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  1. experiments/docs/EXPERIMENT_PROTOCOL.md +3 -1
  2. experiments/evaluate_image_planning.py +90 -84
  3. experiments/planet/README.md +3 -3
  4. experiments/planet/result/paper_training.json +43 -0
  5. experiments/planet/result/paper_training_trace.jsonl +100 -0
  6. experiments/planet/result/parameter_count.json +8 -0
  7. experiments/reports/paper_flowmo_latent_probes.json +396 -0
  8. experiments/reports/paper_planning/counterflow_triangle_uniform.json +0 -0
  9. experiments/reports/paper_planning/passive_to_active_triangle_uniform.json +0 -0
  10. experiments/reports/paper_planning/passive_to_active_twin_uniform.json +0 -0
  11. experiments/reports/paper_planning/reach_uniform_triangle_uniform.json +0 -0
  12. experiments/reports/paper_planning/station_keeping_triangle_uniform.json +0 -0
  13. experiments/reports/paper_planning/station_keeping_twin_uniform.json +0 -0
  14. experiments/reports/paper_planning/waypoint_square_triangle_gradient.json +0 -0
  15. experiments/reports/paper_planning/waypoint_square_twin_gradient.json +0 -0
  16. experiments/reports/paper_planning/waypoint_zigzag_triangle_gradient.json +0 -0
  17. experiments/reports/paper_planning/waypoint_zigzag_twin_gradient.json +0 -0
  18. experiments/reports/paper_prediction_seen_flow_diagnostic.json +1730 -0
  19. experiments/reports/paper_prediction_unseen_boat_params.json +1730 -0
  20. experiments/reports/paper_prediction_unseen_flow.json +1490 -0
  21. experiments/reports/paper_report.md +253 -0
  22. experiments/reports/paper_training_summary.json +174 -0
  23. experiments/run_paper_image_pipeline.py +31 -17
  24. experiments/tables/README.md +3 -0
  25. experiments/tdmpc2/README.md +3 -3
  26. tests/test_experiment_framework.py +0 -3
experiments/docs/EXPERIMENT_PROTOCOL.md CHANGED
@@ -125,6 +125,8 @@ planet
125
  tdmpc2
126
  ```
127
 
 
 
128
  Traditional non-WM controllers:
129
 
130
  ```text
@@ -204,7 +206,7 @@ python -m experiments.run_paper_image_pipeline --stages planning
204
  python -m experiments.run_paper_image_pipeline --stages report
205
  ```
206
 
207
- Run smoke tests:
208
 
209
  ```bash
210
  python -m pytest -q
 
125
  tdmpc2
126
  ```
127
 
128
+ All learned world models use the same route-aware CEM planner over their latent rollouts.
129
+
130
  Traditional non-WM controllers:
131
 
132
  ```text
 
206
  python -m experiments.run_paper_image_pipeline --stages report
207
  ```
208
 
209
+ Run tests:
210
 
211
  ```bash
212
  python -m pytest -q
experiments/evaluate_image_planning.py CHANGED
@@ -136,11 +136,53 @@ def sample_action_sequences(mean: torch.Tensor, std: torch.Tensor, population: i
136
  return samples
137
 
138
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139
  def learned_plan(
140
  model,
141
  image_history: deque,
142
  action_history: deque,
143
- goal: np.ndarray,
 
144
  active_action_dim: int,
145
  args,
146
  prev_action: np.ndarray,
@@ -159,9 +201,11 @@ def learned_plan(
159
  c = donor_context.to(device=device, dtype=torch.float32)
160
  z = z.detach()
161
  c = c.detach()
 
162
  goal_t = torch.as_tensor(goal, dtype=torch.float32, device=device).view(1, 2)
163
  with torch.no_grad(), autocast_context(device, args.precision):
164
  current_pos = decode_absolute(model.decoder(z)).float().detach()[..., :2]
 
