refactor(transforms): several updates

src/lerobot/transforms/transforms.py

@@ -20,6 +20,7 @@ from dataclasses import dataclass, field
 from typing import Any
 
 import torch
+from torchvision.io import decode_image, encode_jpeg
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import (
     Transform,
@@ -159,15 +160,24 @@ class GaussianNoise(Transform):
         super().__init__()
         if isinstance(std, (int, float)):
             self.std = (0.0, float(std))
-        else:
+        elif isinstance(std, Sequence) and len(std) == 2:
             self.std = (float(std[0]), float(std[1]))
+        else:
+            raise TypeError("std must be a number or a sequence with length 2.")
+        if not 0.0 <= self.std[0] <= self.std[1]:
+            raise ValueError(f"std must satisfy 0 <= min <= max, but got {self.std}.")
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        return {"std": torch.empty(1).uniform_(self.std[0], self.std[1]).item()}
+        return {
+            "std": torch.empty(1).uniform_(self.std[0], self.std[1]).item(),
+            "seed": torch.randint(0, torch.iinfo(torch.int64).max, ()).item(),
+        }
 
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
         if isinstance(inpt, torch.Tensor) and inpt.is_floating_point():
-            return (inpt + torch.randn_like(inpt) * (params["std"] / 255.0)).clamp(0.0, 1.0)
+            generator = torch.Generator(device=inpt.device).manual_seed(params["seed"])
+            noise = torch.randn(inpt.shape, device=inpt.device, dtype=inpt.dtype, generator=generator)
+            return (inpt + noise * (params["std"] / 255.0)).clamp(0.0, 1.0)
         return inpt
 
 
@@ -177,43 +187,52 @@ class MotionBlur(Transform):
     Generates a 1D averaging kernel along a random direction, applied via depthwise convolution.
 
     Args:
-        kernel_size: Range (min, max) for blur kernel size. Will be forced odd.
+        kernel_size: An odd kernel size or a range containing at least one odd kernel size.
     """
 
     def __init__(self, kernel_size: int | Sequence[int] = (3, 11)) -> None:
         super().__init__()
         if isinstance(kernel_size, int):
             self.kernel_size = (kernel_size, kernel_size)
-        else:
+        elif isinstance(kernel_size, Sequence) and len(kernel_size) == 2:
             self.kernel_size = (int(kernel_size[0]), int(kernel_size[1]))
+        else:
+            raise TypeError("kernel_size must be an int or a sequence with length 2.")
+        if not 1 <= self.kernel_size[0] <= self.kernel_size[1]:
+            raise ValueError(f"kernel_size must satisfy 1 <= min <= max, but got {self.kernel_size}.")
+        self._first_odd_kernel_size = self.kernel_size[0] + (self.kernel_size[0] + 1) % 2
+        if self._first_odd_kernel_size > self.kernel_size[1]:
+            raise ValueError(f"kernel_size range must contain an odd value, but got {self.kernel_size}.")
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        ks = int(torch.randint(self.kernel_size[0], self.kernel_size[1] + 1, (1,)).item())
+        num_odd_sizes = (self.kernel_size[1] - self._first_odd_kernel_size) // 2 + 1
-        if ks % 2 == 0:
+        size_index = int(torch.randint(0, num_odd_sizes, ()).item())
-            ks += 1
+        ks = self._first_odd_kernel_size + 2 * size_index
         angle = torch.empty(1).uniform_(0, 360).item()
         return {"kernel_size": ks, "angle": angle}
 
