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Browse files- experiments/docs/EXPERIMENT_PROTOCOL.md +3 -1
- experiments/evaluate_image_planning.py +90 -84
- experiments/planet/README.md +3 -3
- experiments/planet/result/paper_training.json +43 -0
- experiments/planet/result/paper_training_trace.jsonl +100 -0
- experiments/planet/result/parameter_count.json +8 -0
- experiments/reports/paper_flowmo_latent_probes.json +396 -0
- experiments/reports/paper_planning/counterflow_triangle_uniform.json +0 -0
- experiments/reports/paper_planning/passive_to_active_triangle_uniform.json +0 -0
- experiments/reports/paper_planning/passive_to_active_twin_uniform.json +0 -0
- experiments/reports/paper_planning/reach_uniform_triangle_uniform.json +0 -0
- experiments/reports/paper_planning/station_keeping_triangle_uniform.json +0 -0
- experiments/reports/paper_planning/station_keeping_twin_uniform.json +0 -0
- experiments/reports/paper_planning/waypoint_square_triangle_gradient.json +0 -0
- experiments/reports/paper_planning/waypoint_square_twin_gradient.json +0 -0
- experiments/reports/paper_planning/waypoint_zigzag_triangle_gradient.json +0 -0
- experiments/reports/paper_planning/waypoint_zigzag_twin_gradient.json +0 -0
- experiments/reports/paper_prediction_seen_flow_diagnostic.json +1730 -0
- experiments/reports/paper_prediction_unseen_boat_params.json +1730 -0
- experiments/reports/paper_prediction_unseen_flow.json +1490 -0
- experiments/reports/paper_report.md +253 -0
- experiments/reports/paper_training_summary.json +174 -0
- experiments/run_paper_image_pipeline.py +31 -17
- experiments/tables/README.md +3 -0
- experiments/tdmpc2/README.md +3 -3
- tests/test_experiment_framework.py +0 -3
experiments/docs/EXPERIMENT_PROTOCOL.md
CHANGED
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@@ -125,6 +125,8 @@ planet
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tdmpc2
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```
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Traditional non-WM controllers:
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```text
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@@ -204,7 +206,7 @@ python -m experiments.run_paper_image_pipeline --stages planning
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python -m experiments.run_paper_image_pipeline --stages report
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```
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Run
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```bash
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python -m pytest -q
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tdmpc2
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```
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+
All learned world models use the same route-aware CEM planner over their latent rollouts.
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Traditional non-WM controllers:
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```text
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python -m experiments.run_paper_image_pipeline --stages report
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```
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Run tests:
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```bash
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python -m pytest -q
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experiments/evaluate_image_planning.py
CHANGED
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@@ -136,11 +136,53 @@ def sample_action_sequences(mean: torch.Tensor, std: torch.Tensor, population: i
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return samples
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def learned_plan(
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model,
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image_history: deque,
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action_history: deque,
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-
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active_action_dim: int,
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args,
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prev_action: np.ndarray,
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c = donor_context.to(device=device, dtype=torch.float32)
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z = z.detach()
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c = c.detach()
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goal_t = torch.as_tensor(goal, dtype=torch.float32, device=device).view(1, 2)
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with torch.no_grad(), autocast_context(device, args.precision):
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current_pos = decode_absolute(model.decoder(z)).float().detach()[..., :2]
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mean = warm_start_mean(
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previous_mean,
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args.cem_horizon,
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prev = torch.zeros((model.config.action_dim,), dtype=torch.float32, device=device)
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prev[:active_action_dim] = torch.as_tensor(prev_action, dtype=torch.float32, device=device)
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best_candidates = None
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-
if args.planner == "gradient":
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-
action, best_candidates, mean = gradient_plan(
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model,
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z,
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c,
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mean,
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goal_t,
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current_pos,
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prev,
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active_action_dim,
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args,
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)
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return action, best_candidates, mean
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with torch.no_grad():
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action, best_candidates, mean = cem_plan(
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model,
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@@ -194,6 +225,7 @@ def learned_plan(
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mean,
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std,
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goal_t,
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current_pos,
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prev,
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active_action_dim,
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pred: torch.Tensor,
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samples: torch.Tensor,
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goal_t: torch.Tensor,
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current_pos: torch.Tensor,
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prev: torch.Tensor,
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active_action_dim: int,
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pos = pred[..., :2]
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goal_delta = goal_t - current_pos
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goal_dir = goal_delta / torch.linalg.norm(goal_delta, dim=-1, keepdim=True).clamp_min(1.0e-6)
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-
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alpha = torch.linspace(1.0 / pos.shape[1], 1.0, pos.shape[1], device=pos.device, dtype=pos.dtype)
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-
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-
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goal_from_pos = goal_t[:, None] - pos
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goal_from_pos = goal_from_pos / torch.linalg.norm(goal_from_pos, dim=-1, keepdim=True).clamp_min(1.0e-6)
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heading = pred[..., 2:4]
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heading_error = (1.0 - (heading * goal_from_pos).sum(dim=-1)).mean(dim=-1)
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terminal = ((pos[:, -1] - goal_t) ** 2).sum(dim=-1)
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path = ((pos - goal_t[:, None]) ** 2).sum(dim=-1).mean(dim=-1)
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energy = (samples[..., :active_action_dim] ** 2).mean(dim=(1, 2))
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smooth_prev = torch.cat([prev.view(1, 1, -1).repeat(samples.shape[0], 1, 1), samples[:, :-1]], dim=1)
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smooth = ((samples - smooth_prev) ** 2).mean(dim=(1, 2))
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boundary = (
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torch.relu(-pos[..., 0])
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+ torch.relu(pos[..., 0] - 10.0)
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-
+ torch.relu(-pos[..., 1])
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-
+ torch.relu(pos[..., 1] - 10.0)
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).mean(dim=-1)
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return (
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args.cem_w_goal * terminal
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+ args.cem_w_path * path
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-
+ args.cem_w_route * route_error
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+ args.cem_w_heading_goal * heading_error
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+ args.cem_w_action * energy
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+ args.cem_w_smooth * smooth
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+ args.cem_w_boundary * boundary
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-
- args.cem_w_progress *
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)
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@@ -253,6 +299,7 @@ def cem_plan(
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mean: torch.Tensor,
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std: torch.Tensor,
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goal_t: torch.Tensor,
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current_pos: torch.Tensor,
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prev: torch.Tensor,
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active_action_dim: int,
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@@ -267,7 +314,7 @@ def cem_plan(
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samples[:, :, active_action_dim:] = 0.0
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with autocast_context(mean.device, args.precision):
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pred = rollout_latent(model, z, c, samples)
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-
cost = planning_cost(pred, samples, goal_t, current_pos, prev, active_action_dim, args)
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elite_idx = torch.topk(cost, k=args.cem_elites, largest=False).indices
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elites = samples[elite_idx]
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mean = elites.mean(dim=0)
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@@ -283,49 +330,6 @@ def cem_plan(
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)
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-
def gradient_plan(
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model,
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z: torch.Tensor,
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-
c: torch.Tensor,
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-
mean: torch.Tensor,
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-
goal_t: torch.Tensor,
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current_pos: torch.Tensor,
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-
prev: torch.Tensor,
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active_action_dim: int,
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args,
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-
) -> tuple[np.ndarray, np.ndarray | None, np.ndarray]:
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-
init = mean.clamp(-0.95, 0.95)
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-
logits = torch.atanh(init).detach().requires_grad_(True)
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-
optimizer = torch.optim.Adam([logits], lr=args.planner_lr)
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-
inactive = None
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if active_action_dim < mean.shape[-1]:
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-
inactive = torch.zeros_like(mean)
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-
inactive[:, :active_action_dim] = 1.0
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-
for _ in range(args.planner_iterations):
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-
seq = torch.tanh(logits)
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-
if inactive is not None:
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-
seq = seq * inactive
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-
with autocast_context(mean.device, args.precision):
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-
pred = rollout_latent(model, z, c, seq.unsqueeze(0))
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-
loss = planning_cost(pred, seq.unsqueeze(0), goal_t, current_pos, prev, active_action_dim, args).mean()
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-
optimizer.zero_grad(set_to_none=True)
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-
loss.backward()
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-
optimizer.step()
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-
with torch.no_grad():
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-
seq = torch.tanh(logits)
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-
if inactive is not None:
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-
seq = seq * inactive
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-
with autocast_context(mean.device, args.precision):
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-
pred = rollout_latent(model, z, c, seq.unsqueeze(0))
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-
candidates = pred[0, :, :2].detach().cpu().numpy()[None, ...] if args.make_gifs else None
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-
action = seq[0, :active_action_dim].detach().cpu().numpy()
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-
return (
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np.clip(action, -1.0, 1.0).astype(np.float32),
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candidates,
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seq.detach().cpu().numpy(),
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)
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-
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-
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@torch.no_grad()
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def donor_context_for_flowmo(model, env: SurfaceBoatEnv, args, seed: int) -> torch.Tensor | None:
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if not hasattr(model, "to_c"):
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@@ -430,7 +434,8 @@ def evaluate_one_method(method: str, args) -> dict:
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model,
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image_history,
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action_history,
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-
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env.action_dim,
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args,
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prev_action,
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@@ -531,29 +536,30 @@ def main() -> None:
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parser.add_argument("--boat", choices=["twin", "triangle"], default="twin")
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parser.add_argument("--flow-type", choices=["uniform", "slowly_varying", "vortex_center", "gradient", "turbulent_patch"], default="uniform")
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parser.add_argument("--episodes", type=int, default=50)
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-
parser.add_argument("--max-steps", type=int, default=
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-
parser.add_argument("--planner", choices=["gradient", "cem"], default="gradient")
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parser.add_argument("--passive-steps", type=int, default=25)
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parser.add_argument("--history-len", type=int, default=32)
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parser.add_argument("--image-size", type=int, default=160)
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parser.add_argument("--visual-scale", type=float, default=2.5)
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-
parser.add_argument("--checkpoint-name", default="
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parser.add_argument("--context-modes", nargs="+", default=["inferred", "zero", "shuffled"])
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-
parser.add_argument("--cem-horizon", type=int, default=
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-
parser.add_argument("--cem-population", type=int, default=
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-
parser.add_argument("--cem-elites", type=int, default=
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-
parser.add_argument("--cem-iterations", type=int, default=
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parser.add_argument("--cem-action-std", type=float, default=0.5)
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-
parser.add_argument("--cem-knots", type=int, default=
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-
parser.add_argument("--
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-
parser.add_argument("--
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parser.add_argument("--cem-w-
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-
parser.add_argument("--cem-w-
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-
parser.add_argument("--cem-w-
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parser.add_argument("--cem-w-heading-goal", type=float, default=0.0)
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-
parser.add_argument("--cem-w-action", type=float, default=0.
