UPDATE: could stablize the board height

logs/run-20250923_060650-u2cujbdh/checkpoint_step_212000_20250923_083937.pth

@@ -0,0 +1,3 @@
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+size 1788985

logs/run-20250923_060650-u2cujbdh/config.yaml

@@ -0,0 +1,243 @@
+_wandb:
+    value:
+        cli_version: 0.21.0
+        e:
+            thakdtmov0ddmmc4ymua4w9y4wntoxj3:
+                args:
+                    - --config
+                    - ICLR/config/bb2/bb2_switching_dim.yaml
+                    - --gpu_id
+                    - "7"
+                codePath: main_cheetah.py
+                codePathLocal: main_cheetah.py
+                cpu_count: 128
+                cpu_count_logical: 255
+                cudaVersion: "12.6"
+                disk:
+                    /:
+                        total: "6598647398400"
+                        used: "2870406983680"
+                email: sangliteng@gmail.com
+                executable: /home/sangliteng/miniconda3/envs/learning-hybrid-systems/bin/python3
+                git:
+                    commit: e65e9632c7a9d9bc1847e0a5a83e8e29db0ac56e
+                    remote: git@github.com:SangliTeng/Leaning-Hybrid-Systems.git
+                gpu: NVIDIA RTX 6000 Ada Generation
+                gpu_count: 8
+                gpu_nvidia:
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-45d30378-435b-de16-3aea-9fc48527fe61
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-19a03a90-a9e0-a194-8d43-c2dcb7925140
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-ea5b1c7d-baf5-6bcb-1ce1-0ee9ca4b5c8f
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-b1a2e98c-e563-a0fe-47ce-cfa29028d5c7
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-208eeaba-0174-d4e0-bc7a-2eb5f7983a6e
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-81a0e787-8873-418d-6ff3-e3f59deb75a0
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-8619099e-16b4-d667-b97f-518c3954df8c
+                    - architecture: Ada
+                      cudaCores: 18176
+                      memoryTotal: "51527024640"
+                      name: NVIDIA RTX 6000 Ada Generation
+                      uuid: GPU-8042fac2-fd28-c8e9-668b-ceb33605fb49
+                host: hr-6000ada
+                memory:
+                    total: "811164614656"
+                os: Linux-5.15.0-143-generic-x86_64-with-glibc2.35
+                program: /home/sangliteng/Research/Leaning-Hybrid-Systems/main_cheetah.py
+                python: CPython 3.12.11
+                root: ./ICLR/bb2
+                startedAt: "2025-09-23T06:06:50.598482Z"
+                writerId: thakdtmov0ddmmc4ymua4w9y4wntoxj3
+        m: []
+        python_version: 3.12.11
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 2
+                - 13
+                - 15
+                - 16
+            "4": 3.12.11
+            "5": 0.21.0
+            "12": 0.21.0
+            "13": linux-x86_64
+anti_collapse_weight:
+    value: 1000
+batch_size:
+    value: 4096
+data_path_test:
+    value: None
+data_path_train:
+    value: /home/sangliteng/Research/DynaTraj/dataset/bb/bb2.npz
+decoder_batch_size:
+    value: 131072
+decoder_finetune_steps:
+    value: 200000
+decoder_lr:
+    value: 0.001
+default_activation:
+    value: ReLU
+dim_linear_in_decoder:
+    value:
+        - 0
+        - 0
+dim_linear_in_encoder:
+    value:
+        - 0
+        - 0
+dim_linear_in_vec_field:
+    value:
+        - 0
+        - 0
+dim_linear_out_decoder:
+    value: 0
+dim_linear_out_encoder:
+    value: 0
+dim_linear_out_vec_field:
+    value: 0
+dynamics_init_scale:
+    value: 0.005
+dynamics_loss_type:
+    value: l2
+dynamics_weight:
+    value: 10
+encoder_lr:
+    value: 0.001
+eval_batch_size:
+    value: 64
+eval_every:
+    value: 2.5e+22
+eval_trajectory_length:
+    value: 500
+except_features:
+    value: []
+external_input_dim:
+    value: 1
+hidden_dim_linear_decoder:
+    value: []
+hidden_dim_linear_encoder:
+    value: []
+hidden_dim_linear_vec_field:
+    value: []
+hidden_dims_dec:
+    value:
+        - 128
+        - 128
+        - 128
+        - 128
+        - 128
+        - 128
+        - 128
+        - 128
+hidden_dims_enc:
+    value:
+        - 64
+        - 64
+        - 64
+hidden_dims_vector_field:
+    value:
+        - 128
+        - 128
+input_dim:
+    value: 4
+is_lagrangian_system:
+    value: true
+isometry_loss_weight:
+    value: 0.1
+latent_dim:
+    value: 8
+learning_rate:
+    value: 0.0005
+log_interval:
+    value: 50
+loss_mode:
+    value: z
+max_iso_samples:
+    value: 16384
+min_covariance_threshold:
+    value: 0.09
+model_type:
+    value: hybrid
+normalize_data:
+    value: false
+ode_method:
+    value: rk4
+project_name:
+    value: debug architecture
+reconstruction_loss_type:
+    value: l2
+run_name:
+    value: bb2 - short horizon
+save_checkpoint_every:
+    value: 250
+smooth_budget:
+    value: 0.0001
+smooth_weight:
+    value: 0
+steps_per_length:
+    value: 2000
+switching_dim:
+    value:
+        - 0
+        - 1
+switching_threshold_scale:
+    value: 1.1
+switching_weight_multiplier:
+    value: 2
+test_info:
+    value: 0.1 iso loss weight
+time_step:
+    value: 0.01
+train_test_ratio:
+    value: 0.95
+trajectory_lengths:
+    value:
+        - 10
+        - 20
+        - 40
+        - 80
+        - 100
+        - 100
+use_switching_weights:
+    value: true
+use_weight_smoothing:
+    value: false
+vector_field_lr:
+    value: 0.001
+viz_interval:
+    value: 50
+wandb_base_dir:
+    value: ./ICLR/bb2
+weight_smoothing_window:
+    value: 0
+z_continuity_weight:
+    value: 10