165
  mean = warm_start_mean(
166
  previous_mean,
167
  args.cem_horizon,
@@ -173,19 +217,6 @@ def learned_plan(
173
  prev = torch.zeros((model.config.action_dim,), dtype=torch.float32, device=device)
174
  prev[:active_action_dim] = torch.as_tensor(prev_action, dtype=torch.float32, device=device)
175
  best_candidates = None
176
- if args.planner == "gradient":
177
- action, best_candidates, mean = gradient_plan(
178
- model,
179
- z,
180
- c,
181
- mean,
182
- goal_t,
183
- current_pos,
184
- prev,
185
- active_action_dim,
186
- args,
187
- )
188
- return action, best_candidates, mean
189
  with torch.no_grad():
190
  action, best_candidates, mean = cem_plan(
191
  model,
@@ -194,6 +225,7 @@ def learned_plan(
194
  mean,
195
  std,
196
  goal_t,
 
197
  current_pos,
198
  prev,
199
  active_action_dim,
@@ -206,6 +238,7 @@ def planning_cost(
206
  pred: torch.Tensor,
207
  samples: torch.Tensor,
208
  goal_t: torch.Tensor,
 
209
  current_pos: torch.Tensor,
210
  prev: torch.Tensor,
211
  active_action_dim: int,
@@ -214,10 +247,19 @@ def planning_cost(
214
  pos = pred[..., :2]
215
  goal_delta = goal_t - current_pos
216
  goal_dir = goal_delta / torch.linalg.norm(goal_delta, dim=-1, keepdim=True).clamp_min(1.0e-6)
217
- progress = ((pos - current_pos[:, None]) * goal_dir[:, None]).sum(dim=-1).amax(dim=-1)
218
  alpha = torch.linspace(1.0 / pos.shape[1], 1.0, pos.shape[1], device=pos.device, dtype=pos.dtype)
219
- route = current_pos[:, None] + alpha.view(1, -1, 1) * goal_delta[:, None]
220
- route_error = ((pos - route) ** 2).sum(dim=-1).mean(dim=-1)
 
 
 
 
 
 
 
 
 
221
  goal_from_pos = goal_t[:, None] - pos
222
  goal_from_pos = goal_from_pos / torch.linalg.norm(goal_from_pos, dim=-1, keepdim=True).clamp_min(1.0e-6)
223
  heading = pred[..., 2:4]
@@ -225,24 +267,28 @@ def planning_cost(
225
  heading_error = (1.0 - (heading * goal_from_pos).sum(dim=-1)).mean(dim=-1)
226
  terminal = ((pos[:, -1] - goal_t) ** 2).sum(dim=-1)
227
  path = ((pos - goal_t[:, None]) ** 2).sum(dim=-1).mean(dim=-1)
 
228
  energy = (samples[..., :active_action_dim] ** 2).mean(dim=(1, 2))
229
  smooth_prev = torch.cat([prev.view(1, 1, -1).repeat(samples.shape[0], 1, 1), samples[:, :-1]], dim=1)
230
  smooth = ((samples - smooth_prev) ** 2).mean(dim=(1, 2))
 
231
  boundary = (
232
- torch.relu(-pos[..., 0])
233
- + torch.relu(pos[..., 0] - 10.0)
234
- + torch.relu(-pos[..., 1])
235
- + torch.relu(pos[..., 1] - 10.0)
236
  ).mean(dim=-1)
237
  return (
238
  args.cem_w_goal * terminal
239
  + args.cem_w_path * path
240
- + args.cem_w_route * route_error
 
 
241
  + args.cem_w_heading_goal * heading_error
242
  + args.cem_w_action * energy
243
  + args.cem_w_smooth * smooth
244
  + args.cem_w_boundary * boundary
245
- - args.cem_w_progress * progress
246
  )
247
 
248
 
@@ -253,6 +299,7 @@ def cem_plan(
253
  mean: torch.Tensor,
254
  std: torch.Tensor,
255
  goal_t: torch.Tensor,
 
256
  current_pos: torch.Tensor,
257
  prev: torch.Tensor,
258
  active_action_dim: int,
@@ -267,7 +314,7 @@ def cem_plan(
267
  samples[:, :, active_action_dim:] = 0.0
268
  with autocast_context(mean.device, args.precision):
269
  pred = rollout_latent(model, z, c, samples)
270
- cost = planning_cost(pred, samples, goal_t, current_pos, prev, active_action_dim, args)
271
  elite_idx = torch.topk(cost, k=args.cem_elites, largest=False).indices
272
  elites = samples[elite_idx]
273
  mean = elites.mean(dim=0)
@@ -283,49 +330,6 @@ def cem_plan(
283
  )
284
 
