chore(robots): drive mode calibration gripper

The SO follower/leader calibration hardcoded drive_mode=0 for all motors.
When the gripper motor is mounted mirrored relative to the other arm's
(raw position increases when closing instead of decreasing), the
0=closed/100=open normalization convention flips: a 'closed' (0%)
command from the leader unnormalizes to the follower's fully-open hard
stop, slamming the gripper open and triggering the Feetech overload
protection (torque off until power cycle).

FeetechMotorsBus already supports per-motor inversion via drive_mode
(apply_drive_mode=True); calibration just never set it. This adds a
step after range recording that asks the user to fully close the
gripper, reads the raw position, and sets drive_mode=1 when the closed
position sits at range_max. No calibration file format change.

Verified against the exact calibration values reported in #3942 using
the real FeetechMotorsBus normalization: with drive_mode=0 a 0% goal
maps to raw 2035 (open, bug reproduced); with drive_mode=1 it maps to
raw 3528 (closed, expected).

Fixes #3942

AGENT_GUIDE.md

@@ -321,11 +321,10 @@ SmolVLA ships with `freeze_vision_encoder=True`. Unfreezing usually **improves p
 
 ```bash
 lerobot-train ... --policy.type=smolvla \
-  --policy.fine_tune_vision_encoder=true
+  --policy.freeze_vision_encoder=false \
+  --policy.train_expert_only=false
 ```
 
-This selectively trains the vision encoder and connector while leaving the language model frozen. Their learning rate defaults to `0.1 × optimizer_lr`; adjust it with `--policy.vision_encoder_lr_multiplier` if needed.
-
 ### 7.7 Signals to stop / keep going
 
 - Train loss plateaus → stop, save a Hub checkpoint.

docs/source/smolvla.mdx

@@ -70,19 +70,6 @@ cd lerobot && lerobot-train \
   GPU allows it, as long as loading times remain short.
 </Tip>
 