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parser.add_argument("--cem-w-smooth", type=float, default=0.08)
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-
parser.add_argument("--cem-w-boundary", type=float, default=
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parser.add_argument("--cem-w-progress", type=float, default=2.0)
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parser.add_argument("--success-radius", type=float, default=0.65)
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parser.add_argument("--make-gifs", type=int, default=3)
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@@ -562,7 +568,7 @@ def main() -> None:
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parser.add_argument("--seed", type=int, default=33)
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parser.add_argument("--device", default="cuda")
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parser.add_argument("--precision", choices=["fp32", "bf16", "fp16"], default="fp32")
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-
parser.add_argument("--out", default="experiments/reports/
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| 566 |
args = parser.parse_args()
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out_dir = Path(args.out)
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return samples
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| 139 |
+
def route_points_tensor(
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| 140 |
+
current_pos: torch.Tensor,
|
| 141 |
+
goals: np.ndarray,
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| 142 |
+
goal_idx: int,
|
| 143 |
+
) -> torch.Tensor:
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| 144 |
+
remaining = torch.as_tensor(goals[goal_idx:], dtype=torch.float32, device=current_pos.device)
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| 145 |
+
return torch.cat([current_pos.reshape(1, 2).detach(), remaining], dim=0)
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| 146 |
+
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+
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| 148 |
+
def route_projection(pos: torch.Tensor, route_points: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
| 149 |
+
starts = route_points[:-1]
|
| 150 |
+
ends = route_points[1:]
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| 151 |
+
seg = ends - starts
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| 152 |
+
seg_len = torch.linalg.norm(seg, dim=-1).clamp_min(1.0e-6)
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| 153 |
+
seg_len_sq = (seg_len * seg_len).clamp_min(1.0e-6)
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| 154 |
+
rel = pos[:, :, None, :] - starts.view(1, 1, -1, 2)
|
| 155 |
+
t = (rel * seg.view(1, 1, -1, 2)).sum(dim=-1) / seg_len_sq.view(1, 1, -1)
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| 156 |
+
t = t.clamp(0.0, 1.0)
|
| 157 |
+
proj = starts.view(1, 1, -1, 2) + t[..., None] * seg.view(1, 1, -1, 2)
|
| 158 |
+
dist_sq = ((pos[:, :, None, :] - proj) ** 2).sum(dim=-1)
|
| 159 |
+
min_dist_sq, idx = dist_sq.min(dim=-1)
|
| 160 |
+
cum = torch.cat([torch.zeros(1, device=pos.device), seg_len.cumsum(dim=0)[:-1]], dim=0)
|
| 161 |
+
along = cum.view(1, 1, -1) + t * seg_len.view(1, 1, -1)
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| 162 |
+
route_s = along.gather(dim=-1, index=idx[..., None]).squeeze(-1)
|
| 163 |
+
return min_dist_sq, route_s, seg_len.sum()
|
| 164 |
+
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+
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| 166 |
+
def route_points_at_s(route_points: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
|
| 167 |
+
starts = route_points[:-1]
|
| 168 |
+
ends = route_points[1:]
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| 169 |
+
seg = ends - starts
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| 170 |
+
seg_len = torch.linalg.norm(seg, dim=-1).clamp_min(1.0e-6)
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| 171 |
+
cum_end = seg_len.cumsum(dim=0)
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| 172 |
+
cum_start = cum_end - seg_len
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+
flat_s = s.reshape(-1).clamp(0.0, float(cum_end[-1].detach().cpu()))
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| 174 |
+
idx = torch.searchsorted(cum_end, flat_s, right=False).clamp(max=seg_len.numel() - 1)
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| 175 |
+
local = ((flat_s - cum_start[idx]) / seg_len[idx]).clamp(0.0, 1.0)
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| 176 |
+
pts = starts[idx] + local[:, None] * seg[idx]
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| 177 |
+
return pts.reshape(*s.shape, 2)
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| 178 |
+
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| 179 |
+
|
| 180 |
def learned_plan(
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| 181 |
model,
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| 182 |
image_history: deque,
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| 183 |
action_history: deque,
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| 184 |
+
goals: np.ndarray,
|
| 185 |
+
goal_idx: int,
|
| 186 |
active_action_dim: int,
|
| 187 |
args,
|
| 188 |
prev_action: np.ndarray,
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|
| 201 |
c = donor_context.to(device=device, dtype=torch.float32)
|
| 202 |
z = z.detach()
|
| 203 |
c = c.detach()
|
| 204 |
+
goal = goals[goal_idx]
|
| 205 |
goal_t = torch.as_tensor(goal, dtype=torch.float32, device=device).view(1, 2)
|
| 206 |
with torch.no_grad(), autocast_context(device, args.precision):
|
| 207 |
current_pos = decode_absolute(model.decoder(z)).float().detach()[..., :2]
|
| 208 |
+
route_points = route_points_tensor(current_pos[0], goals, goal_idx)
|
| 209 |
mean = warm_start_mean(
|
| 210 |
previous_mean,
|
| 211 |
args.cem_horizon,
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|
| 217 |
prev = torch.zeros((model.config.action_dim,), dtype=torch.float32, device=device)
|
| 218 |
prev[:active_action_dim] = torch.as_tensor(prev_action, dtype=torch.float32, device=device)
|
| 219 |
best_candidates = None
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 220 |
with torch.no_grad():
|
| 221 |
action, best_candidates, mean = cem_plan(
|
| 222 |
model,
|
|
|
|
| 225 |
mean,
|
| 226 |
std,
|
| 227 |
goal_t,
|
| 228 |
+
route_points,
|
| 229 |
current_pos,
|
| 230 |
prev,
|
| 231 |
active_action_dim,
|
|
|
|
| 238 |
pred: torch.Tensor,
|
| 239 |
samples: torch.Tensor,
|
| 240 |
goal_t: torch.Tensor,
|
| 241 |
+
route_points: torch.Tensor,
|
| 242 |
current_pos: torch.Tensor,
|
| 243 |
prev: torch.Tensor,
|
| 244 |
active_action_dim: int,
|
|
|
|
| 247 |
pos = pred[..., :2]
|
| 248 |
goal_delta = goal_t - current_pos
|
| 249 |
goal_dir = goal_delta / torch.linalg.norm(goal_delta, dim=-1, keepdim=True).clamp_min(1.0e-6)
|
| 250 |
+
direct_progress = ((pos - current_pos[:, None]) * goal_dir[:, None]).sum(dim=-1).amax(dim=-1)
|
| 251 |
alpha = torch.linspace(1.0 / pos.shape[1], 1.0, pos.shape[1], device=pos.device, dtype=pos.dtype)
|
| 252 |
+
direct_route = current_pos[:, None] + alpha.view(1, -1, 1) * goal_delta[:, None]
|
| 253 |
+
direct_route_error = ((pos - direct_route) ** 2).sum(dim=-1).mean(dim=-1)
|
| 254 |
+
route_dist_sq, route_s, route_len = route_projection(pos, route_points)
|
| 255 |
+
route_error = route_dist_sq.mean(dim=-1)
|
| 256 |
+
scheduled_s = alpha * torch.minimum(
|
| 257 |
+
route_len,
|
| 258 |
+
torch.as_tensor(args.cem_route_horizon_distance, dtype=pos.dtype, device=pos.device),
|
| 259 |
+
)
|
| 260 |
+
scheduled = route_points_at_s(route_points, scheduled_s).view(1, pos.shape[1], 2)
|
| 261 |
+
lookahead_error = ((pos - scheduled) ** 2).sum(dim=-1).mean(dim=-1)
|
| 262 |
+
route_progress = (route_s.amax(dim=-1) / route_len.clamp_min(1.0e-6)).clamp(0.0, 1.0)
|
| 263 |
goal_from_pos = goal_t[:, None] - pos
|
| 264 |
goal_from_pos = goal_from_pos / torch.linalg.norm(goal_from_pos, dim=-1, keepdim=True).clamp_min(1.0e-6)
|
| 265 |
heading = pred[..., 2:4]
|
|
|
|
| 267 |
heading_error = (1.0 - (heading * goal_from_pos).sum(dim=-1)).mean(dim=-1)
|
| 268 |
terminal = ((pos[:, -1] - goal_t) ** 2).sum(dim=-1)
|
| 269 |
path = ((pos - goal_t[:, None]) ** 2).sum(dim=-1).mean(dim=-1)
|
| 270 |
+
via = ((pos - goal_t[:, None]) ** 2).sum(dim=-1).amin(dim=-1)
|
| 271 |
energy = (samples[..., :active_action_dim] ** 2).mean(dim=(1, 2))
|
| 272 |
smooth_prev = torch.cat([prev.view(1, 1, -1).repeat(samples.shape[0], 1, 1), samples[:, :-1]], dim=1)
|
| 273 |
smooth = ((samples - smooth_prev) ** 2).mean(dim=(1, 2))
|
| 274 |
+
margin = args.cem_boundary_margin
|
| 275 |
boundary = (
|
| 276 |
+
torch.relu(margin - pos[..., 0])
|
| 277 |
+
+ torch.relu(pos[..., 0] - (10.0 - margin))
|
| 278 |
+
+ torch.relu(margin - pos[..., 1])
|
| 279 |
+
+ torch.relu(pos[..., 1] - (10.0 - margin))
|
| 280 |
).mean(dim=-1)
|
| 281 |
return (
|
| 282 |
args.cem_w_goal * terminal
|
| 283 |
+ args.cem_w_path * path
|
| 284 |
+
+ args.cem_w_route * (route_error + 0.25 * direct_route_error)
|
| 285 |
+
+ args.cem_w_lookahead * lookahead_error
|
| 286 |
+
+ args.cem_w_via * via
|
| 287 |
+ args.cem_w_heading_goal * heading_error
|
| 288 |
+ args.cem_w_action * energy
|
| 289 |
+ args.cem_w_smooth * smooth
|
| 290 |
+ args.cem_w_boundary * boundary
|
| 291 |
+
- args.cem_w_progress * (route_progress + 0.1 * direct_progress)
|
| 292 |
)
|
| 293 |
|
| 294 |
|
|
|
|
| 299 |
mean: torch.Tensor,
|
| 300 |
std: torch.Tensor,
|
| 301 |
goal_t: torch.Tensor,
|
| 302 |
+
route_points: torch.Tensor,
|
| 303 |
current_pos: torch.Tensor,
|
| 304 |
prev: torch.Tensor,
|
| 305 |
active_action_dim: int,
|
|
|
|
| 314 |
samples[:, :, active_action_dim:] = 0.0
|
| 315 |
with autocast_context(mean.device, args.precision):
|
| 316 |
pred = rollout_latent(model, z, c, samples)
|
| 317 |
+
cost = planning_cost(pred, samples, goal_t, route_points, current_pos, prev, active_action_dim, args)
|
| 318 |
elite_idx = torch.topk(cost, k=args.cem_elites, largest=False).indices
|
| 319 |
elites = samples[elite_idx]
|
| 320 |
mean = elites.mean(dim=0)
|
|
|
|
| 330 |
)
|
| 331 |
|
| 332 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 333 |
@torch.no_grad()
|
| 334 |
def donor_context_for_flowmo(model, env: SurfaceBoatEnv, args, seed: int) -> torch.Tensor | None:
|
| 335 |
if not hasattr(model, "to_c"):
|
|
|
|
| 434 |
model,
|
| 435 |
image_history,
|
| 436 |
action_history,
|
| 437 |
+
goals,
|
| 438 |
+
goal_idx,
|
| 439 |
env.action_dim,
|
| 440 |
args,
|
| 441 |
prev_action,
|
|
|
|
| 536 |