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
         if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
             return inpt
-        ks = params["kernel_size"]
+        if inpt.ndim < 3:
-        rad = params["angle"] * math.pi / 180
+            raise ValueError(f"MotionBlur expects [..., C, H, W] input, but got shape {inpt.shape}.")
-        cos_a, sin_a = abs(math.cos(rad)), abs(math.sin(rad))
+
-        x = inpt.unsqueeze(0) if inpt.dim() == 3 else inpt
+        kernel_size = params["kernel_size"]
-        if cos_a > sin_a:
+        radius = kernel_size // 2
-            out = torch.nn.functional.avg_pool2d(
+        angle = math.radians(params["angle"])
-                torch.nn.functional.pad(x, (ks // 2, ks // 2, 0, 0), mode="replicate"),
+        positions = torch.linspace(-radius, radius, kernel_size, device=inpt.device)
-                (1, ks),
+        x_coords = (positions * math.cos(angle)).round().to(torch.long) + radius
-                stride=1,
+        y_coords = (positions * math.sin(angle)).round().to(torch.long) + radius
-            )
+        kernel = torch.zeros((kernel_size, kernel_size), device=inpt.device, dtype=inpt.dtype)
-        else:
+        kernel[y_coords, x_coords] = 1
-            out = torch.nn.functional.avg_pool2d(
+        kernel /= kernel.sum()
-                torch.nn.functional.pad(x, (0, 0, ks // 2, ks // 2), mode="replicate"),
+
-                (ks, 1),
+        channels, height, width = inpt.shape[-3:]
-                stride=1,
+        flat_input = inpt.reshape(-1, channels, height, width)
-            )
+        depthwise_kernel = kernel.expand(channels, 1, kernel_size, kernel_size)
-        return (out.squeeze(0) if inpt.dim() == 3 else out).clamp(0.0, 1.0)
+        padded = torch.nn.functional.pad(flat_input, (radius,) * 4, mode="replicate")
+        output = torch.nn.functional.conv2d(padded, depthwise_kernel, groups=channels)
+        return output.reshape(inpt.shape).clamp(0.0, 1.0)
 
 
 class JPEGCompression(Transform):
@@ -229,8 +248,12 @@ class JPEGCompression(Transform):
         super().__init__()
         if isinstance(quality, int):
             self.quality = (quality, quality)
-        else:
+        elif isinstance(quality, Sequence) and len(quality) == 2:
             self.quality = (int(quality[0]), int(quality[1]))
+        else:
+            raise TypeError("quality must be an int or a sequence with length 2.")
+        if not 1 <= self.quality[0] <= self.quality[1] <= 100:
+            raise ValueError(f"quality must satisfy 1 <= min <= max <= 100, but got {self.quality}.")
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
         return {"quality": int(torch.randint(self.quality[0], self.quality[1] + 1, (1,)).item())}
@@ -238,16 +261,20 @@ class JPEGCompression(Transform):
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
         if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
             return inpt
-        from torchvision.io import decode_image, encode_jpeg
+        if inpt.ndim < 3:
+            raise ValueError(f"JPEGCompression expects [..., C, H, W] input, but got shape {inpt.shape}.")
 
-        img_uint8 = (inpt * 255).byte()
+        channels, height, width = inpt.shape[-3:]
-        if img_uint8.dim() == 3:
+        if channels not in (1, 3):
-            try:
+            raise ValueError(f"JPEGCompression expects 1 or 3 channels, but got {channels}.")
-                buf = encode_jpeg(img_uint8.cpu(), quality=params["quality"])
+
-                return decode_image(buf).to(device=inpt.device, dtype=inpt.dtype) / 255.0
+        flat_input = inpt.reshape(-1, channels, height, width)
-            except Exception:
+        flat_uint8 = (flat_input.clamp(0.0, 1.0) * 255).round().to(torch.uint8).cpu()
-                return inpt
+        decoded_frames = [
-        return inpt
+            decode_image(encode_jpeg(frame, quality=params["quality"])) for frame in flat_uint8.unbind()
+        ]
+        output = torch.stack(decoded_frames).to(device=inpt.device, dtype=inpt.dtype) / 255.0
+        return output.reshape(inpt.shape)
 