-For tasks that require adapting visual features, such as distinguishing new colors or shapes, selectively
-fine-tune the vision encoder and its connector:
-
-```bash
-lerobot-train ... \
-  --policy.path=lerobot/smolvla_base \
-  --policy.fine_tune_vision_encoder=true
-```
-
-This keeps the language model frozen with the default `train_expert_only=true` setting and trains the vision
-path at `0.1` times the main learning rate by default. Fine-tuning the vision encoder increases memory use and
-can reduce the model's general visual knowledge, so enable it only when the frozen encoder is insufficient.
-
 Fine-tuning is an art. For a complete overview of the options for finetuning, run
 
 ```bash

src/lerobot/policies/smolvla/configuration_smolvla.py

@@ -67,8 +67,6 @@ class SmolVLAConfig(PreTrainedConfig):
 
     # Finetuning settings
     freeze_vision_encoder: bool = True
-    fine_tune_vision_encoder: bool = False  # Fine-tune vision + connector; takes priority over freezing.
-    vision_encoder_lr_multiplier: float = 0.1
     train_expert_only: bool = True
     train_state_proj: bool = True
 
@@ -112,12 +110,6 @@ class SmolVLAConfig(PreTrainedConfig):
         super().__post_init__()
 
         """Input validation (not exhaustive)."""
-        if self.fine_tune_vision_encoder:
-            self.freeze_vision_encoder = False
-        if self.vision_encoder_lr_multiplier <= 0:
-            raise ValueError(
-                f"`vision_encoder_lr_multiplier` must be positive, got {self.vision_encoder_lr_multiplier}."
-            )
         if self.n_action_steps > self.chunk_size:
             raise ValueError(
                 f"The chunk size is the upper bound for the number of action steps per model invocation. Got "

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -186,27 +186,8 @@ class SmolVLAPolicy(PreTrainedPolicy):
             if model_value is not None:
                 model_value.rtc_processor = self.rtc_processor
 
-    def get_optim_params(self):
-        if not self.config.fine_tune_vision_encoder:
-            return self.parameters()
-
-        vision_params = []
-        other_params = []
-        for name, param in self.named_parameters():
-            if not param.requires_grad:
-                continue
-            if ".vision_model." in name or ".connector." in name:
-                vision_params.append(param)
-            else:
-                other_params.append(param)
-
-        return [
-            {"params": other_params},
-            {
-                "params": vision_params,
-                "lr": self.config.optimizer_lr * self.config.vision_encoder_lr_multiplier,
-            },
-        ]
+    def get_optim_params(self) -> dict:
+        return self.parameters()
 
     def _get_action_chunk(
         self, batch: dict[str, Tensor], noise: Tensor | None = None, **kwargs: Unpack[ActionSelectKwargs]
@@ -512,7 +493,6 @@ class VLAFlowMatching(nn.Module):
         self.vlm_with_expert = SmolVLMWithExpertModel(
             model_id=self.config.vlm_model_name,
             freeze_vision_encoder=self.config.freeze_vision_encoder,
-            fine_tune_vision_encoder=self.config.fine_tune_vision_encoder,
             train_expert_only=self.config.train_expert_only,
             load_vlm_weights=self.config.load_vlm_weights,
             attention_mode=self.config.attention_mode,

src/lerobot/policies/smolvla/smolvlm_with_expert.py

@@ -78,7 +78,6 @@ class SmolVLMWithExpertModel(nn.Module):
         load_vlm_weights: bool = True,
         train_expert_only: bool = True,
         freeze_vision_encoder: bool = False,
-        fine_tune_vision_encoder: bool = False,
         attention_mode: str = "self_attn",
         num_expert_layers: int = -1,
         num_vlm_layers: int = -1,
@@ -142,7 +141,6 @@ class SmolVLMWithExpertModel(nn.Module):
         self.num_key_value_heads = self.config.text_config.num_key_value_heads
 
         self.freeze_vision_encoder = freeze_vision_encoder
-        self.fine_tune_vision_encoder = fine_tune_vision_encoder
         self.train_expert_only = train_expert_only
         self.attention_mode = attention_mode
         self.expert_hidden_size = lm_expert_config.hidden_size
@@ -152,6 +150,10 @@ class SmolVLMWithExpertModel(nn.Module):
         return self.vlm.model
 
     def set_requires_grad(self):
+        if self.freeze_vision_encoder:
+            self.get_vlm_model().vision_model.eval()
+            for params in self.get_vlm_model().vision_model.parameters():
+                params.requires_grad = False
         if self.train_expert_only:
             self.vlm.eval()
             for params in self.vlm.parameters():
@@ -174,18 +176,6 @@ class SmolVLMWithExpertModel(nn.Module):
             for name, params in self.vlm.named_parameters():
                 if any(k in name for k in frozen_layers):
                     params.requires_grad = False
-
-        if self.freeze_vision_encoder:
-            self.get_vlm_model().vision_model.eval()
-            for params in self.get_vlm_model().vision_model.parameters():
-                params.requires_grad = False
-
-        if self.fine_tune_vision_encoder:
-            for params in self.get_vlm_model().vision_model.parameters():
-                params.requires_grad = True
-            for params in self.get_vlm_model().connector.parameters():
-                params.requires_grad = True
-
         # To avoid unused params issue with distributed training
         for name, params in self.lm_expert.named_parameters():
             if "lm_head" in name:
@@ -194,15 +184,11 @@ class SmolVLMWithExpertModel(nn.Module):
     def train(self, mode: bool = True):
         super().train(mode)
 
-        if self.train_expert_only:
-            self.vlm.eval()
-
         if self.freeze_vision_encoder:
             self.get_vlm_model().vision_model.eval()
 
-        if self.fine_tune_vision_encoder:
-            self.get_vlm_model().vision_model.train(mode)
-            self.get_vlm_model().connector.train(mode)
+        if self.train_expert_only:
+            self.vlm.eval()
 
     def embed_image(self, image: torch.Tensor):
         patch_attention_mask = None

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -510,10 +510,10 @@ class ForwardKinematicsJointsToEEAction(RobotActionProcessorStep):
         # We only use the ee pose in the dataset, so we don't need the joint positions
         for n in self.motor_names:
             features[PipelineFeatureType.ACTION].pop(f"{n}.pos", None)
-        # Store end-effector features as actions in the dataset schema
+        # We specify the dataset features of this step that we want to be stored in the dataset