parser.add_argument("--boat", choices=["twin", "triangle"], default="twin")
|
| 537 |
parser.add_argument("--flow-type", choices=["uniform", "slowly_varying", "vortex_center", "gradient", "turbulent_patch"], default="uniform")
|
| 538 |
parser.add_argument("--episodes", type=int, default=50)
|
| 539 |
+
parser.add_argument("--max-steps", type=int, default=420)
|
|
|
|
| 540 |
parser.add_argument("--passive-steps", type=int, default=25)
|
| 541 |
parser.add_argument("--history-len", type=int, default=32)
|
| 542 |
parser.add_argument("--image-size", type=int, default=160)
|
| 543 |
parser.add_argument("--visual-scale", type=float, default=2.5)
|
| 544 |
+
parser.add_argument("--checkpoint-name", default="paper.pt")
|
| 545 |
parser.add_argument("--context-modes", nargs="+", default=["inferred", "zero", "shuffled"])
|
| 546 |
+
parser.add_argument("--cem-horizon", type=int, default=45)
|
| 547 |
+
parser.add_argument("--cem-population", type=int, default=512)
|
| 548 |
+
parser.add_argument("--cem-elites", type=int, default=64)
|
| 549 |
+
parser.add_argument("--cem-iterations", type=int, default=4)
|
| 550 |
parser.add_argument("--cem-action-std", type=float, default=0.5)
|
| 551 |
+
parser.add_argument("--cem-knots", type=int, default=10)
|
| 552 |
+
parser.add_argument("--cem-w-goal", type=float, default=6.0)
|
| 553 |
+
parser.add_argument("--cem-w-path", type=float, default=0.2)
|
| 554 |
+
parser.add_argument("--cem-w-route", type=float, default=6.0)
|
| 555 |
+
parser.add_argument("--cem-w-lookahead", type=float, default=2.0)
|
| 556 |
+
parser.add_argument("--cem-w-via", type=float, default=2.0)
|
| 557 |
+
parser.add_argument("--cem-route-horizon-distance", type=float, default=3.0)
|
| 558 |
parser.add_argument("--cem-w-heading-goal", type=float, default=0.0)
|
| 559 |
+
parser.add_argument("--cem-w-action", type=float, default=0.08)
|
| 560 |
parser.add_argument("--cem-w-smooth", type=float, default=0.08)
|
| 561 |
+
parser.add_argument("--cem-w-boundary", type=float, default=250.0)
|
| 562 |
+
parser.add_argument("--cem-boundary-margin", type=float, default=0.75)
|
| 563 |
parser.add_argument("--cem-w-progress", type=float, default=2.0)
|
| 564 |
parser.add_argument("--success-radius", type=float, default=0.65)
|
| 565 |
parser.add_argument("--make-gifs", type=int, default=3)
|
|
|
|
| 568 |
parser.add_argument("--seed", type=int, default=33)
|
| 569 |
parser.add_argument("--device", default="cuda")
|
| 570 |
parser.add_argument("--precision", choices=["fp32", "bf16", "fp16"], default="fp32")
|
| 571 |
+
parser.add_argument("--out", default="experiments/reports/paper_planning")
|
| 572 |
args = parser.parse_args()
|
| 573 |
|
| 574 |
out_dir = Path(args.out)
|
experiments/planet/README.md
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
-
#
|
| 2 |
|
| 3 |
-
Recurrent state-space world
|
| 4 |
|
| 5 |
-
This
|
|
|
|
| 1 |
+
# PlaNet RSSM
|
| 2 |
|
| 3 |
+
Recurrent state-space world-model baseline under the shared clean-image boat drift benchmark.
|
| 4 |
|
| 5 |
+
This method uses an image encoder, recurrent latent dynamics, action-conditioned rollout, and decoded pose prediction under the same data, optimizer, and evaluation protocol as FlowMo.
|
experiments/planet/result/paper_training.json
ADDED
|
@@ -0,0 +1,43 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"method": "planet",
|
| 3 |
+
"steps": 20000,
|
| 4 |
+
"batch_size": 256,
|
| 5 |
+
"train_samples": 5120000,
|
| 6 |
+
"final_train_loss": 0.008471474051475525,
|
| 7 |
+
"total_parameters": 664644,
|
| 8 |
+
"target_mode": "absolute_normalized",
|
| 9 |
+
"position_scale": 5.0,
|
| 10 |
+
"heading_weight": 2.0,
|
| 11 |
+
"current_pose_weight": 1.0,
|
| 12 |
+
"motion_weight": 0.5,
|
| 13 |
+
"precision": "bf16",
|
| 14 |
+
"checkpoint_name": "paper.pt",
|
| 15 |
+
"final_checkpoint": "paper.pt",
|
| 16 |
+
"intermediate_checkpoints": [
|
| 17 |
+
"paper_step_002000.pt",
|
| 18 |
+
"paper_step_004000.pt",
|
| 19 |
+
"paper_step_006000.pt",
|
| 20 |
+
"paper_step_008000.pt",
|
| 21 |
+
"paper_step_010000.pt",
|
| 22 |
+
"paper_step_012000.pt",
|
| 23 |
+
"paper_step_014000.pt",
|
| 24 |
+
"paper_step_016000.pt",
|
| 25 |
+
"paper_step_018000.pt",
|
| 26 |
+
"paper_step_020000.pt"
|
| 27 |
+
],
|
| 28 |
+
"checkpoint_interval": 2000,
|
| 29 |
+
"prediction": {
|
| 30 |
+
"pos1": 0.12035437487065792,
|
| 31 |
+
"heading1": 0.050536025839392096,
|
| 32 |
+
"pos3": 0.12147162947803736,
|
| 33 |
+
"heading3": 0.05107929309209188,
|
| 34 |
+
"pos6": 0.13511362741701305,
|
| 35 |
+
"heading6": 0.05543786101043224,
|
| 36 |
+
"pos8": 0.14701085817068815,
|
| 37 |
+
"heading8": 0.05840683872035394,
|
| 38 |
+
"pos10": 0.16024279454723,
|
| 39 |
+
"heading10": 0.061416062798040606,
|
| 40 |
+
"pos20": 0.22345823872213563,
|
| 41 |
+
"heading20": 0.07620794516211997
|
| 42 |
+
}
|
| 43 |
+
}
|
experiments/planet/result/paper_training_trace.jsonl
ADDED
|
@@ -0,0 +1,100 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"method": "planet", "step": 200, "loss": 2.562255382537842}
|
| 2 |
+
{"method": "planet", "step": 400, "loss": 2.560068368911743}
|
| 3 |
+
{"method": "planet", "step": 600, "loss": 2.3322505950927734}
|
| 4 |
+
{"method": "planet", "step": 800, "loss": 1.9882159233093262}
|
| 5 |
+
{"method": "planet", "step": 1000, "loss": 1.8262876272201538}
|
| 6 |
+
{"method": "planet", "step": 1200, "loss": 1.7105216979980469}
|
| 7 |
+
{"method": "planet", "step": 1400, "loss": 1.6352806091308594}
|
| 8 |
+
{"method": "planet", "step": 1600, "loss": 1.5955568552017212}
|
| 9 |
+
{"method": "planet", "step": 1800, "loss": 1.5308232307434082}
|
| 10 |
+
{"method": "planet", "step": 2000, "loss": 1.1763442754745483}
|
| 11 |
+
{"method": "planet", "step": 2200, "loss": 0.8024235963821411}
|
| 12 |
+
{"method": "planet", "step": 2400, "loss": 0.31014516949653625}
|
| 13 |
+
{"method": "planet", "step": 2600, "loss": 0.16347827017307281}
|
| 14 |
+
{"method": "planet", "step": 2800, "loss": 0.12191551923751831}
|
| 15 |
+
{"method": "planet", "step": 3000, "loss": 0.09709076583385468}
|
| 16 |
+
{"method": "planet", "step": 3200, "loss": 0.08425617218017578}
|
| 17 |
+
{"method": "planet", "step": 3400, "loss": 0.07675585150718689}
|
| 18 |
+
{"method": "planet", "step": 3600, "loss": 0.06946409493684769}
|
| 19 |
+
{"method": "planet", "step": 3800, "loss": 0.06394364684820175}
|
| 20 |
+
{"method": "planet", "step": 4000, "loss": 0.058629147708415985}
|
| 21 |
+
{"method": "planet", "step": 4200, "loss": 0.05564868822693825}
|
| 22 |
+
{"method": "planet", "step": 4400, "loss": 0.05275396630167961}
|
| 23 |
+
{"method": "planet", "step": 4600, "loss": 0.04817806929349899}
|
| 24 |
+
{"method": "planet", "step": 4800, "loss": 0.04586486145853996}
|
| 25 |
+
{"method": "planet", "step": 5000, "loss": 0.04238906130194664}
|
| 26 |
+
{"method": "planet", "step": 5200, "loss": 0.04104524478316307}
|
| 27 |
+
{"method": "planet", "step": 5400, "loss": 0.03818673640489578}
|
| 28 |
+
{"method": "planet", "step": 5600, "loss": 0.038053277879953384}
|
| 29 |
+
{"method": "planet", "step": 5800, "loss": 0.04066094383597374}
|
| 30 |
+
{"method": "planet", "step": 6000, "loss": 0.03227749094367027}
|
| 31 |
+
{"method": "planet", "step": 6200, "loss": 0.02956106886267662}
|
| 32 |
+
{"method": "planet", "step": 6400, "loss": 0.03040161542594433}
|
| 33 |
+
{"method": "planet", "step": 6600, "loss": 0.027002347633242607}
|
| 34 |
+
{"method": "planet", "step": 6800, "loss": 0.025158630684018135}
|
| 35 |
+
{"method": "planet", "step": 7000, "loss": 0.02645196206867695}
|
| 36 |
+
{"method": "planet", "step": 7200, "loss": 0.024857478216290474}
|
| 37 |
+
{"method": "planet", "step": 7400, "loss": 0.02301337942481041}
|
| 38 |
+
{"method": "planet", "step": 7600, "loss": 0.022782575339078903}
|
| 39 |
+
{"method": "planet", "step": 7800, "loss": 0.04205402731895447}
|
| 40 |
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{"method": "planet", "step": 8000, "loss": 0.02556300349533558}
|
| 41 |
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{"method": "planet", "step": 8200, "loss": 0.019278213381767273}
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| 42 |
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{"method": "planet", "step": 8400, "loss": 0.017884887754917145}
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| 43 |
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{"method": "planet", "step": 8600, "loss": 0.017695849761366844}
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| 44 |
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{"method": "planet", "step": 8800, "loss": 0.017878837883472443}
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| 45 |
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{"method": "planet", "step": 9000, "loss": 0.01779993809759617}
|
| 46 |
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{"method": "planet", "step": 9200, "loss": 0.017646415159106255}
|
| 47 |
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{"method": "planet", "step": 9400, "loss": 0.022703692317008972}
|
| 48 |
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{"method": "planet", "step": 9600, "loss": 0.016452010720968246}
|
| 49 |
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{"method": "planet", "step": 9800, "loss": 0.01951923966407776}
|
| 50 |
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{"method": "planet", "step": 10000, "loss": 0.015538707375526428}
|
| 51 |
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{"method": "planet", "step": 10200, "loss": 0.014863155782222748}
|
| 52 |
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{"method": "planet", "step": 10400, "loss": 0.014721364714205265}
|
| 53 |
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{"method": "planet", "step": 10600, "loss": 0.014877153560519218}
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| 54 |
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{"method": "planet", "step": 10800, "loss": 0.014600856229662895}
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| 55 |
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{"method": "planet", "step": 11000, "loss": 0.013675257563591003}
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| 56 |
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{"method": "planet", "step": 11200, "loss": 0.014189041219651699}
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| 57 |
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{"method": "planet", "step": 11400, "loss": 0.05367814004421234}
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| 58 |
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{"method": "planet", "step": 11600, "loss": 0.017949046567082405}