 
 class GaussianPatchBrightness(Transform):
@@ -271,10 +298,22 @@ class GaussianPatchBrightness(Transform):
         super().__init__()
         if isinstance(num_patches, int):
             self.num_patches = (num_patches, num_patches)
-        else:
+        elif isinstance(num_patches, Sequence) and len(num_patches) == 2:
             self.num_patches = (int(num_patches[0]), int(num_patches[1]))
-        self.sigma_range = sigma_range
+        else:
-        self.factor_range = factor_range
+            raise TypeError("num_patches must be an int or a sequence with length 2.")
+        if not 1 <= self.num_patches[0] <= self.num_patches[1]:
+            raise ValueError(f"num_patches must satisfy 1 <= min <= max, but got {self.num_patches}.")
+        if not isinstance(sigma_range, Sequence) or len(sigma_range) != 2:
+            raise TypeError("sigma_range must be a sequence with length 2.")
+        self.sigma_range = (float(sigma_range[0]), float(sigma_range[1]))
+        if not 0.0 < self.sigma_range[0] <= self.sigma_range[1]:
+            raise ValueError(f"sigma_range must satisfy 0 < min <= max, but got {self.sigma_range}.")
+        if not isinstance(factor_range, Sequence) or len(factor_range) != 2:
+            raise TypeError("factor_range must be a sequence with length 2.")
+        self.factor_range = (float(factor_range[0]), float(factor_range[1]))
+        if not 0.0 <= self.factor_range[0] <= self.factor_range[1]:
+            raise ValueError(f"factor_range must satisfy 0 <= min <= max, but got {self.factor_range}.")
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
         n = int(torch.randint(self.num_patches[0], self.num_patches[1] + 1, (1,)).item())
@@ -297,9 +336,8 @@ class GaussianPatchBrightness(Transform):
         ):
             gauss = torch.exp(-((yy - cy) ** 2 + (xx - cx) ** 2) / (2 * sigma**2))
             mask = mask * (1.0 + (factor - 1.0) * gauss)
-        if inpt.dim() == 3:
+        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
-            return (inpt * mask.unsqueeze(0)).clamp(0.0, 1.0)
+        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
-        return (inpt * mask.unsqueeze(0).unsqueeze(0)).clamp(0.0, 1.0)
 
 
 class RandomShadow(Transform):
@@ -316,33 +354,44 @@ class RandomShadow(Transform):
         super().__init__()
         if isinstance(opacity, (int, float)):
             self.opacity = (float(opacity), float(opacity))
-        else:
+        elif isinstance(opacity, Sequence) and len(opacity) == 2:
             self.opacity = (float(opacity[0]), float(opacity[1]))
+        else:
+            raise TypeError("opacity must be a number or a sequence with length 2.")
+        if not 0.0 <= self.opacity[0] <= self.opacity[1] <= 1.0:
+            raise ValueError(f"opacity must satisfy 0 <= min <= max <= 1, but got {self.opacity}.")
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        return {"opacity": torch.empty(1).uniform_(self.opacity[0], self.opacity[1]).item()}
+        return {
+            "opacity": torch.empty(1).uniform_(self.opacity[0], self.opacity[1]).item(),
+            "start": torch.rand(1).item(),
+            "width": torch.empty(1).uniform_(1 / 3, 2 / 3).item(),
+            "direction": -1.0 if torch.rand(1).item() < 0.5 else 1.0,
+        }
 