285
 
286
- def gradient_plan(
287
- model,
288
- z: torch.Tensor,
289
- c: torch.Tensor,
290
- mean: torch.Tensor,
291
- goal_t: torch.Tensor,
292
- current_pos: torch.Tensor,
293
- prev: torch.Tensor,
294
- active_action_dim: int,
295
- args,
296
- ) -> tuple[np.ndarray, np.ndarray | None, np.ndarray]:
297
- init = mean.clamp(-0.95, 0.95)
298
- logits = torch.atanh(init).detach().requires_grad_(True)
299
- optimizer = torch.optim.Adam([logits], lr=args.planner_lr)
300
- inactive = None
301
- if active_action_dim < mean.shape[-1]:
302
- inactive = torch.zeros_like(mean)
303
- inactive[:, :active_action_dim] = 1.0
304
- for _ in range(args.planner_iterations):
305
- seq = torch.tanh(logits)
306
- if inactive is not None:
307
- seq = seq * inactive
308
- with autocast_context(mean.device, args.precision):
309
- pred = rollout_latent(model, z, c, seq.unsqueeze(0))
310
- loss = planning_cost(pred, seq.unsqueeze(0), goal_t, current_pos, prev, active_action_dim, args).mean()
311
- optimizer.zero_grad(set_to_none=True)
312
- loss.backward()
313
- optimizer.step()
314
- with torch.no_grad():
315
- seq = torch.tanh(logits)
316
- if inactive is not None:
317
- seq = seq * inactive
318
- with autocast_context(mean.device, args.precision):
319
- pred = rollout_latent(model, z, c, seq.unsqueeze(0))
320
- candidates = pred[0, :, :2].detach().cpu().numpy()[None, ...] if args.make_gifs else None
321
- action = seq[0, :active_action_dim].detach().cpu().numpy()
322
- return (
323
- np.clip(action, -1.0, 1.0).astype(np.float32),
324
- candidates,
325
- seq.detach().cpu().numpy(),
326
- )
327
-
328
-
329
  @torch.no_grad()
330
  def donor_context_for_flowmo(model, env: SurfaceBoatEnv, args, seed: int) -> torch.Tensor | None:
331
  if not hasattr(model, "to_c"):
@@ -430,7 +434,8 @@ def evaluate_one_method(method: str, args) -> dict:
430
  model,
431
  image_history,
432
  action_history,
433
- goal,
 
434
  env.action_dim,
435
  args,
436
  prev_action,
@@ -531,29 +536,30 @@ def main() -> None:
531
  parser.add_argument("--boat", choices=["twin", "triangle"], default="twin")
532
  parser.add_argument("--flow-type", choices=["uniform", "slowly_varying", "vortex_center", "gradient", "turbulent_patch"], default="uniform")
533
  parser.add_argument("--episodes", type=int, default=50)
534
- parser.add_argument("--max-steps", type=int, default=240)
535
- parser.add_argument("--planner", choices=["gradient", "cem"], default="gradient")
536
  parser.add_argument("--passive-steps", type=int, default=25)
537
  parser.add_argument("--history-len", type=int, default=32)
538
  parser.add_argument("--image-size", type=int, default=160)
539
  parser.add_argument("--visual-scale", type=float, default=2.5)
540
- parser.add_argument("--checkpoint-name", default="image_local.pt")
541
  parser.add_argument("--context-modes", nargs="+", default=["inferred", "zero", "shuffled"])
542
- parser.add_argument("--cem-horizon", type=int, default=15)
543
- parser.add_argument("--cem-population", type=int, default=128)
544
- parser.add_argument("--cem-elites", type=int, default=16)
545
- parser.add_argument("--cem-iterations", type=int, default=3)
546
  parser.add_argument("--cem-action-std", type=float, default=0.5)
547
- parser.add_argument("--cem-knots", type=int, default=5)
548
- parser.add_argument("--planner-iterations", type=int, default=30)
549
- parser.add_argument("--planner-lr", type=float, default=0.08)
550
- parser.add_argument("--cem-w-goal", type=float, default=8.0)
551
- parser.add_argument("--cem-w-path", type=float, default=0.45)
552
- parser.add_argument("--cem-w-route", type=float, default=1.0)
 