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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{"method": "planet", "step": 12800, "loss": 0.012266267091035843}
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| 65 |
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{"method": "planet", "step": 13000, "loss": 0.012356869876384735}
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| 66 |
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{"method": "planet", "step": 13200, "loss": 0.012614256702363491}
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| 67 |
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{"method": "planet", "step": 13400, "loss": 0.011980431154370308}
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| 68 |
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{"method": "planet", "step": 13600, "loss": 0.012069996446371078}
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| 69 |
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{"method": "planet", "step": 13800, "loss": 0.011765953153371811}
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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{"method": "planet", "step": 15000, "loss": 0.011587102897465229}
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| 76 |
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{"method": "planet", "step": 15200, "loss": 0.011276498436927795}
|
| 77 |
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{"method": "planet", "step": 15400, "loss": 0.020549682900309563}
|
| 78 |
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{"method": "planet", "step": 15600, "loss": 0.011510416865348816}
|
| 79 |
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{"method": "planet", "step": 15800, "loss": 0.010764612816274166}
|
| 80 |
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{"method": "planet", "step": 16000, "loss": 0.010224522091448307}
|
| 81 |
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{"method": "planet", "step": 16200, "loss": 0.009960352443158627}
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| 82 |
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|
| 83 |
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|
| 84 |
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{"method": "planet", "step": 16800, "loss": 0.015476037748157978}
|
| 85 |
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{"method": "planet", "step": 17000, "loss": 0.015195525251328945}
|
| 86 |
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{"method": "planet", "step": 17200, "loss": 0.0104270800948143}
|
| 87 |
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{"method": "planet", "step": 17400, "loss": 0.009850576519966125}
|
| 88 |
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|
| 89 |
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{"method": "planet", "step": 17800, "loss": 0.009646783582866192}
|
| 90 |
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{"method": "planet", "step": 18000, "loss": 0.009326843544840813}
|
| 91 |
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{"method": "planet", "step": 18200, "loss": 0.009361878037452698}
|
| 92 |
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{"method": "planet", "step": 18400, "loss": 0.009450376033782959}
|
| 93 |
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{"method": "planet", "step": 18600, "loss": 0.011586474254727364}
|
| 94 |
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{"method": "planet", "step": 18800, "loss": 0.009417250752449036}
|
| 95 |
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{"method": "planet", "step": 19000, "loss": 0.008938208222389221}
|
| 96 |
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{"method": "planet", "step": 19200, "loss": 0.009088343009352684}
|
| 97 |
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{"method": "planet", "step": 19400, "loss": 0.009216764941811562}
|
| 98 |
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|
| 99 |
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{"method": "planet", "step": 19800, "loss": 0.011418974958360195}
|
| 100 |
+
{"method": "planet", "step": 20000, "loss": 0.008471474051475525}
|
experiments/planet/result/parameter_count.json
ADDED
|
@@ -0,0 +1,8 @@
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{
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| 2 |
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"encoder": 471584,
|
| 3 |
+
"recurrent": 83880,
|
| 4 |
+
"posterior": 39108,
|
| 5 |
+
"prior": 27588,
|
| 6 |
+
"decoder": 42484,
|
| 7 |
+
"total": 664644
|
| 8 |
+
}
|
experiments/reports/paper_flowmo_latent_probes.json
ADDED
|
@@ -0,0 +1,396 @@
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|
| 1 |
+
{
|
| 2 |
+
"method": "flowmo",
|
| 3 |
+
"checkpoint": "paper.pt",
|
| 4 |
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"train_source": "data/paper/train.npz",
|
| 5 |
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"train_windows": 32768,
|
| 6 |
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"eval_windows": 8192,
|
| 7 |
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"ridge_alpha": 0.001,
|
| 8 |
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"feature_sets": {
|
| 9 |
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"z": "short-history object-motion latent",
|
| 10 |
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"c": "long-history ambient-drift context",
|
| 11 |
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"z_c": "concatenated state and context"
|
| 12 |
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},
|
| 13 |
+
"targets": {
|
| 14 |
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"momentum": [
|
| 15 |
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"vx",
|
| 16 |
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|
| 17 |
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|
| 18 |
+
],
|
| 19 |
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"local_flow": [
|
| 20 |
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"flow_x",
|
| 21 |
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|
| 22 |
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],
|
| 23 |
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"episode_drift": [
|
| 24 |
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"mean_flow_x",
|
| 25 |
+
"mean_flow_y"
|
| 26 |
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]
|
| 27 |
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},
|
| 28 |
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"splits": {
|
| 29 |
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"unseen_flow": {
|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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"c": {
|
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|
| 47 |
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|
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|
| 49 |
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|
| 50 |
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|
| 51 |
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| 52 |
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|
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| 54 |
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|
| 55 |
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|
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|
| 57 |
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|
| 58 |
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|
| 59 |
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"z_c": {
|
| 60 |
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|
| 61 |
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|
| 63 |
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|
| 64 |
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|
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|
| 66 |
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|
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
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| 1730 |
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|
experiments/reports/paper_prediction_unseen_boat_params.json
ADDED
|
@@ -0,0 +1,1730 @@
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experiments/reports/paper_prediction_unseen_flow.json
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@@ -0,0 +1,1490 @@
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| 1478 |
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| 1479 |
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|
| 1480 |
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|
| 1481 |
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| 1482 |
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|
| 1483 |
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| 1484 |
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|
| 1485 |
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|
| 1486 |
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}
|
| 1487 |
+
}
|
| 1488 |
+
}
|
| 1489 |
+
}
|
| 1490 |
+
]
|
experiments/reports/paper_report.md
ADDED
|
@@ -0,0 +1,253 @@
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|
|
|
|
|
| 1 |
+
# FlowMo Clean-Image Paper Experiment Report
|
| 2 |
+
|
| 3 |
+
## Observation Setup
|
| 4 |
+
|
| 5 |
+
- Input: clean top-down RGB image, no flow arrows, no goal marker, no velocity vector.
|
| 6 |
+
- Image size: `160 x 160`.
|
| 7 |
+
- Boat visual scale: `2.5`.
|
| 8 |
+
- Training target: absolute normalized pose rollout plus shared current-pose observation head.
|
| 9 |
+
- Formal experiments include only A-category learned WMs and B-category traditional non-WM controllers.
|
| 10 |
+
|
| 11 |
+
## Parameter Counts
|
| 12 |
+
|
| 13 |
+
| Method | Total Parameters | Main Components |
|
| 14 |
+
|---|---:|---|
|
| 15 |
+
| FlowMo | 663,964 | encoder: 340,416, state_history: 75,648, context_history: 75,648, to_z: 30,960, to_c: 17,544, base_delta: 45,808, residual_delta: 46,448, decoder: 31,492 |
|
| 16 |
+
| LeWorldModel | 664,612 | encoder: 471,584, to_z: 43,328, transition: 87,104, decoder: 62,596 |
|
| 17 |
+
| PlaNet RSSM | 664,644 | encoder: 471,584, recurrent: 83,880, posterior: 39,108, prior: 27,588, decoder: 42,484 |
|
| 18 |
+
| TD-MPC2 Dynamics | 667,780 | encoder: 471,584, history: 87,936, to_z: 30,960, transition: 45,808, decoder: 31,492 |
|
| 19 |
+
|
| 20 |
+
## Prediction
|
| 21 |
+
|
| 22 |
+
Main claims use unseen flow families and unseen boat dynamics. The seen-flow-family diagnostic split is reported only as an optimization sanity check.