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
         if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
             return inpt
+        if inpt.ndim < 3:
+            raise ValueError(f"RandomShadow expects [..., C, H, W] input, but got shape {inpt.shape}.")
+
         h, w = inpt.shape[-2:]
-        x_start = int(torch.randint(0, w // 2, (1,)).item())
+        band_width = max(1, min(w, round(params["width"] * w)))
-        x_end = int(torch.randint(w // 3, w, (1,)).item())
+        x_start = round(params["start"] * (w - band_width))
+        x_end = x_start + band_width
         mask = torch.ones(h, w, device=inpt.device, dtype=inpt.dtype)
-        if torch.rand(1).item() < 0.5:
+        mask[:, x_start:x_end] = 1.0 + params["direction"] * params["opacity"]
-            mask[:, x_start:x_end] = 1.0 - params["opacity"]
+
-        else:
+        smoothing_size = min(8, h, w)
-            mask[:, x_start:x_end] = 1.0 + params["opacity"]
+        if smoothing_size > 1:
-        mask = mask.unsqueeze(0).unsqueeze(0)
+            batched_mask = mask[None, None]
-        small = torch.nn.functional.avg_pool2d(mask, 8, stride=8)
+            small = torch.nn.functional.avg_pool2d(batched_mask, smoothing_size, stride=smoothing_size)
-        mask = (
+            mask = torch.nn.functional.interpolate(small, size=(h, w), mode="bilinear", align_corners=False)[
-            torch.nn.functional.interpolate(small, size=(h, w), mode="bilinear", align_corners=False)
+                0, 0
-            .squeeze(0)
+            ]
-            .squeeze(0)
+
-        )
+        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
-        if inpt.dim() == 3:
+        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
-            return (inpt * mask.unsqueeze(0)).clamp(0.0, 1.0)
-        return (inpt * mask.unsqueeze(0).unsqueeze(0)).clamp(0.0, 1.0)
 
 
 class CoarseDropout(Transform):
@@ -366,6 +415,16 @@ class CoarseDropout(Transform):
         fill_value: float = 0.0,
     ) -> None:
         super().__init__()
+        if not isinstance(max_holes, int):
+            raise TypeError("max_holes must be an int.")
+        if max_holes < 1:
+            raise ValueError(f"max_holes must be at least 1, but got {max_holes}.")
+        if not 0.0 < max_height_frac <= 1.0:
+            raise ValueError(f"max_height_frac must be in (0, 1], but got {max_height_frac}.")
+        if not 0.0 < max_width_frac <= 1.0:
+            raise ValueError(f"max_width_frac must be in (0, 1], but got {max_width_frac}.")
+        if not 0.0 <= fill_value <= 1.0:
+            raise ValueError(f"fill_value must be in [0, 1], but got {fill_value}.")
         self.max_holes = max_holes
         self.max_height_frac = max_height_frac
         self.max_width_frac = max_width_frac
@@ -373,22 +432,27 @@ class CoarseDropout(Transform):
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
         n = int(torch.randint(1, self.max_holes + 1, (1,)).item())
-        return {"n_holes": n}
+        sizes = torch.rand(n, 2)
+        sizes[:, 0] *= self.max_height_frac
+        sizes[:, 1] *= self.max_width_frac
+        return {"sizes": sizes.tolist(), "positions": torch.rand(n, 2).tolist()}
 
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
         if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
             return inpt
+        if inpt.ndim < 3:
+            raise ValueError(f"CoarseDropout expects [..., C, H, W] input, but got shape {inpt.shape}.")
+
         h, w = inpt.shape[-2:]
         result = inpt.clone()
-        for _ in range(params["n_holes"]):
+        for (height_frac, width_frac), (y_frac, x_frac) in zip(
-            hole_h = int(torch.randint(1, max(2, int(h * self.max_height_frac)), (1,)).item())
+            params["sizes"], params["positions"], strict=True
-            hole_w = int(torch.randint(1, max(2, int(w * self.max_width_frac)), (1,)).item())
+        ):
-            y = int(torch.randint(0, h - hole_h + 1, (1,)).item())
+            hole_h = max(1, min(h, round(height_frac * h)))
-            x = int(torch.randint(0, w - hole_w + 1, (1,)).item())
+            hole_w = max(1, min(w, round(width_frac * w)))
-            if result.dim() == 3:
+            y = round(y_frac * (h - hole_h))
-                result[:, y : y + hole_h, x : x + hole_w] = self.fill_value
+            x = round(x_frac * (w - hole_w))
-            else:
+            result[..., y : y + hole_h, x : x + hole_w] = self.fill_value
-                result[:, :, y : y + hole_h, x : x + hole_w] = self.fill_value
         return result
 
