ecot
Datasets
All datasets matching “ecot”libero-wo-ecotThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "panda",
"total_episodes": 3917,
"total_frames": 567494,
"total_tasks": 73,
"total_videos": 0,
"total_chunks": 4,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:3917"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/uclanecl/libero-wo-ecot.ECOT-Alignment-900-Episodes
ECOT Alignment 900 Episodes
This dataset contains 900 rollout episodes generated by the original MiniVLA
policy across all 90 LIBERO-90 tasks (10 distinct initial configurations per
task). It was collected for supervised policy/reasoning alignment experiments.
Successful and failed episodes are both included.
Splits and counts
Split
Episodes
Policy queries
Training
720
13,093
Validation
180
3,229
Total
900
16,322
The split is task-stratified:… See the full description on the dataset page: https://huggingface.co/datasets/yyshi0619/ECOT-Alignment-900-Episodes.libero10-dense-object-mask-ecot
LIBERO-10 Object Masks + ECoT Reasoning Traces
Object-mask annotations and Embodied Chain-of-Thought (ECoT) reasoning traces for
the 379 demonstrations of the LIBERO-10 (libero_10_image) benchmark.
Generated for the CoT-VLA project. Per-object segmentation masks were produced
with interactive SAM2 point/box prompts and bidirectional video propagation; the CoT
reasoning traces were hand-refined per task and re-timed to each episode's actuator
(gripper + motion) signal.
Source… See the full description on the dataset page: https://huggingface.co/datasets/mwnuk/libero10-dense-object-mask-ecot.emotiv-ecot
emotiv-ecot
Embodied Chain-of-Thought episodes where the body is a human cortex: one person in an EMOTIV EPOC X talking to an agent that reads a one-line brain summary before every reply. Each turn becomes a LeRobot v3.0 episode (Zawalski et al. 2024 with the robot body swapped for a head): the brain is observation and reward (Δstress, Δengagement across the reply), the agent's speech is the action, the reasoning is the per-frame TASK | AMBIENT | PLAN | TOOL | ACT | REWARD… See the full description on the dataset page: https://huggingface.co/datasets/cagataydev/emotiv-ecot.ecot_liberoThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "panda",
"total_episodes": 3917,
"total_frames": 567494,
"total_tasks": 73,
"total_videos": 0,
"total_chunks": 4,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:3917"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/uclanecl/ecot_libero.ecotopia-citizens-data
Ecotopia Citizens Data
Training dataset for the Ecotopia citizen dialogue generation model. Contains citizen profiles and contextual reactions to mayor policies.
Dataset Details
Size: 340 examples (272 train / 68 validation)
Format: Conversational (system/user/assistant messages)
Task: Generate realistic citizen dialogue based on demographic profiles and policy context
Links
Citizens Model
GitHub Repo