553
  parser.add_argument("--cem-w-heading-goal", type=float, default=0.0)
554
- parser.add_argument("--cem-w-action", type=float, default=0.04)
555
  parser.add_argument("--cem-w-smooth", type=float, default=0.08)
556
- parser.add_argument("--cem-w-boundary", type=float, default=10.0)
 
557
  parser.add_argument("--cem-w-progress", type=float, default=2.0)
558
  parser.add_argument("--success-radius", type=float, default=0.65)
559
  parser.add_argument("--make-gifs", type=int, default=3)
@@ -562,7 +568,7 @@ def main() -> None:
562
  parser.add_argument("--seed", type=int, default=33)
563
  parser.add_argument("--device", default="cuda")
564
  parser.add_argument("--precision", choices=["fp32", "bf16", "fp16"], default="fp32")
565
- parser.add_argument("--out", default="experiments/reports/image_planning")
566
  args = parser.parse_args()
567
 
568
  out_dir = Path(args.out)
 
136
  return samples
137
 
138
 
139
+ def route_points_tensor(
140
+ current_pos: torch.Tensor,
141
+ goals: np.ndarray,
142
+ goal_idx: int,
143
+ ) -> torch.Tensor:
144
+ remaining = torch.as_tensor(goals[goal_idx:], dtype=torch.float32, device=current_pos.device)
145
+ return torch.cat([current_pos.reshape(1, 2).detach(), remaining], dim=0)
146
+
147
+
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:]
151
+ seg = ends - starts
152
+ seg_len = torch.linalg.norm(seg, dim=-1).clamp_min(1.0e-6)
153
+ seg_len_sq = (seg_len * seg_len).clamp_min(1.0e-6)
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)
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)
162
+ route_s = along.gather(dim=-1, index=idx[..., None]).squeeze(-1)
163
+ return min_dist_sq, route_s, seg_len.sum()
164
+
165
+
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:]
169
+ seg = ends - starts
170
+ seg_len = torch.linalg.norm(seg, dim=-1).clamp_min(1.0e-6)
171
+ cum_end = seg_len.cumsum(dim=0)
172
+ cum_start = cum_end - seg_len
173
+ flat_s = s.reshape(-1).clamp(0.0, float(cum_end[-1].detach().cpu()))
174
+ idx = torch.searchsorted(cum_end, flat_s, right=False).clamp(max=seg_len.numel() - 1)
175
+ local = ((flat_s - cum_start[idx]) / seg_len[idx]).clamp(0.0, 1.0)
176
+ pts = starts[idx] + local[:, None] * seg[idx]
177
+ return pts.reshape(*s.shape, 2)
178
+
179
+
180
  def learned_plan(
181
  model,
182
  image_history: deque,
183
  action_history: deque,
184
+ goals: np.ndarray,
185
+ goal_idx: int,
186
  active_action_dim: int,
187
  args,
188
  prev_action: np.ndarray,
 
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,
 
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)
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+ parser.add_argument("--cem-w-lookahead", type=float, default=2.0)
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+ parser.add_argument("--cem-w-via", type=float, default=2.0)
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+ parser.add_argument("--cem-route-horizon-distance", type=float, default=3.0)
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  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)
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  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
- # RSSM Adapter
2
 
3
- Recurrent state-space world model with CEM planning, adapted to the boat drift benchmark.
4
 
5
- This baseline keeps the world-model component that is directly comparable for our setting: image encoder, recurrent latent dynamics, action-conditioned rollout, and decoded pose prediction.
 