|
| 23 |
+
|
| 24 |
+
### Unseen Flow Families
|
| 25 |
+
|
| 26 |
+
| Method | pos@10 | pos@20 | pos@40 | pos@60 | heading@20 |
|
| 27 |
+
|---|---:|---:|---:|---:|---:|
|
| 28 |
+
| FlowMo | 0.103 | 0.146 | 0.236 | 0.334 | 0.065 |
|
| 29 |
+
| FlowMo c=0 | 0.253 | 0.405 | 0.618 | 0.780 | 0.122 |
|
| 30 |
+
| FlowMo shuffled c | 0.335 | 0.542 | 0.832 | 1.062 | 0.155 |
|
| 31 |
+
| LeWorldModel | 0.172 | 0.247 | 0.371 | 0.489 | 0.099 |
|
| 32 |
+
| PlaNet RSSM | 0.166 | 0.229 | 0.339 | 0.441 | 0.076 |
|
| 33 |
+
| TD-MPC2 Dynamics | 0.165 | 0.241 | 0.361 | 0.471 | 0.084 |
|
| 34 |
+
|
| 35 |
+
### Unseen Boat Dynamics
|
| 36 |
+
|
| 37 |
+
| Method | pos@10 | pos@20 | pos@40 | pos@60 | heading@20 |
|
| 38 |
+
|---|---:|---:|---:|---:|---:|
|
| 39 |
+
| FlowMo | 0.141 | 0.233 | 0.437 | 0.638 | 0.131 |
|
| 40 |
+
| FlowMo c=0 | 0.262 | 0.424 | 0.681 | 0.901 | 0.182 |
|
| 41 |
+
| FlowMo shuffled c | 0.354 | 0.578 | 0.921 | 1.210 | 0.217 |
|
| 42 |
+
| LeWorldModel | 0.219 | 0.350 | 0.590 | 0.811 | 0.170 |
|
| 43 |
+
| PlaNet RSSM | 0.202 | 0.317 | 0.533 | 0.725 | 0.138 |
|
| 44 |
+
| TD-MPC2 Dynamics | 0.215 | 0.342 | 0.572 | 0.782 | 0.157 |
|
| 45 |
+
|
| 46 |
+
### Seen Flow-Family Diagnostic
|
| 47 |
+
|
| 48 |
+
| Method | pos@10 | pos@20 | pos@40 | pos@60 | heading@20 |
|
| 49 |
+
|---|---:|---:|---:|---:|---:|
|
| 50 |
+
| FlowMo | 0.104 | 0.147 | 0.240 | 0.339 | 0.067 |
|
| 51 |
+
| FlowMo c=0 | 0.258 | 0.414 | 0.633 | 0.802 | 0.125 |
|
| 52 |
+
| FlowMo shuffled c | 0.343 | 0.557 | 0.858 | 1.098 | 0.159 |
|
| 53 |
+
| LeWorldModel | 0.179 | 0.259 | 0.385 | 0.506 | 0.100 |
|
| 54 |
+
| PlaNet RSSM | 0.160 | 0.223 | 0.332 | 0.436 | 0.076 |
|
| 55 |
+
| TD-MPC2 Dynamics | 0.164 | 0.240 | 0.358 | 0.470 | 0.088 |
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
## FlowMo Latent Probes
|
| 59 |
+
|
| 60 |
+
| Split | Target | z R2 | c R2 | z+c R2 |
|
| 61 |
+
|---|---|---:|---:|---:|
|
| 62 |
+
| unseen_flow | Object momentum | 0.518 | 0.416 | 0.737 |
|
| 63 |
+
| unseen_flow | Local flow | -0.019 | -0.005 | -0.023 |
|
| 64 |
+
| unseen_flow | Episode drift | -0.043 | -0.010 | -0.049 |
|
| 65 |
+
| unseen_boat_params | Object momentum | 0.404 | 0.292 | 0.615 |
|
| 66 |
+
| unseen_boat_params | Local flow | -0.014 | -0.003 | -0.014 |
|
| 67 |
+
| unseen_boat_params | Episode drift | -0.015 | -0.005 | -0.015 |
|
| 68 |
+
| seen_flow_diagnostic | Object momentum | 0.510 | 0.428 | 0.747 |
|
| 69 |
+
| seen_flow_diagnostic | Local flow | -0.034 | -0.009 | -0.034 |
|
| 70 |
+
| seen_flow_diagnostic | Episode drift | -0.034 | -0.009 | -0.035 |
|
| 71 |
+
|
| 72 |
+
Probe interpretation: `z` is the short-history object-motion latent, `c` is the long-history ambient-drift context, and `z+c` is their concatenation. Probes are linear and are trained after freezing FlowMo.
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
## Planning
|
| 76 |
+
|
| 77 |
+
### counterflow_triangle_uniform
|
| 78 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 79 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 80 |
+
| FlowMo | inferred | 1.000 | 50/50 | 0.646 | 144.16 | 6.27 | 162.3 |
|
| 81 |
+
| FlowMo | shuffled | 1.000 | 50/50 | 0.630 | 157.73 | 6.78 | 130.1 |
|
| 82 |
+
| FlowMo | zero | 1.000 | 50/50 | 0.637 | 147.00 | 6.30 | 97.5 |
|
| 83 |
+
| LeWorldModel | inferred | 1.000 | 50/50 | 0.622 | 141.57 | 6.64 | 109.6 |
|
| 84 |
+
| PlaNet RSSM | inferred | 1.000 | 50/50 | 0.632 | 179.71 | 7.48 | 145.9 |
|
| 85 |
+
| TD-MPC2 Dynamics | inferred | 1.000 | 50/50 | 0.627 | 128.67 | 5.90 | 102.8 |
|
| 86 |
+
| PID/LOS controller | inferred | 1.000 | 50/50 | 0.634 | 42.96 | 5.77 | 213.0 |
|
| 87 |
+
| Physics MPC No-Flow | inferred | 1.000 | 50/50 | 0.628 | 51.00 | 5.77 | 181.1 |
|
| 88 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.630 | 51.00 | 5.77 | 181.1 |
|
| 89 |
+
| Oracle-Flow MPC | inferred | 1.000 | 50/50 | 0.628 | 51.00 | 5.77 | 181.1 |
|
| 90 |
+
|
| 91 |
+
### counterflow_twin_uniform
|
| 92 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 93 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 94 |
+
| FlowMo | inferred | 1.000 | 50/50 | 0.642 | 94.99 | 5.79 | 130.9 |
|
| 95 |
+
| FlowMo | shuffled | 0.960 | 48/50 | 0.674 | 123.71 | 5.88 | 86.7 |
|
| 96 |
+
| FlowMo | zero | 1.000 | 50/50 | 0.614 | 123.73 | 5.80 | 82.0 |
|
| 97 |
+
| LeWorldModel | inferred | 1.000 | 50/50 | 0.626 | 109.22 | 5.81 | 99.4 |
|
| 98 |
+
| PlaNet RSSM | inferred | 1.000 | 50/50 | 0.629 | 108.57 | 5.78 | 91.8 |
|
| 99 |
+
| TD-MPC2 Dynamics | inferred | 1.000 | 50/50 | 0.622 | 106.70 | 5.79 | 88.1 |
|
| 100 |
+
| PID/LOS controller | inferred | 1.000 | 50/50 | 0.618 | 69.89 | 5.78 | 115.5 |
|
| 101 |
+
| Physics MPC No-Flow | inferred | 1.000 | 50/50 | 0.611 | 86.04 | 5.79 | 101.8 |
|
| 102 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.618 | 80.96 | 5.78 | 102.5 |
|
| 103 |
+
| Oracle-Flow MPC | inferred | 1.000 | 50/50 | 0.611 | 86.04 | 5.79 | 101.8 |
|
| 104 |
+
|
| 105 |
+
### passive_to_active_triangle_uniform
|
| 106 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 107 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 108 |
+
| FlowMo | inferred | 1.000 | 50/50 | 0.638 | 168.95 | 8.38 | 163.0 |
|
| 109 |
+
| FlowMo | shuffled | 0.900 | 45/50 | 0.746 | 212.46 | 9.46 | 160.1 |
|
| 110 |
+
| FlowMo | zero | 0.980 | 49/50 | 0.650 | 215.09 | 9.31 | 158.4 |
|
| 111 |
+
| LeWorldModel | inferred | 0.960 | 48/50 | 0.834 | 194.00 | 9.14 | 155.8 |
|
| 112 |
+
| PlaNet RSSM | inferred | 0.980 | 49/50 | 0.630 | 156.19 | 8.37 | 152.2 |
|
| 113 |
+
| TD-MPC2 Dynamics | inferred | 0.980 | 49/50 | 0.698 | 180.29 | 9.47 | 162.3 |
|
| 114 |
+
| PID/LOS controller | inferred | 0.980 | 49/50 | 0.644 | 77.96 | 8.07 | 239.5 |
|
| 115 |
+
| Physics MPC No-Flow | inferred | 1.000 | 50/50 | 0.622 | 75.08 | 8.06 | 217.3 |
|
| 116 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.629 | 78.30 | 8.00 | 216.7 |
|
| 117 |
+
| Oracle-Flow MPC | inferred | 1.000 | 50/50 | 0.624 | 75.00 | 8.02 | 216.6 |
|
| 118 |
+
|
| 119 |
+
### passive_to_active_twin_uniform
|
| 120 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 121 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 122 |
+
| FlowMo | inferred | 0.980 | 49/50 | 0.804 | 161.52 | 8.59 | 195.3 |
|
| 123 |
+
| FlowMo | shuffled | 0.660 | 33/50 | 1.571 | 192.00 | 9.74 | 173.0 |
|
| 124 |
+
| FlowMo | zero | 0.640 | 32/50 | 1.426 | 233.83 | 10.70 | 197.1 |
|
| 125 |
+
| LeWorldModel | inferred | 0.660 | 33/50 | 2.027 | 194.64 | 9.37 | 168.9 |
|
| 126 |
+
| PlaNet RSSM | inferred | 0.740 | 37/50 | 1.014 | 205.79 | 9.96 | 181.6 |
|
| 127 |
+
| TD-MPC2 Dynamics | inferred | 0.640 | 32/50 | 2.128 | 160.81 | 9.41 | 176.8 |
|
| 128 |
+
| PID/LOS controller | inferred | 0.980 | 49/50 | 0.631 | 111.36 | 8.38 | 185.8 |
|
| 129 |
+
| Physics MPC No-Flow | inferred | 0.900 | 45/50 | 0.699 | 126.01 | 8.82 | 186.9 |
|
| 130 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.623 | 118.66 | 8.54 | 182.4 |
|
| 131 |
+
| Oracle-Flow MPC | inferred | 0.880 | 44/50 | 0.689 | 116.72 | 8.45 | 174.5 |
|
| 132 |
+
|
| 133 |
+
### reach_uniform_triangle_uniform
|
| 134 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 135 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 136 |
+
| FlowMo | inferred | 1.000 | 50/50 | 0.637 | 168.98 | 8.30 | 136.2 |
|
| 137 |
+
| FlowMo | shuffled | 0.840 | 42/50 | 0.867 | 203.59 | 9.15 | 133.0 |
|
| 138 |
+
| FlowMo | zero | 0.960 | 48/50 | 0.682 | 215.72 | 9.08 | 130.8 |
|
| 139 |
+
| LeWorldModel | inferred | 0.980 | 49/50 | 0.739 | 202.95 | 9.33 | 134.6 |
|
| 140 |
+
| PlaNet RSSM | inferred | 1.000 | 50/50 | 0.628 | 158.79 | 8.39 | 130.5 |
|
| 141 |
+
| TD-MPC2 Dynamics | inferred | 0.980 | 49/50 | 0.692 | 182.64 | 9.46 | 136.7 |
|
| 142 |
+
| PID/LOS controller | inferred | 1.000 | 50/50 | 0.629 | 78.46 | 8.00 | 218.5 |
|
| 143 |
+
| Physics MPC No-Flow | inferred | 1.000 | 50/50 | 0.623 | 74.44 | 7.99 | 192.1 |
|
| 144 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.627 | 77.65 | 7.93 | 191.6 |
|
| 145 |
+
| Oracle-Flow MPC | inferred | 1.000 | 50/50 | 0.628 | 74.93 | 7.94 | 191.3 |
