     # Input / output structure.
     n_obs_steps: int = 1
-    chunk_size: int = 51
-    n_action_steps: int = 50
+    chunk_size: int = 10
+    n_action_steps: int = 5
 
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {

lerobot/common/policies/pi0fast/modeling_pi0fast.py

@@ -87,6 +87,61 @@ def display(tensor: torch.Tensor):
     print(f"Max: {tensor.max().item()}")
 
 
+def normalize(x, min_val, max_val):
+    return (x - min_val) / (max_val - min_val)
+
+
+def unnormalize(x, min_val, max_val):
+    return x * (max_val - min_val) + min_val
+
+
+def safe_arcsin(value):
+    # This ensures that the input stays within
+    # [−1,1] to avoid invalid values for arcsin
+    return torch.arcsin(torch.clamp(value, -1.0, 1.0))
+
+
+def aloha_gripper_to_angular(value):
+    # Aloha transforms the gripper positions into a linear space. The following code
+    # reverses this transformation to be consistent with pi0 which is pretrained in
+    # angular space.
+    #
+    # These values are coming from the Aloha code:
+    # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED
+    value = unnormalize(value, min_val=0.01844, max_val=0.05800)
+
+    # This is the inverse of the angular to linear transformation inside the Interbotix code.
+    def linear_to_radian(linear_position, arm_length, horn_radius):
+        value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position)
+        return safe_arcsin(value)
+
+    # The constants are taken from the Interbotix code.
+    value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022)
+
+    # Normalize to [0, 1].
+    # The values 0.4 and 1.5 were measured on an actual Trossen robot.
+    return normalize(value, min_val=0.4, max_val=1.5)
+
+
+def aloha_gripper_from_angular(value):
+    # Convert from the gripper position used by pi0 to the gripper position that is used by Aloha.
+    # Note that the units are still angular but the range is different.
+
+    # The values 0.4 and 1.5 were measured on an actual Trossen robot.
+    value = unnormalize(value, min_val=0.4, max_val=1.5)
+
+    # These values are coming from the Aloha code:
+    # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE
+    return normalize(value, min_val=-0.6213, max_val=1.4910)
+
+
+def aloha_gripper_from_angular_inv(value):
+    # Directly inverts the gripper_from_angular function.
+    value = unnormalize(value, min_val=-0.6213, max_val=1.4910)
+    return normalize(value, min_val=0.4, max_val=1.5)
+
+
+
 class PI0FASTPolicy(PreTrainedPolicy):
     """Wrapper class around PI0FAST tokenizer and model to train and run inference within LeRobot."""
 
@@ -130,6 +185,33 @@ class PI0FASTPolicy(PreTrainedPolicy):
     def get_optim_params(self) -> dict:
         return self.parameters()
 
+    def _pi_aloha_decode_state(self, state):
+        # Flip the joints.
+        for motor_idx in [1, 2, 8, 9]:
+            state[:, motor_idx] *= -1
+        # Reverse the gripper transformation that is being applied by the Aloha runtime.
+        for motor_idx in [6, 13]:
+            state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx])
+        return state
+
+    def _pi_aloha_encode_actions(self, actions):
+        # Flip the joints.
+        for motor_idx in [1, 2, 8, 9]:
+            actions[:, :, motor_idx] *= -1
+        # Reverse the gripper transformation that is being applied by the Aloha runtime.
+        for motor_idx in [6, 13]:
+            actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx])
+        return actions
+
+    def _pi_aloha_encode_actions_inv(self, actions):
+        # Flip the joints again.
+        for motor_idx in [1, 2, 8, 9]:
+            actions[:, :, motor_idx] *= -1
+        # Reverse the gripper transformation that is being applied by the Aloha runtime.
+        for motor_idx in [6, 13]:
+            actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx])
+        return actions
+    
     @torch.no_grad
     def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Select a single action given environment observations.

lerobot/common/policies/pi0fast/test_pi0fast.sh

@@ -9,8 +9,9 @@ TASK=AlohaTransferCube-v0
 REPO_ID=lerobot/aloha_sim_transfer_cube_human
 OUT_DIR=~/logs/lerobot/tmp/act_aloha_transfer
 
-EVAL_FREQ=5
+EVAL_FREQ=50
 
+POLICY_PATH=~/.cache/openpi/openpi-assets/checkpoints/pi0_fast_base_pytorch/
 POLICY=pi0fast
 
 python lerobot/scripts/train.py \