mppi/task/bb1d_track.py

@@ -177,6 +177,7 @@ class NeuralHybridDynamics:
 
         t_eval = out.get('t_eval', t_batch)        # [B, Te] or [Te]
         x_traj = out['x_trajectory']               # [B, Te, Dx]
+        z_traj = out['z_trajectory']               # TODO
 
         # Interpolate from t_eval to our target sampling times
         x_pred = interpolate_trajectory(t_eval, t_batch - t_batch[:, :1], x_traj)  # [B, H, Dx]
@@ -196,7 +197,7 @@ class BBTrack:
         # Objective
         self.omega = 2
         self.amplitude = 1
-        self.offset = 0.5
+        self.offset = 0.8
         np.random.seed(self.parser.seed)
 
         print("\n=======Loading Environment=======")
@@ -230,7 +231,7 @@ class BBTrack:
         parser.add_argument("--realtime", action="store_true", help="Match simulation speed to real time")
         parser.add_argument("--seed", type=int, default=42, help="Random seed")
         parser.add_argument("--headless", action="store_true", help="Run in headless mode (no rendering)")
-        parser.add_argument("--weights_dir", type=str, default="/home/lau/sim/DynaTraj/logs/run-20250922_190038-msikxorg", help="absolute path to the weights directory")
+        parser.add_argument("--weights_dir", type=str, default="/home/lau/sim/DynaTraj/logs/run-20250923_060650-u2cujbdh", help="absolute path to the weights directory")
         
         return parser.parse_args()
 
@@ -287,7 +288,8 @@ class BBTrack:
         # print("target_z:",target_z[0,0])
         
         # Tracking cost - quadratic penalty for deviation from target
-        pos_cost = (ball_pos - target_z) ** 2 + (board_pos - target_z) ** 2 *100
+        # pos_cost = (ball_pos - target_z) ** 2
+        pos_cost = (board_pos - target_z) ** 2 *100 # TODO
 
         
         # Control cost - penalize large board velocities (action is 6D: vx,vy,vz,wx,wy,wz)
@@ -322,7 +324,8 @@ class BBTrack:
                 else:
                     obs_tensor = obs
 
-                print("GT:zt:",obs_tensor[0,0])
+                # print("GT:zt:",obs_tensor[0,0])
+                print("Board GT:zt:",obs_tensor[0,2]) # TODO
                 target_z = torch.abs(torch.sin(self.omega * torch.tensor(self.current_simulation_time,device=self.device))) * self.amplitude + self.offset  # [horizon]
                 # print("target_z:",target_z)