|
| 146 |
+
|
| 147 |
+
### reach_uniform_twin_uniform
|
| 148 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 149 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 150 |
+
| FlowMo | inferred | 0.980 | 49/50 | 0.738 | 162.72 | 8.61 | 173.0 |
|
| 151 |
+
| FlowMo | shuffled | 0.700 | 35/50 | 1.509 | 203.32 | 9.82 | 154.7 |
|
| 152 |
+
| FlowMo | zero | 0.640 | 32/50 | 1.442 | 217.56 | 10.24 | 158.3 |
|
| 153 |
+
| LeWorldModel | inferred | 0.720 | 36/50 | 1.684 | 219.39 | 9.96 | 163.6 |
|
| 154 |
+
| PlaNet RSSM | inferred | 0.820 | 41/50 | 0.938 | 219.60 | 10.25 | 165.6 |
|
| 155 |
+
| TD-MPC2 Dynamics | inferred | 0.640 | 32/50 | 2.114 | 157.66 | 9.37 | 149.3 |
|
| 156 |
+
| PID/LOS controller | inferred | 0.980 | 49/50 | 0.634 | 111.31 | 8.29 | 160.9 |
|
| 157 |
+
| Physics MPC No-Flow | inferred | 0.920 | 46/50 | 0.688 | 125.69 | 8.72 | 161.5 |
|
| 158 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.625 | 118.62 | 8.45 | 157.5 |
|
| 159 |
+
| Oracle-Flow MPC | inferred | 0.860 | 43/50 | 0.697 | 115.29 | 8.32 | 147.8 |
|
| 160 |
+
|
| 161 |
+
### station_keeping_triangle_uniform
|
| 162 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 163 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 164 |
+
| FlowMo | inferred | 1.000 | 50/50 | 0.105 | 24.60 | 0.76 | 101.0 |
|
| 165 |
+
| FlowMo | shuffled | 0.880 | 44/50 | 0.668 | 86.66 | 4.13 | 145.0 |
|
| 166 |
+
| FlowMo | zero | 1.000 | 50/50 | 0.449 | 33.50 | 1.60 | 103.8 |
|
| 167 |
+
| LeWorldModel | inferred | 1.000 | 50/50 | 0.345 | 58.17 | 1.22 | 105.7 |
|
| 168 |
+
| PlaNet RSSM | inferred | 1.000 | 50/50 | 0.539 | 36.11 | 1.32 | 109.2 |
|
| 169 |
+
| TD-MPC2 Dynamics | inferred | 1.000 | 50/50 | 0.473 | 42.38 | 0.97 | 101.9 |
|
| 170 |
+
| PID/LOS controller | inferred | 1.000 | 50/50 | 0.094 | 101.33 | 0.75 | 101.0 |
|
| 171 |
+
| Physics MPC No-Flow | inferred | 1.000 | 50/50 | 0.132 | 87.17 | 0.92 | 101.0 |
|
| 172 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.148 | 88.16 | 0.96 | 101.0 |
|
| 173 |
+
| Oracle-Flow MPC | inferred | 1.000 | 50/50 | 0.269 | 37.87 | 1.18 | 101.0 |
|
| 174 |
+
|
| 175 |
+
### station_keeping_twin_uniform
|
| 176 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 177 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 178 |
+
| FlowMo | inferred | 1.000 | 50/50 | 0.277 | 35.20 | 0.60 | 101.0 |
|
| 179 |
+
| FlowMo | shuffled | 0.660 | 33/50 | 0.821 | 79.29 | 2.40 | 152.4 |
|
| 180 |
+
| FlowMo | zero | 1.000 | 50/50 | 0.563 | 35.98 | 0.93 | 108.6 |
|
| 181 |
+
| LeWorldModel | inferred | 1.000 | 50/50 | 0.391 | 11.32 | 0.55 | 101.0 |
|
| 182 |
+
| PlaNet RSSM | inferred | 0.980 | 49/50 | 0.541 | 88.16 | 0.60 | 101.6 |
|
| 183 |
+
| TD-MPC2 Dynamics | inferred | 1.000 | 50/50 | 0.301 | 11.79 | 1.03 | 101.0 |
|
| 184 |
+
| PID/LOS controller | inferred | 0.960 | 48/50 | 0.598 | 78.07 | 1.19 | 109.1 |
|
| 185 |
+
| Physics MPC No-Flow | inferred | 0.260 | 13/50 | 1.166 | 69.87 | 1.95 | 144.0 |
|
| 186 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.645 | 85.14 | 2.39 | 169.1 |
|
| 187 |
+
| Oracle-Flow MPC | inferred | 0.860 | 43/50 | 0.626 | 51.48 | 0.99 | 108.0 |
|
| 188 |
+
|
| 189 |
+
### waypoint_square_triangle_gradient
|
| 190 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 191 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 192 |
+
| FlowMo | inferred | 1.000 | 50/50 | 0.640 | 299.62 | 15.66 | 267.7 |
|
| 193 |
+
| FlowMo | shuffled | 0.620 | 31/50 | 1.644 | 591.60 | 23.39 | 371.9 |
|
| 194 |
+
| FlowMo | zero | 0.800 | 40/50 | 1.379 | 501.45 | 20.43 | 286.5 |
|
| 195 |
+
| LeWorldModel | inferred | 0.880 | 44/50 | 0.968 | 447.58 | 19.56 | 284.8 |
|
| 196 |
+
| PlaNet RSSM | inferred | 0.940 | 47/50 | 0.855 | 305.87 | 15.94 | 276.4 |
|
| 197 |
+
| TD-MPC2 Dynamics | inferred | 0.920 | 46/50 | 0.860 | 439.87 | 20.66 | 342.0 |
|
| 198 |
+
| PID/LOS controller | inferred | 1.000 | 50/50 | 0.631 | 187.41 | 15.52 | 416.3 |
|
| 199 |
+
| Physics MPC No-Flow | inferred | 1.000 | 50/50 | 0.624 | 165.97 | 15.74 | 369.8 |
|
| 200 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.631 | 182.30 | 14.93 | 368.0 |
|
| 201 |
+
| Oracle-Flow MPC | inferred | 1.000 | 50/50 | 0.629 | 164.86 | 15.59 | 366.9 |
|
| 202 |
+
|
| 203 |
+
### waypoint_square_twin_gradient
|
| 204 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 205 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 206 |
+
| FlowMo | inferred | 0.760 | 38/50 | 1.425 | 353.93 | 17.11 | 361.6 |
|
| 207 |
+
| FlowMo | shuffled | 0.440 | 22/50 | 2.661 | 589.01 | 26.62 | 483.3 |
|
| 208 |
+
| FlowMo | zero | 0.680 | 34/50 | 1.542 | 579.75 | 25.16 | 447.2 |
|
| 209 |
+
| LeWorldModel | inferred | 0.640 | 32/50 | 1.819 | 525.35 | 24.29 | 412.0 |
|
| 210 |
+
| PlaNet RSSM | inferred | 0.500 | 25/50 | 2.664 | 455.73 | 19.86 | 406.3 |
|
| 211 |
+
| TD-MPC2 Dynamics | inferred | 0.600 | 30/50 | 1.940 | 432.08 | 21.20 | 405.3 |
|
| 212 |
+
| PID/LOS controller | inferred | 0.600 | 30/50 | 1.366 | 310.11 | 20.36 | 394.3 |
|
| 213 |
+
| Physics MPC No-Flow | inferred | 0.160 | 8/50 | 2.177 | 316.06 | 22.46 | 431.2 |
|
| 214 |
+
| Current-Estimator MPC | inferred | 0.820 | 41/50 | 1.213 | 303.16 | 19.17 | 433.5 |
|
| 215 |
+
| Oracle-Flow MPC | inferred | 0.060 | 3/50 | 2.004 | 354.09 | 21.01 | 485.7 |
|
| 216 |
+
|
| 217 |
+
### waypoint_zigzag_triangle_gradient
|
| 218 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 219 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 220 |
+
| FlowMo | inferred | 0.980 | 49/50 | 0.703 | 400.64 | 16.90 | 385.0 |
|
| 221 |
+
| FlowMo | shuffled | 0.660 | 33/50 | 1.546 | 685.03 | 27.67 | 454.7 |
|
| 222 |
+
| FlowMo | zero | 0.980 | 49/50 | 0.692 | 662.20 | 26.88 | 419.6 |
|
| 223 |
+
| LeWorldModel | inferred | 1.000 | 50/50 | 0.615 | 557.45 | 22.98 | 361.4 |
|
| 224 |
+
| PlaNet RSSM | inferred | 0.840 | 42/50 | 1.208 | 436.56 | 19.65 | 364.2 |
|
| 225 |
+
| TD-MPC2 Dynamics | inferred | 1.000 | 50/50 | 0.627 | 464.03 | 21.37 | 368.3 |
|
| 226 |
+
| PID/LOS controller | inferred | 1.000 | 50/50 | 0.630 | 296.45 | 17.78 | 541.5 |
|
| 227 |
+
| Physics MPC No-Flow | inferred | 1.000 | 50/50 | 0.626 | 255.95 | 18.19 | 489.9 |
|
| 228 |
+
| Current-Estimator MPC | inferred | 1.000 | 50/50 | 0.628 | 257.53 | 17.03 | 471.9 |
|
| 229 |
+
| Oracle-Flow MPC | inferred | 1.000 | 50/50 | 0.626 | 255.53 | 18.04 | 486.8 |
|
| 230 |
+
|
| 231 |
+
### waypoint_zigzag_twin_gradient
|
| 232 |
+
| Method | Context | Success | Successes | Final Dist | Energy (Success) | Path Len (Success) | Steps (Success) |
|
| 233 |
+
|---|---|---:|---:|---:|---:|---:|---:|
|
| 234 |
+
| FlowMo | inferred | 0.960 | 48/50 | 0.731 | 395.72 | 18.19 | 411.3 |
|
| 235 |
+
| FlowMo | shuffled | 0.560 | 28/50 | 2.051 | 644.22 | 28.79 | 522.9 |
|
| 236 |
+
| FlowMo | zero | 0.920 | 46/50 | 0.766 | 643.96 | 27.35 | 484.6 |
|
| 237 |
+
| LeWorldModel | inferred | 0.840 | 42/50 | 1.183 | 599.71 | 26.11 | 452.6 |
|
| 238 |
+
| PlaNet RSSM | inferred | 0.880 | 44/50 | 0.836 | 622.65 | 26.56 | 548.1 |
|
| 239 |
+
| TD-MPC2 Dynamics | inferred | 0.720 | 36/50 | 2.202 | 480.98 | 24.07 | 433.1 |
|
| 240 |
+
| PID/LOS controller | inferred | 0.780 | 39/50 | 1.220 | 423.71 | 27.71 | 595.6 |
|
| 241 |
+
| Physics MPC No-Flow | inferred | 0.220 | 11/50 | 2.217 | 413.60 | 29.55 | 642.1 |
|
| 242 |
+
| Current-Estimator MPC | inferred | 0.860 | 43/50 | 0.672 | 386.66 | 24.86 | 548.5 |
|
| 243 |
+
| Oracle-Flow MPC | inferred | 0.240 | 12/50 | 2.062 | 398.71 | 29.40 | 615.3 |
|
| 244 |
+
|
| 245 |
+
|
| 246 |
+
## Artifact Locations
|
| 247 |
+
|
| 248 |
+
- Unseen-flow prediction JSON: `experiments/reports/paper_prediction_unseen_flow.json`
|
| 249 |
+
- Unseen-boat-dynamics prediction JSON: `experiments/reports/paper_prediction_unseen_boat_params.json`
|
| 250 |
+
- Diagnostic prediction JSON: `experiments/reports/paper_prediction_seen_flow_diagnostic.json`
|
| 251 |
+
- FlowMo latent probe JSON: `experiments/reports/paper_flowmo_latent_probes.json`
|
| 252 |
+
- Planning JSON files: `experiments/reports/paper_planning/*.json`
|
| 253 |
+