         for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]:
             features[PipelineFeatureType.ACTION][f"ee.{k}"] = PolicyFeature(
-                type=FeatureType.ACTION, shape=(1,)
+                type=FeatureType.STATE, shape=(1,)
             )
         return features
 

src/lerobot/robots/so_follower/so_follower.py

@@ -142,11 +142,25 @@ class SOFollower(Robot):
         range_mins[full_turn_motor] = 0
         range_maxes[full_turn_motor] = 4095
 
+        drive_modes = dict.fromkeys(self.bus.motors, 0)
+        input(f"Fully close the gripper of {self} and press ENTER....")
+        gripper_closed_pos = self.bus.read(
+            "Present_Position", "gripper", normalize=False, num_retry=self.config.num_read_retries
+        )
+        distance_to_min = abs(gripper_closed_pos - range_mins["gripper"])
+        distance_to_max = abs(gripper_closed_pos - range_maxes["gripper"])
+        if min(distance_to_min, distance_to_max) > (range_maxes["gripper"] - range_mins["gripper"]) * 0.2:
+            raise ValueError("Gripper is not fully closed. Run calibration again.")
+
+        drive_modes["gripper"] = int(distance_to_max < distance_to_min)
+        if drive_modes["gripper"]:
+            logger.info("Gripper motor is inverted, setting drive_mode=1 to compensate.")
+
         self.calibration = {}
         for motor, m in self.bus.motors.items():
             self.calibration[motor] = MotorCalibration(
                 id=m.id,
-                drive_mode=0,
+                drive_mode=drive_modes[motor],
                 homing_offset=homing_offsets[motor],
                 range_min=range_mins[motor],
                 range_max=range_maxes[motor],

src/lerobot/teleoperators/so_leader/so_leader.py

@@ -110,11 +110,25 @@ class SOLeader(Teleoperator):
         range_mins[full_turn_motor] = 0
         range_maxes[full_turn_motor] = 4095
 
+        drive_modes = dict.fromkeys(self.bus.motors, 0)
+        input(f"Fully close the gripper of {self} and press ENTER....")
+        gripper_closed_pos = self.bus.read(
+            "Present_Position", "gripper", normalize=False, num_retry=self.config.num_read_retries
+        )
+        distance_to_min = abs(gripper_closed_pos - range_mins["gripper"])
+        distance_to_max = abs(gripper_closed_pos - range_maxes["gripper"])
+        if min(distance_to_min, distance_to_max) > (range_maxes["gripper"] - range_mins["gripper"]) * 0.2:
+            raise ValueError("Gripper is not fully closed. Run calibration again.")
+
+        drive_modes["gripper"] = int(distance_to_max < distance_to_min)
+        if drive_modes["gripper"]:
+            logger.info("Gripper motor is inverted, setting drive_mode=1 to compensate.")
+
         self.calibration = {}
         for motor, m in self.bus.motors.items():
             self.calibration[motor] = MotorCalibration(
                 id=m.id,
-                drive_mode=0,
+                drive_mode=drive_modes[motor],
                 homing_offset=homing_offsets[motor],
                 range_min=range_mins[motor],
                 range_max=range_maxes[motor],

tests/robots/test_robot_kinematic_processor.py

@@ -1,45 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pytest
-
-from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEEAction,
-    ForwardKinematicsJointsToEEObservation,
-)
-
-MOTOR_NAMES = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"]
-EE_KEYS = {f"ee.{k}" for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]}
-
-
-def _joint_bucket(feature_type: FeatureType) -> dict[str, PolicyFeature]:
-    return {f"{n}.pos": PolicyFeature(type=feature_type, shape=(1,)) for n in MOTOR_NAMES}
-
-
-@pytest.mark.parametrize(
-    ("step_cls", "bucket", "feature_type"),
-    [
-        (ForwardKinematicsJointsToEEAction, PipelineFeatureType.ACTION, FeatureType.ACTION),
-        (ForwardKinematicsJointsToEEObservation, PipelineFeatureType.OBSERVATION, FeatureType.STATE),
-    ],
-)
-def test_fk_feature_schema(step_cls, bucket, feature_type):
-    features = {PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: {}}
-    features[bucket] = _joint_bucket(feature_type)
-    out = step_cls(kinematics=None, motor_names=MOTOR_NAMES).transform_features(features)[bucket]
-    assert set(out) == EE_KEYS
-    assert {feature.type for feature in out.values()} == {feature_type}

tests/robots/test_so100_follower.py

@@ -149,3 +149,51 @@ def test_configure_writes_position_pid_coefficients():
     bus_mock.write.assert_any_call("P_Coefficient", "shoulder_pan", 32)
     bus_mock.write.assert_any_call("I_Coefficient", "shoulder_pan", 1)
     bus_mock.write.assert_any_call("D_Coefficient", "shoulder_pan", 16)
+
+
+@pytest.mark.parametrize(
+    "gripper_closed_pos, expected_drive_mode",
+    [
+        (2035, 0),  # closed position at range_min -> raw increases when opening -> not inverted
+        (3528, 1),  # closed position at range_max -> raw increases when closing -> inverted
+        (2781, None),  # not near either end stop -> unsafe to infer
+    ],
+)
+def test_calibrate_detects_gripper_drive_mode(follower, gripper_closed_pos, expected_drive_mode):
+    """Regression test for #3942: the follower gripper can be mounted mirrored with respect to the
+    leader's, in which case its raw position increases when closing. Calibration must detect this
+    and set drive_mode=1 so that normalized values follow the 0=closed/100=open convention."""
+    follower.connect()
+
+    motors = list(follower.bus.motors)
+    follower.bus.set_half_turn_homings.return_value = dict.fromkeys(motors, 0)
+    follower.bus.record_ranges_of_motion.return_value = (
+        dict.fromkeys(motors, 2035),
+        dict.fromkeys(motors, 3528),
+    )
+    follower.bus.read.return_value = gripper_closed_pos
+
+    with (
+        patch("builtins.input", return_value=""),
+        patch.object(type(follower), "_save_calibration", lambda self: None),
+    ):
+        follower.calibration = {}
+        if expected_drive_mode is None:
+            with pytest.raises(ValueError, match="Gripper is not fully closed"):
+                follower.calibrate()
+        else:
+            follower.calibrate()
+
+    follower.bus.read.assert_called_with(
+        "Present_Position",
+        "gripper",
+        normalize=False,
+        num_retry=follower.config.num_read_retries,
+    )
+    if expected_drive_mode is None:
+        return
+
+    assert follower.calibration["gripper"].drive_mode == expected_drive_mode
+    for motor in motors:
+        if motor != "gripper":
+            assert follower.calibration[motor].drive_mode == 0