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.
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+ ]
experiments/reports/paper_report.md ADDED
@@ -0,0 +1,253 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ "paper_step_004000.pt",
20
+ "paper_step_006000.pt",
21
+ "paper_step_008000.pt",
22
+ "paper_step_010000.pt",
23
+ "paper_step_012000.pt",
24
+ "paper_step_014000.pt",
25
+ "paper_step_016000.pt",
26
+ "paper_step_018000.pt",
27
+ "paper_step_020000.pt"
28
+ ],
29
+ "checkpoint_interval": 2000,
30
+ "prediction": {
31
+ "pos1": 0.07179122391001631,
32
+ "heading1": 0.045128354569897056,
33
+ "pos3": 0.07811223280926545,
34
+ "heading3": 0.04785821299689511,
35
+ "pos6": 0.08915455282355349,
36
+ "heading6": 0.05167978972895071,
37
+ "pos8": 0.09605406736955047,
38
+ "heading8": 0.054166459323217474,
39
+ "pos10": 0.10336457837062578,
40
+ "heading10": 0.05640091137805333,
41
+ "pos20": 0.1460142444508771,
42
+ "heading20": 0.06746077448284875
43
+ }
44
+ },
45
+ {
46
+ "method": "leworldmodel",
47
+ "steps": 20000,
48
+ "batch_size": 256,
49
+ "train_samples": 5120000,
50
+ "final_train_loss": 0.018198927864432335,
51
+ "total_parameters": 664612,
52
+ "target_mode": "absolute_normalized",
53
+ "position_scale": 5.0,
54
+ "heading_weight": 2.0,
55
+ "current_pose_weight": 1.0,
56
+ "motion_weight": 0.5,
57
+ "precision": "bf16",
58
+ "checkpoint_name": "paper.pt",
59
+ "final_checkpoint": "paper.pt",
60
+ "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
+ "checkpoint_interval": 2000,
73
+ "prediction": {
74
+ "pos1": 0.10107660254773994,
75
+ "heading1": 0.0479962204505379,
76
+ "pos3": 0.11733688049328823,
77
+ "heading3": 0.054719595277371504,
78
+ "pos6": 0.14297669551645717,
79
+ "heading6": 0.0649993756475548,
80
+ "pos8": 0.1607180543554326,
81
+ "heading8": 0.07113518511566023,
82
+ "pos10": 0.1783415130339563,
83
+ "heading10": 0.07669965278667708,
84
+ "pos20": 0.25807287978629273,
85
+ "heading20": 0.0995667139844348
86
+ }
87
+ },
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
+ "pos3": 0.12147162947803736,
120
+ "heading3": 0.05107929309209188,
121
+ "pos6": 0.13511362741701305,
122
+ "heading6": 0.05543786101043224,
123
+ "pos8": 0.14701085817068815,
124
+ "heading8": 0.05840683872035394,
125
+ "pos10": 0.16024279454723,
126
+ "heading10": 0.061416062798040606,
127
+ "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
+ "pos3": 0.10856497256706159,
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(args.cem_action_std if args.cem_action_std is not None else plan.get("cem_action_std", 0.5)),
186
  "--cem-knots",
187
- str(args.cem_knots if args.cem_knots is not None else plan.get("cem_knots", 5)),
 
 
 
 
188
  "--cem-w-route",
189
- str(args.cem_w_route if args.cem_w_route is not None else plan.get("cem_w_route", 1.0)),
 
 
 
 
 
 
190
  "--cem-w-heading-goal",
191
- str(args.cem_w_heading_goal if args.cem_w_heading_goal is not None else plan.get("cem_w_heading_goal", 0.0)),
192
  "--cem-w-progress",
193
- str(args.cem_w_progress if args.cem_w_progress is not None else plan.get("cem_w_progress", 2.0)),
194
  "--cem-w-action",
195
- str(args.cem_w_action if args.cem_w_action is not None else plan.get("cem_w_action", 0.04)),
196
  "--cem-w-smooth",
197
- str(args.cem_w_smooth if args.cem_w_smooth is not None else plan.get("cem_w_smooth", 0.08)),
198
  "--cem-w-boundary",
199
- str(args.cem_w_boundary if args.cem_w_boundary is not None else plan.get("cem_w_boundary", 10.0)),
 
 
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 Adapter
2
 
3
- Task-oriented latent dynamics model adapted to the boat drift benchmark.
4
 
5
- This baseline keeps compact latent dynamics, action-conditioned rollout, and auxiliary heads sized to match the parameter budget.
 
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