- GIFs: `experiments/reports/paper_planning/gifs/*.gif`
|
experiments/reports/paper_training_summary.json
ADDED
|
@@ -0,0 +1,174 @@
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|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"method": "flowmo",
|
| 4 |
+
"steps": 20000,
|
| 5 |
+
"batch_size": 256,
|
| 6 |
+
"train_samples": 5120000,
|
| 7 |
+
"final_train_loss": 0.004372047260403633,
|
| 8 |
+
"total_parameters": 663964,
|
| 9 |
+
"target_mode": "absolute_normalized",
|
| 10 |
+
"position_scale": 5.0,
|
| 11 |
+
"heading_weight": 2.0,
|
| 12 |
+
"current_pose_weight": 1.0,
|
| 13 |
+
"motion_weight": 0.5,
|
| 14 |
+
"precision": "bf16",
|
| 15 |
+
"checkpoint_name": "paper.pt",
|
| 16 |
+
"final_checkpoint": "paper.pt",
|
| 17 |
+
"intermediate_checkpoints": [
|
| 18 |
+
"paper_step_002000.pt",
|
| 19 |
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"paper_step_004000.pt",
|
| 20 |
+
"paper_step_006000.pt",
|
| 21 |
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"paper_step_008000.pt",
|
| 22 |
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"paper_step_010000.pt",
|
| 23 |
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"paper_step_012000.pt",
|
| 24 |
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"paper_step_014000.pt",
|
| 25 |
+
"paper_step_016000.pt",
|
| 26 |
+
"paper_step_018000.pt",
|
| 27 |
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"paper_step_020000.pt"
|
| 28 |
+
],
|
| 29 |
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"checkpoint_interval": 2000,
|
| 30 |
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|
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}
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| 44 |
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},
|
| 45 |
+
{
|
| 46 |
+
"method": "leworldmodel",
|
| 47 |
+
"steps": 20000,
|
| 48 |
+
"batch_size": 256,
|
| 49 |
+
"train_samples": 5120000,
|
| 50 |
+
"final_train_loss": 0.018198927864432335,
|
| 51 |
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"total_parameters": 664612,
|
| 52 |
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"target_mode": "absolute_normalized",
|
| 53 |
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"position_scale": 5.0,
|
| 54 |
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"heading_weight": 2.0,
|
| 55 |
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"current_pose_weight": 1.0,
|
| 56 |
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"motion_weight": 0.5,
|
| 57 |
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"precision": "bf16",
|
| 58 |
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"checkpoint_name": "paper.pt",
|
| 59 |
+
"final_checkpoint": "paper.pt",
|
| 60 |
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"intermediate_checkpoints": [
|
| 61 |
+
"paper_step_002000.pt",
|
| 62 |
+
"paper_step_004000.pt",
|
| 63 |
+
"paper_step_006000.pt",
|
| 64 |
+
"paper_step_008000.pt",
|
| 65 |
+
"paper_step_010000.pt",
|
| 66 |
+
"paper_step_012000.pt",
|
| 67 |
+
"paper_step_014000.pt",
|
| 68 |
+
"paper_step_016000.pt",
|
| 69 |
+
"paper_step_018000.pt",
|
| 70 |
+
"paper_step_020000.pt"
|
| 71 |
+
],
|
| 72 |
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"checkpoint_interval": 2000,
|
| 73 |
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"prediction": {
|
| 74 |
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|
| 75 |
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|
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|
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|
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| 83 |
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"heading10": 0.07669965278667708,
|
| 84 |
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"pos20": 0.25807287978629273,
|
| 85 |
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"heading20": 0.0995667139844348
|
| 86 |
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}
|
| 87 |
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},
|
| 88 |
+
{
|
| 89 |
+
"method": "planet",
|
| 90 |
+
"steps": 20000,
|
| 91 |
+
"batch_size": 256,
|
| 92 |
+
"train_samples": 5120000,
|
| 93 |
+
"final_train_loss": 0.008471474051475525,
|
| 94 |
+
"total_parameters": 664644,
|
| 95 |
+
"target_mode": "absolute_normalized",
|
| 96 |
+
"position_scale": 5.0,
|
| 97 |
+
"heading_weight": 2.0,
|
| 98 |
+
"current_pose_weight": 1.0,
|
| 99 |
+
"motion_weight": 0.5,
|
| 100 |
+
"precision": "bf16",
|
| 101 |
+
"checkpoint_name": "paper.pt",
|
| 102 |
+
"final_checkpoint": "paper.pt",
|
| 103 |
+
"intermediate_checkpoints": [
|
| 104 |
+
"paper_step_002000.pt",
|
| 105 |
+
"paper_step_004000.pt",
|
| 106 |
+
"paper_step_006000.pt",
|
| 107 |
+
"paper_step_008000.pt",
|
| 108 |
+
"paper_step_010000.pt",
|
| 109 |
+
"paper_step_012000.pt",
|
| 110 |
+
"paper_step_014000.pt",
|
| 111 |
+
"paper_step_016000.pt",
|
| 112 |
+
"paper_step_018000.pt",
|
| 113 |
+
"paper_step_020000.pt"
|
| 114 |
+
],
|
| 115 |
+
"checkpoint_interval": 2000,
|
| 116 |
+
"prediction": {
|
| 117 |
+
"pos1": 0.12035437487065792,
|
| 118 |
+
"heading1": 0.050536025839392096,
|
| 119 |
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|
| 120 |
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"heading3": 0.05107929309209188,
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| 121 |
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|
| 122 |
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"heading6": 0.05543786101043224,
|
| 123 |
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|
| 124 |
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"heading8": 0.05840683872035394,
|
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| 126 |
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|
| 127 |
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"pos20": 0.22345823872213563,
|
| 128 |
+
"heading20": 0.07620794516211997
|
| 129 |
+
}
|
| 130 |
+
},
|
| 131 |
+
{
|
| 132 |
+
"method": "tdmpc2",
|
| 133 |
+
"steps": 20000,
|
| 134 |
+
"batch_size": 256,
|
| 135 |
+
"train_samples": 5120000,
|
| 136 |
+
"final_train_loss": 0.010143937543034554,
|
| 137 |
+
"total_parameters": 667780,
|
| 138 |
+
"target_mode": "absolute_normalized",
|
| 139 |
+
"position_scale": 5.0,
|
| 140 |
+
"heading_weight": 2.0,
|
| 141 |
+
"current_pose_weight": 1.0,
|
| 142 |
+
"motion_weight": 0.5,
|
| 143 |
+
"precision": "bf16",
|
| 144 |
+
"checkpoint_name": "paper.pt",
|
| 145 |
+
"final_checkpoint": "paper.pt",
|
| 146 |
+
"intermediate_checkpoints": [
|
| 147 |
+
"paper_step_002000.pt",
|
| 148 |
+
"paper_step_004000.pt",
|
| 149 |
+
"paper_step_006000.pt",
|
| 150 |
+
"paper_step_008000.pt",
|
| 151 |
+
"paper_step_010000.pt",
|
| 152 |
+
"paper_step_012000.pt",
|
| 153 |
+
"paper_step_014000.pt",
|
| 154 |
+
"paper_step_016000.pt",
|
| 155 |
+
"paper_step_018000.pt",
|
| 156 |
+
"paper_step_020000.pt"
|
| 157 |
+
],
|
| 158 |
+
"checkpoint_interval": 2000,
|
| 159 |
+
"prediction": {
|
| 160 |
+
"pos1": 0.09520710981450975,
|
| 161 |
+
"heading1": 0.048679903343630336,
|
| 162 |
+
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|
| 163 |
+
"heading3": 0.053618756433327995,
|
| 164 |
+
"pos6": 0.1308438451960683,
|
| 165 |
+
"heading6": 0.06132081007429709,
|
| 166 |
+
"pos8": 0.14709665269280472,
|
| 167 |
+
"heading8": 0.06618776960143198,
|
| 168 |
+
"pos10": 0.1638579194744428,
|
| 169 |
+
"heading10": 0.07064890124214192,
|
| 170 |
+
"pos20": 0.23970605929692587,
|
| 171 |
+
"heading20": 0.08867141495769222
|
| 172 |
+
}
|
| 173 |
+
}
|
| 174 |
+
]
|
experiments/run_paper_image_pipeline.py
CHANGED
|
@@ -141,6 +141,11 @@ def stage_planning(cfg: dict, args, task: str, boat: str) -> list[str]:
|
|
| 141 |
max_steps = args.max_steps or task_max_steps.get(task, plan["max_steps"])
|
| 142 |
methods = args.methods or cfg["training"]["methods"]
|
| 143 |
methods = methods + TRADITIONAL_METHODS
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 144 |
return [
|
| 145 |
sys.executable,
|
| 146 |
"-m",
|
|
@@ -157,12 +162,6 @@ def stage_planning(cfg: dict, args, task: str, boat: str) -> list[str]:
