feat(flow-matching): cover per-policy sampling divergences

Extend the shared samplers to represent the non-openpi conventions Martino
catalogued, without changing existing openpi behavior:

- sample_noise: add distribution="uniform" for evo1's rand*2-1 noise.
- sample_time_beta: add complement flag (groot/wall_x forward t=(1-beta)*s)
  and optional clamp_min/clamp_max (evo1 Beta(2,2) clamped to [0.02, 0.98]);
  scale/offset now default to the identity mapping.
- sample_beta: build the Beta distribution via a cached, CPU-side helper
  (groot convention) so it is constructed once per (alpha, beta).

Adds tests for uniform noise, complement/clamp timesteps, and caching.

Co-authored-by: Cursor <cursoragent@cursor.com>

src/lerobot/policies/common/flow_matching.py

@@ -23,6 +23,7 @@ stateless; adopting them does not affect checkpoints.
 """
 
 from collections.abc import Callable
+from functools import lru_cache
 from typing import TYPE_CHECKING
 
 import torch
@@ -32,29 +33,67 @@ if TYPE_CHECKING:
     from lerobot.policies.rtc.modeling_rtc import RTCProcessor
 
 
-def sample_beta(alpha: float, beta: float, bsize: int, device) -> Tensor:  # see openpi (exact copy)
+@lru_cache(maxsize=None)
-    # Beta sampling uses _sample_dirichlet which isn't implemented for MPS, so sample on CPU
+def _beta_distribution(alpha: float, beta: float) -> "torch.distributions.Beta":
+    # Beta sampling uses _sample_dirichlet which isn't implemented for MPS, so build on CPU.
+    # Cached (groot convention) so the distribution object is constructed once per (alpha, beta).
     alpha_t = torch.tensor(alpha, dtype=torch.float32)
     beta_t = torch.tensor(beta, dtype=torch.float32)
-    dist = torch.distributions.Beta(alpha_t, beta_t)
+    return torch.distributions.Beta(alpha_t, beta_t)
-    return dist.sample((bsize,)).to(device)
 
 
-def sample_noise(shape, device) -> Tensor:
+def sample_beta(alpha: float, beta: float, bsize: int, device) -> Tensor:  # see openpi (exact copy)
-    """Standard-normal float32 noise, the flow-matching x_1 sample."""
+    return _beta_distribution(alpha, beta).sample((bsize,)).to(device)
-    return torch.normal(
-        mean=0.0,
-        std=1.0,
-        size=shape,
-        dtype=torch.float32,
-        device=device,
-    )
 
 
-def sample_time_beta(bsize: int, device, *, alpha: float, beta: float, scale: float, offset: float) -> Tensor:
+def sample_noise(shape, device, *, distribution: str = "normal") -> Tensor:
-    """Beta-distributed flow-matching timesteps: ``Beta(alpha, beta) * scale + offset`` (openpi convention)."""
+    """Float32 flow-matching noise sample.
+
+    ``distribution="normal"`` (default, openpi: pi0/pi05/eo1/smolvla/groot/wall_x) draws
+    standard-normal noise. ``distribution="uniform"`` (evo1) draws uniformly from
+    ``[-1, 1)`` via ``rand * 2 - 1``.
+    """
+    if distribution == "normal":
+        return torch.normal(
+            mean=0.0,
+            std=1.0,
+            size=shape,
+            dtype=torch.float32,
+            device=device,
+        )
+    if distribution == "uniform":
+        return torch.rand(shape, dtype=torch.float32, device=device) * 2 - 1
+    raise ValueError(f"Unknown noise distribution: {distribution!r} (expected 'normal' or 'uniform')")
+
+
+def sample_time_beta(
+    bsize: int,
+    device,
+    *,
+    alpha: float,
+    beta: float,
+    scale: float = 1.0,
+    offset: float = 0.0,
+    complement: bool = False,
+    clamp_min: float | None = None,
+    clamp_max: float | None = None,
+) -> Tensor:
+    """Beta-distributed flow-matching timesteps.
+
+    Computes ``t = f(Beta(alpha, beta)) * scale + offset`` where ``f`` is the identity by
+    default or ``1 - x`` when ``complement=True``, then optionally clamps to
+    ``[clamp_min, clamp_max]``. This covers the known per-policy conventions:
+
+    * openpi backward (pi0/pi05/eo1/smolvla): ``scale=0.999, offset=0.001``.
+    * forward (groot/wall_x): ``complement=True, scale=0.999`` giving ``(1 - beta) * 0.999``.
+    * evo1: ``alpha=beta=2, clamp_min=0.02, clamp_max=0.98``.
+    """
     time_beta = sample_beta(alpha, beta, bsize, device)
+    if complement:
+        time_beta = 1.0 - time_beta
     time = time_beta * scale + offset
+    if clamp_min is not None or clamp_max is not None:
+        time = time.clamp(min=clamp_min, max=clamp_max)
     return time.to(dtype=torch.float32, device=device)
 
