Dataset - BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion

Dataset - BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion Dataset Content dataset_root/├─ rosbag_agile_motion/│ ├─ C1970_tkd_skill_clip1.mcap│ ├─ C1975_side_flip.mcap│ ├─ C1980_double_high_kick.mcap│ └─ C1985_merge2.mcap # side_flip + tkd_skill│├─ rosbag_walk_and_run/│ ├─ walk_rosbag│ └─ run_rosbag│└─ rosbag_ablation/ ├─ 2ms_rosbag2_2025_10_22-03_50_34 ├─ 5ms_rosbag2_2025_10_22-03_41_01 ├─ 10ms_rosbag2_2025_10_22-03_36_03 ├─ arma0_rosbag2_2025_10_22-00_29_58 ├─ arma0.1_rosbag2_2025_10_22-00_44_01 ├─ arma10_rosbag2_2025_10_22-00_48_58 ├─ asapgain_rosbag2_2025_10_22-01_03_25 ├─ axisangle_rosbag2_2025_10_22-01_24_25 ├─ hist4_rosbag2_2025_10_22-07_00_11 ├─ hist8_rosbag2_2025_10_22-07_03_51 ├─ hist25_rosbag2_2025_10_22-07_06_58 ├─ origin_rosbag2_2025_10_22-01_35_46 ├─ quat_rosbag2_2025_10_22-01_17_38 ├─ wn5_rosbag2_2025_10_22-00_54_05 └─ wn25_rosbag2_2025_10_22-00_57_52 1) rosbag_agile_motion/ Agile acrobatic clips recorded on the humanoid: C1970_tkd_skill_clip1.mcap — Taekwondo skill clip. C1975_side_flip.mcap — Side-flip sequence. C1980_double_high_kick.mcap — Double high-kick sequence. C1985_merge2.mcap — Composite sequence (side_flip + tkd_skill). 2) rosbag_walk_and_run/ walk_rosbag — Walking trials. run_rosbag — Running/sprinting trials. 3) rosbag_ablation/ Ablation study recordings: Prefix Meaning origin Baseline (Our setting) hist4/8/25 Observation history length = 4 / 8 / 25 2ms/5ms/10ms Execution delay = 2 / 5 / 10 ms arma0/0.1/10 Armature coeff wn5/wn25/asapgain PD Gain: natural frequency = 5 / 25 / same as ASAP's quat/axisangle Orientation represented as quaternion / axis-angle Code for Plot 1. IMU data plot Directory: ./base_imu/ We first use plotjuggler to export the .csv file from .mcap bag python export_imu_csv.py rosbag_data_raw.csvpython plot_imu_csv.py 2. Plot local motion tracking err Directory: ./ablation/ 2.1. Plot raw joint position vs ref motion joint position data python plot_joint_raw.py Please change the `BAG` path in the code. 2.2. Batch run for local errPlot local motion tracking err for all the bags under `BASE_DIR`. Please change the `BASE_DIR` in the code. python batch_local.py The results will be stored at the path of the dataset. 3. Plot global motion tracking err 3.1. Batch run for global errPlot global motion tracking err for all the bags under `BASE_DIR`. Please change the `BASE_DIR` in the code. python batch_global.py Visualize global err of all the experiments: Use Chrome open: `mocap_error_gallery_local_folder_viewer.html` 4. Plot overall err Compare local and global err among selected experiments. Example:python compare_err.py --base "/media/nv/TSD302/rosbag_ablation" --exp origin hist4 hist8 hist25 Batch run for overall errCompare the global and local motion tracking err among experiments. Please change the `BASE_DIR` in the code. python batch_all_err.py 5. Adaptive Sampling Plot 5.1. Plot Adaptive Sampling Failure Hatmap python plot_failure_heatmap.py --dir ./w \ --out_dir ./w_out \ --bins 200 \ --thresh 0.05 \ --dpi 180 \ --pattern "*_ckpt_failure_*.npy" --save_csv 5.2. Plot Adaptive Sampling Prob over Timepython plot_sampling_prob.py sampling_prob_over_time.pkl 6. GRF Plot 6.1. Walking Human ref:python grf_walk_ref.pyRobot real:python run_bag.py --csv walk_bag2.csv --out walk_bag2.png 6.2. Running Human ref:python grf_run_ref.py RBDS028runT25forces.txt --save ref_run_grf.pngRobot real:python run_bag.py --csv run_bag.csv --out run_bag.png