 
@@ -406,9 +470,16 @@ class GammaCorrection(Transform):
     def __init__(self, gamma: float | Sequence[float] = (0.5, 2.0)) -> None:
         super().__init__()
         if isinstance(gamma, (int, float)):
-            self.gamma = (1.0 / float(gamma), float(gamma))
+            gamma = float(gamma)
-        else:
+            if gamma <= 0:
+                raise ValueError(f"gamma must be positive, but got {gamma}.")
+            self.gamma = (min(gamma, 1.0 / gamma), max(gamma, 1.0 / gamma))
+        elif isinstance(gamma, Sequence) and len(gamma) == 2:
             self.gamma = (float(gamma[0]), float(gamma[1]))
+        else:
+            raise TypeError("gamma must be a number or a sequence with length 2.")
+        if not 0.0 < self.gamma[0] <= self.gamma[1]:
+            raise ValueError(f"gamma must satisfy 0 < min <= max, but got {self.gamma}.")
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
         log_lo = math.log(self.gamma[0])
@@ -422,43 +493,112 @@ class GammaCorrection(Transform):
         return inpt
 
 
+# From the paper authors' MIT-licensed reference implementation:
+# https://github.com/TheZino/PlanckianJitter
+_PLANCKIAN_BLACKBODY_COEFFICIENTS = (
+    (0.6743, 0.4029, 0.0013),
+    (0.6281, 0.4241, 0.1665),
+    (0.5919, 0.4372, 0.2513),
+    (0.5623, 0.4457, 0.3154),
+    (0.5376, 0.4515, 0.3672),
+    (0.5163, 0.4555, 0.4103),
+    (0.4979, 0.4584, 0.4468),
+    (0.4816, 0.4604, 0.4782),
+    (0.4672, 0.4619, 0.5053),
+    (0.4542, 0.4630, 0.5289),
+    (0.4426, 0.4638, 0.5497),
+    (0.4320, 0.4644, 0.5681),
+    (0.4223, 0.4648, 0.5844),
+    (0.4135, 0.4651, 0.5990),
+    (0.4054, 0.4653, 0.6121),
+    (0.3980, 0.4654, 0.6239),
+    (0.3911, 0.4655, 0.6346),
+    (0.3847, 0.4656, 0.6444),
+    (0.3787, 0.4656, 0.6532),
+    (0.3732, 0.4656, 0.6613),
+    (0.3680, 0.4655, 0.6688),
+    (0.3632, 0.4655, 0.6756),
+    (0.3586, 0.4655, 0.6820),
+    (0.3544, 0.4654, 0.6878),
+    (0.3503, 0.4653, 0.6933),
+)
+_PLANCKIAN_MIN_TEMPERATURE = 3_000
+_PLANCKIAN_MAX_TEMPERATURE = 15_000
+_PLANCKIAN_TEMPERATURE_STEP = 500
+
+
 class PlanckianJitter(Transform):
     """Simulate color temperature shift along the Planckian locus.
 
-    Models the visual effect of different light sources (LED vs fluorescent vs
+    Samples one black-body temperature and applies the corresponding correlated red
-    daylight) by applying physically-motivated per-channel scaling. More accurate
+    and blue channel scaling while preserving the green channel. Coefficients between
-    than arbitrary hue shift for lighting variation.
+    the tabulated 500 K intervals are linearly interpolated.
 
     Reference: Zini et al., "Planckian Jitter", CVPR 2022 Workshop.
 
     Args:
-        strength: Range (min, max) for per-channel scale factor.
+        temperature: A fixed color temperature or range in Kelvin. Supported values
+            are between 3000 K and 15000 K.
     """
 
-    def __init__(self, strength: Sequence[float] = (0.85, 1.15)) -> None:
+    def __init__(self, temperature: int | Sequence[int] = (3_000, 15_000)) -> None:
         super().__init__()
-        self.strength = strength
+        if isinstance(temperature, int):
+            self.temperature = (temperature, temperature)
+        elif isinstance(temperature, Sequence) and len(temperature) == 2:
+            self.temperature = (int(temperature[0]), int(temperature[1]))
+        else:
+            raise TypeError("temperature must be an int or a sequence with length 2.")
+        if not (
+            _PLANCKIAN_MIN_TEMPERATURE
+            <= self.temperature[0]
+            <= self.temperature[1]
+            <= _PLANCKIAN_MAX_TEMPERATURE
+        ):
+            raise ValueError(
+                "temperature must satisfy "
+                f"{_PLANCKIAN_MIN_TEMPERATURE} <= min <= max <= {_PLANCKIAN_MAX_TEMPERATURE}, "
+                f"but got {self.temperature}."
+            )
 