 

tests/policies/common/test_flow_matching.py

@@ -24,6 +24,7 @@ reference is a behavior change for released checkpoints.
 import torch
 
 from lerobot.policies.common.flow_matching import (
+    _beta_distribution,
     euler_integrate,
     sample_beta,
     sample_noise,
@@ -63,6 +64,57 @@ def test_sample_noise_seeded():
     assert n1.dtype == torch.float32 and n1.shape == (2, 8, 4)
 
 
+def test_sample_noise_normal_is_default():
+    torch.manual_seed(2)
+    default = sample_noise((2, 8, 4), "cpu")
+    torch.manual_seed(2)
+    normal = sample_noise((2, 8, 4), "cpu", distribution="normal")
+    assert torch.equal(default, normal)
+
+
+def test_sample_noise_uniform_evo1():
+    torch.manual_seed(2)
+    n = sample_noise((4096,), "cpu", distribution="uniform")
+    assert n.dtype == torch.float32
+    assert n.min() >= -1.0 and n.max() < 1.0
+    # evo1's rand_like * 2 - 1 has mean ~0 over [-1, 1).
+    assert abs(n.mean().item()) < 0.05
+    # Exact match to the historical evo1 expression on the same RNG stream.
+    torch.manual_seed(2)
+    expected = torch.rand((4096,), dtype=torch.float32) * 2 - 1
+    torch.testing.assert_close(n, expected, rtol=0, atol=0)
+
+
+def test_sample_noise_invalid_distribution():
+    import pytest
+
+    with pytest.raises(ValueError, match="Unknown noise distribution"):
+        sample_noise((2, 2), "cpu", distribution="bogus")
+
+
+def test_sample_time_beta_forward_complement_convention():
+    # groot/wall_x forward convention: t = (1 - beta) * 0.999.
+    torch.manual_seed(9)
+    time = sample_time_beta(4096, "cpu", alpha=1.5, beta=1.0, scale=0.999, complement=True)
+    torch.manual_seed(9)
+    expected = (1.0 - sample_beta(1.5, 1.0, 4096, "cpu")) * 0.999
+    torch.testing.assert_close(time, expected, rtol=0, atol=0)
+
+
+def test_sample_time_beta_evo1_clamp():
+    # evo1: Beta(2, 2) clamped to [0.02, 0.98].
+    torch.manual_seed(10)
+    time = sample_time_beta(4096, "cpu", alpha=2.0, beta=2.0, clamp_min=0.02, clamp_max=0.98)
+    assert time.min() >= 0.02 and time.max() <= 0.98
+
+
+def test_sample_beta_distribution_is_cached():
+    a = _beta_distribution(1.5, 1.0)
+    b = _beta_distribution(1.5, 1.0)
+    assert a is b
+    assert _beta_distribution(2.0, 2.0) is not a
+
+
 def test_euler_integrate_constant_velocity_is_exact():
     # With v_t == c constant, x_0 = x_1 + sum(dt * c) = x_1 - c exactly (num_steps * dt = -1).
     noise = torch.randn(3, 5, 2)