|
|
| 157 |
str(args.planning_episodes or plan["episodes"]),
|
| 158 |
"--max-steps",
|
| 159 |
str(max_steps),
|
| 160 |
-
"--planner",
|
| 161 |
-
args.planner or plan.get("planner", "gradient"),
|
| 162 |
-
"--planner-iterations",
|
| 163 |
-
str(args.planner_iterations if args.planner_iterations is not None else plan.get("planner_iterations", 30)),
|
| 164 |
-
"--planner-lr",
|
| 165 |
-
str(args.planner_lr if args.planner_lr is not None else plan.get("planner_lr", 0.08)),
|
| 166 |
"--make-gifs",
|
| 167 |
str(args.make_gifs if args.make_gifs is not None else plan["make_gifs"]),
|
| 168 |
"--gif-stride",
|
|
@@ -182,21 +181,33 @@ def stage_planning(cfg: dict, args, task: str, boat: str) -> list[str]:
|
|
| 182 |
"--cem-iterations",
|
| 183 |
str(args.cem_iterations or plan["cem_iterations"]),
|
| 184 |
"--cem-action-std",
|
| 185 |
-
str(
|
| 186 |
"--cem-knots",
|
| 187 |
-
str(
|
|
|
|
|
|
|
|
|
|
|
|
|
| 188 |
"--cem-w-route",
|
| 189 |
-
str(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 190 |
"--cem-w-heading-goal",
|
| 191 |
-
str(
|
| 192 |
"--cem-w-progress",
|
| 193 |
-
str(
|
| 194 |
"--cem-w-action",
|
| 195 |
-
str(
|
| 196 |
"--cem-w-smooth",
|
| 197 |
-
str(
|
| 198 |
"--cem-w-boundary",
|
| 199 |
-
str(
|
|
|
|
|
|
|
| 200 |
"--device",
|
| 201 |
args.device,
|
| 202 |
"--precision",
|
|
@@ -286,21 +297,24 @@ def main() -> None:
|
|
| 286 |
parser.add_argument("--steps", type=int, default=None)
|
| 287 |
parser.add_argument("--planning-episodes", type=int, default=None)
|
| 288 |
parser.add_argument("--max-steps", type=int, default=None)
|
| 289 |
-
parser.add_argument("--planner", choices=["gradient", "cem"], default=None)
|
| 290 |
-
parser.add_argument("--planner-iterations", type=int, default=None)
|
| 291 |
-
parser.add_argument("--planner-lr", type=float, default=None)
|
| 292 |
parser.add_argument("--cem-horizon", type=int, default=None)
|
| 293 |
parser.add_argument("--cem-population", type=int, default=None)
|
| 294 |
parser.add_argument("--cem-elites", type=int, default=None)
|
| 295 |
parser.add_argument("--cem-iterations", type=int, default=None)
|
| 296 |
parser.add_argument("--cem-action-std", type=float, default=None)
|
| 297 |
parser.add_argument("--cem-knots", type=int, default=None)
|
|
|
|
|
|
|
| 298 |
parser.add_argument("--cem-w-route", type=float, default=None)
|
|
|
|
|
|
|
|
|
|
| 299 |
parser.add_argument("--cem-w-heading-goal", type=float, default=None)
|
| 300 |
parser.add_argument("--cem-w-progress", type=float, default=None)
|
| 301 |
parser.add_argument("--cem-w-action", type=float, default=None)
|
| 302 |
parser.add_argument("--cem-w-smooth", type=float, default=None)
|
| 303 |
parser.add_argument("--cem-w-boundary", type=float, default=None)
|
|
|
|
| 304 |
parser.add_argument("--make-gifs", type=int, default=None)
|
| 305 |
parser.add_argument("--gif-stride", type=int, default=1)
|
| 306 |
parser.add_argument("--gif-duration-ms", type=int, default=55)
|
|
|
|
| 141 |
max_steps = args.max_steps or task_max_steps.get(task, plan["max_steps"])
|
| 142 |
methods = args.methods or cfg["training"]["methods"]
|
| 143 |
methods = methods + TRADITIONAL_METHODS
|
| 144 |
+
|
| 145 |
+
def arg_value(name: str, default):
|
| 146 |
+
value = getattr(args, name, None)
|
| 147 |
+
return value if value is not None else plan.get(name, default)
|
| 148 |
+
|
| 149 |
return [
|
| 150 |
sys.executable,
|
| 151 |
"-m",
|
|
|
|
| 162 |
str(args.planning_episodes or plan["episodes"]),
|
| 163 |
"--max-steps",
|
| 164 |
str(max_steps),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 165 |
"--make-gifs",
|
| 166 |
str(args.make_gifs if args.make_gifs is not None else plan["make_gifs"]),
|
| 167 |
"--gif-stride",
|
|
|
|
| 181 |
"--cem-iterations",
|
| 182 |
str(args.cem_iterations or plan["cem_iterations"]),
|
| 183 |
"--cem-action-std",
|
| 184 |
+
str(arg_value("cem_action_std", 0.5)),
|
| 185 |
"--cem-knots",
|
| 186 |
+
str(arg_value("cem_knots", 5)),
|
| 187 |
+
"--cem-w-goal",
|
| 188 |
+
str(arg_value("cem_w_goal", 8.0)),
|
| 189 |
+
"--cem-w-path",
|
| 190 |
+
str(arg_value("cem_w_path", 0.45)),
|
| 191 |
"--cem-w-route",
|
| 192 |
+
str(arg_value("cem_w_route", 1.0)),
|
| 193 |
+
"--cem-w-lookahead",
|
| 194 |
+
str(arg_value("cem_w_lookahead", 1.5)),
|
| 195 |
+
"--cem-w-via",
|
| 196 |
+
str(arg_value("cem_w_via", 1.5)),
|
| 197 |
+
"--cem-route-horizon-distance",
|
| 198 |
+
str(arg_value("cem_route_horizon_distance", 2.5)),
|
| 199 |
"--cem-w-heading-goal",
|
| 200 |
+
str(arg_value("cem_w_heading_goal", 0.0)),
|
| 201 |
"--cem-w-progress",
|
| 202 |
+
str(arg_value("cem_w_progress", 2.0)),
|
| 203 |
"--cem-w-action",
|
| 204 |
+
str(arg_value("cem_w_action", 0.04)),
|
| 205 |
"--cem-w-smooth",
|
| 206 |
+
str(arg_value("cem_w_smooth", 0.08)),
|
| 207 |
"--cem-w-boundary",
|
| 208 |
+
str(arg_value("cem_w_boundary", 10.0)),
|
| 209 |
+
"--cem-boundary-margin",
|
| 210 |
+
str(arg_value("cem_boundary_margin", 0.75)),
|
| 211 |
"--device",
|
| 212 |
args.device,
|
| 213 |
"--precision",
|
|
|
|
| 297 |
parser.add_argument("--steps", type=int, default=None)
|
| 298 |
parser.add_argument("--planning-episodes", type=int, default=None)
|
| 299 |
parser.add_argument("--max-steps", type=int, default=None)
|
|
|
|
|
|
|
|
|
|
| 300 |
parser.add_argument("--cem-horizon", type=int, default=None)
|
| 301 |
parser.add_argument("--cem-population", type=int, default=None)
|
| 302 |
parser.add_argument("--cem-elites", type=int, default=None)
|
| 303 |
parser.add_argument("--cem-iterations", type=int, default=None)
|
| 304 |
parser.add_argument("--cem-action-std", type=float, default=None)
|
| 305 |
parser.add_argument("--cem-knots", type=int, default=None)
|
| 306 |
+
parser.add_argument("--cem-w-goal", type=float, default=None)
|
| 307 |
+
parser.add_argument("--cem-w-path", type=float, default=None)
|
| 308 |
parser.add_argument("--cem-w-route", type=float, default=None)
|
| 309 |
+
parser.add_argument("--cem-w-lookahead", type=float, default=None)
|
| 310 |
+
parser.add_argument("--cem-w-via", type=float, default=None)
|
| 311 |
+
parser.add_argument("--cem-route-horizon-distance", type=float, default=None)
|
| 312 |
parser.add_argument("--cem-w-heading-goal", type=float, default=None)
|
| 313 |
parser.add_argument("--cem-w-progress", type=float, default=None)
|
| 314 |
parser.add_argument("--cem-w-action", type=float, default=None)
|
| 315 |
parser.add_argument("--cem-w-smooth", type=float, default=None)
|
| 316 |
parser.add_argument("--cem-w-boundary", type=float, default=None)
|
| 317 |
+
parser.add_argument("--cem-boundary-margin", type=float, default=None)
|
| 318 |
parser.add_argument("--make-gifs", type=int, default=None)
|
| 319 |
parser.add_argument("--gif-stride", type=int, default=1)
|
| 320 |
parser.add_argument("--gif-duration-ms", type=int, default=55)
|
experiments/tables/README.md
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Tables
|
| 2 |
+
|
| 3 |
+
Paper-facing Markdown/CSV tables.
|
experiments/tdmpc2/README.md
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
-
# TD-MPC2 Dynamics
|
| 2 |
|
| 3 |
-
Task-oriented latent dynamics
|
| 4 |
|
| 5 |
-
This
|
|
|
|
| 1 |
+
# TD-MPC2 Dynamics
|
| 2 |
|
| 3 |
+
Task-oriented latent dynamics baseline under the shared clean-image boat drift benchmark.
|
| 4 |
|
| 5 |
+
This method uses compact latent dynamics, action-conditioned rollout, and auxiliary heads under the same data, optimizer, and evaluation protocol as FlowMo.
|
tests/test_experiment_framework.py
CHANGED
|
@@ -93,9 +93,6 @@ def test_paper_pipeline_uses_stage_specific_precision() -> None:
|
|
| 93 |
probe_results = "probe.json"
|
| 94 |
planning_episodes = None
|
| 95 |
max_steps = None
|
| 96 |
-
planner = None
|
| 97 |
-
planner_iterations = None
|
| 98 |
-
planner_lr = None
|
| 99 |
make_gifs = None
|
| 100 |
gif_stride = 1
|
| 101 |
gif_duration_ms = 55
|
|
|
|
| 93 |
probe_results = "probe.json"
|
| 94 |
planning_episodes = None
|
| 95 |
max_steps = None
|
|
|
|
|
|
|
|
|
|
| 96 |
make_gifs = None
|
| 97 |
gif_stride = 1
|
| 98 |
gif_duration_ms = 55
|