     def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        return {"scale": torch.empty(3).uniform_(self.strength[0], self.strength[1]).tolist()}
+        temperature = int(torch.randint(self.temperature[0], self.temperature[1] + 1, ()).item())
+        return {"temperature": temperature}
 
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
         if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
             return inpt
-        scale = torch.tensor(params["scale"], device=inpt.device, dtype=inpt.dtype)
+        if inpt.ndim < 3 or inpt.shape[-3] != 3:
-        if inpt.dim() == 3:
+            raise ValueError(f"PlanckianJitter expects [..., 3, H, W] input, but got shape {inpt.shape}.")
-            return (inpt * scale.view(3, 1, 1)).clamp(0.0, 1.0)
+
-        return (inpt * scale.view(1, 3, 1, 1)).clamp(0.0, 1.0)
+        table_position = (params["temperature"] - _PLANCKIAN_MIN_TEMPERATURE) / _PLANCKIAN_TEMPERATURE_STEP
+        left_index = math.floor(table_position)
+        right_index = min(left_index + 1, len(_PLANCKIAN_BLACKBODY_COEFFICIENTS) - 1)
+        interpolation_weight = table_position - left_index
+
+        left = torch.tensor(
+            _PLANCKIAN_BLACKBODY_COEFFICIENTS[left_index],
+            device=inpt.device,
+            dtype=inpt.dtype,
+        )
+        right = torch.tensor(
+            _PLANCKIAN_BLACKBODY_COEFFICIENTS[right_index],
+            device=inpt.device,
+            dtype=inpt.dtype,
+        )
+        coefficients = torch.lerp(left, right, interpolation_weight)
+        scale = torch.stack(
+            (
+                coefficients[0] / coefficients[1],
+                coefficients.new_tensor(1.0),
+                coefficients[2] / coefficients[1],
+            )
+        )
+        broadcast_shape = (1,) * (inpt.ndim - 3) + (3, 1, 1)
+        return (inpt * scale.reshape(broadcast_shape)).clamp(0.0, 1.0)
 
 
-# Custom transform registry for make_transform_from_config
+_CUSTOM_TRANSFORMS: dict[str, type[Transform]] = {
-_CUSTOM_TRANSFORMS: dict[str, type] = {}
-
-
-def _register_custom_transforms() -> None:
-    """Register all custom transforms defined in this module."""
-    _CUSTOM_TRANSFORMS.update(
-        {
     "SharpnessJitter": SharpnessJitter,
     "GaussianNoise": GaussianNoise,
     "MotionBlur": MotionBlur,
@@ -468,11 +608,7 @@ def _register_custom_transforms() -> None:
     "CoarseDropout": CoarseDropout,
     "GammaCorrection": GammaCorrection,
     "PlanckianJitter": PlanckianJitter,
-        }
+}
-    )
-
-
-_register_custom_transforms()
 
 
 @dataclass

tests/datasets/test_image_transforms.py

@@ -475,7 +475,7 @@ ROBOTICS_TRANSFORMS = [
     ("RandomShadow", RandomShadow, {"opacity": (0.3, 0.6)}),
     ("CoarseDropout", CoarseDropout, {"max_holes": 8}),
     ("GammaCorrection", GammaCorrection, {"gamma": (0.5, 2.0)}),
-    ("PlanckianJitter", PlanckianJitter, {"strength": (0.85, 1.15)}),
+    ("PlanckianJitter", PlanckianJitter, {"temperature": (3_000, 15_000)}),
 ]
 
 
@@ -523,3 +523,93 @@ def test_make_transform_error_message_includes_custom():
     """Error message should list all registered custom transforms."""
     with pytest.raises(ValueError, match="GaussianNoise"):
         make_transform_from_config(ImageTransformConfig(type="NonExistent"))
+
+
+@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
+@pytest.mark.parametrize("shape", [(4, 3, 32, 32), (2, 4, 3, 16, 16)])
+def test_robotics_transform_supports_temporal_batches(name, cls, kwargs, shape):
+    img = torch.rand(shape)
+    out = cls(**kwargs)(img)
+    assert out.shape == img.shape, f"{name} changed shape: {img.shape} -> {out.shape}"
+    assert out.min() >= 0
+    assert out.max() <= 1
+
+
+@pytest.mark.parametrize(
+    "cls,kwargs",
+    [
+        (GaussianNoise, {"std": (25.0, 25.0)}),
+        (MotionBlur, {"kernel_size": 5}),
+        (JPEGCompression, {"quality": 10}),
+        (
+            GaussianPatchBrightness,
+            {"num_patches": 1, "sigma_range": (0.2, 0.2), "factor_range": (0.5, 0.5)},
+        ),
+        (RandomShadow, {"opacity": 0.5}),
+        (CoarseDropout, {"max_holes": 1, "fill_value": 0.0}),
+        (GammaCorrection, {"gamma": (2.0, 2.0)}),
+        (PlanckianJitter, {"temperature": 3_000}),
+    ],
+)
+def test_robotics_transform_is_not_silent_noop(cls, kwargs):
+    img = torch.rand(3, 32, 32)
+    out = cls(**kwargs)(img)
+    assert not torch.equal(out, img)
+
+
+@pytest.mark.parametrize(
+    "transform",
+    [
+        GaussianNoise(std=25),
+        RandomShadow(opacity=0.5),
+        CoarseDropout(max_holes=4),
+    ],
+)
+def test_robotics_transform_random_params_are_reused(transform):
+    img = torch.rand(3, 32, 32)
+    params = transform.make_params([img])
+    torch.testing.assert_close(transform.transform(img, params), transform.transform(img, params))
+
+
+def test_motion_blur_kernel_size_stays_in_configured_range():
+    transform = MotionBlur(kernel_size=(4, 10))
+    sampled_sizes = {transform.make_params([])["kernel_size"] for _ in range(100)}
+    assert sampled_sizes <= {5, 7, 9}
+    assert sampled_sizes
+
+
+def test_gamma_correction_scalar_below_one_defines_symmetric_range():
+    transform = GammaCorrection(gamma=0.5)
+    assert transform.gamma == (0.5, 2.0)
+    assert transform(torch.rand(3, 8, 8)).shape == (3, 8, 8)
+
+
+def test_planckian_jitter_uses_correlated_temperature_coefficients():
+    img = torch.full((2, 3, 8, 8), 0.25)
+    out = PlanckianJitter(temperature=3_000)(img)
+    torch.testing.assert_close(out[:, 1], img[:, 1])
+    assert torch.all(out[:, 0] > out[:, 1])
+    assert torch.all(out[:, 2] < out[:, 1])
+
+
+def test_random_shadow_supports_small_images():
+    img = torch.rand(3, 7, 7)
+    assert RandomShadow()(img).shape == img.shape
+
+
+@pytest.mark.parametrize(
+    "cls,kwargs",
+    [
+        (GaussianNoise, {"std": (-1.0, 1.0)}),
+        (MotionBlur, {"kernel_size": 4}),
+        (JPEGCompression, {"quality": (0, 75)}),
+        (GaussianPatchBrightness, {"sigma_range": (0.0, 0.25)}),
+        (RandomShadow, {"opacity": (0.3, 1.1)}),
+        (CoarseDropout, {"max_holes": 0}),
+        (GammaCorrection, {"gamma": 0.0}),
+        (PlanckianJitter, {"temperature": (2_000, 6_500)}),
+    ],
+)
+def test_robotics_transform_rejects_invalid_config(cls, kwargs):
+    with pytest.raises(ValueError):
+        cls(**kwargs)