add real robot dataset converters and sample CSVs

4 datasets: ALOHA (14-DOF), Push-T real (8-DOF), Franka Panda (13-DOF),
Unitree H1 humanoid (40-DOF). Space now has dataset picker with one-click
CSV download in the upload tab.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app.py

@@ -379,7 +379,7 @@ def run_upload(file):
     return run_analysis(policy_data)
 
 
-# ── CSV template download ──────────────────────────────────────────────────────
+# ── CSV template + sample downloads ───────────────────────────────────────────
 def make_template():
     rows = []
     for ep in range(3):
@@ -395,6 +395,46 @@ def make_template():
     df.to_csv(path, index=False)
     return path
 
+SAMPLE_DATASETS = {
+    "ALOHA bimanual — cup opening (14-DOF)":
+        ("lerobot/aloha_static_cups_open",     "observation.state", "action", 20),
+    "Push-T real robot — tabletop push (8-DOF)":
+        ("lerobot/columbia_cairlab_pusht_real", "observation.state", "action", 20),
+    "Franka Panda — free-play manipulation (13-DOF)":
+        ("lerobot/nyu_franka_play_dataset",     "observation.state", "action", 20),
+    "Unitree H1 humanoid — warehouse (19-DOF / 40-DOF action)":
+        ("lerobot/unitreeh1_warehouse",         "observation.state", "action", 12),
+}
+
+def download_sample(choice, progress=gr.Progress()):
+    if not choice:
+        return None
+    progress(0.1, desc=f"Loading {choice}…")
+    hf, sc, ac, max_eps = SAMPLE_DATASETS[choice]
+    ds  = load_dataset(hf, split="train")
+    df_raw = ds.to_pandas()
+    ep_ids = sorted(df_raw["episode_index"].unique())[:max_eps]
+
+    rows = []
+    policy_name = choice.split("—")[0].strip()
+    progress(0.4, desc="Extracting episodes…")
+    for ei in ep_ids:
+        grp     = df_raw[df_raw["episode_index"] == ei].reset_index(drop=True)
+        success = int(grp["next.reward"].max() > 0) if "next.reward" in grp.columns else 1
+        states  = np.vstack(grp[sc].values)
+        actions = np.vstack(grp[ac].values)
+        for fi, (s, a) in enumerate(zip(states, actions)):
+            row = {"episode_id": int(ei), "policy_name": policy_name,
+                   "frame_id": fi, "success": success}
+            for i, v in enumerate(s):  row[f"state_{i}"]  = round(float(v), 6)
+            for i, v in enumerate(a):  row[f"action_{i}"] = round(float(v), 6)
+            rows.append(row)
+
+    path = f"/tmp/sample_{hf.split('/')[-1]}.csv"
+    pd.DataFrame(rows).to_csv(path, index=False)
+    progress(1.0, desc="Ready!")
+    return path
+
 
 # ── UI ────────────────────────────────────────────────────────────────────────
 CSS = f"""
@@ -501,15 +541,40 @@ def build_ui():
 
             # ── TAB 2: UPLOAD ─────────────────────────────────────────────────
             with gr.Tab("📂 Upload Your Own Data"):
+                gr.Markdown("### Try a real dataset — or upload your own rollouts")
                 with gr.Row():
                     with gr.Column(scale=2):
-                        gr.Markdown(FORMAT_HINT)
+                        gr.Markdown("""
+**Step 1 — pick a real robot dataset to download as a ready-to-use CSV:**
+
+| Dataset | Robot | DOF | Task |
+|---|---|---|---|
+| ALOHA bimanual | Stanford ALOHA (2× ViperX) | 14 | Cup opening |
+| Push-T real | Columbia delta robot | 8 | Push block to goal |
+| Franka Panda | NYU Franka Emika Panda | 13 | Free-play manipulation |
+| Unitree H1 | Full-size humanoid | 19 state / 40 action | Warehouse pick-place |
+
+Then upload the downloaded CSV below and hit **Analyse**.
+                        """)
                     with gr.Column(scale=1):
-                        tmpl_btn  = gr.Button("⬇ Download Template CSV")
+                        sample_picker = gr.Dropdown(
+                            choices=list(SAMPLE_DATASETS.keys()),
+                            label="Real robot dataset",
+                            value=None,
+                        )
+                        dl_btn   = gr.Button("⬇ Download as CSV", variant="secondary")
+                        dl_file  = gr.File(label="Downloaded CSV (upload below ↓)")
+                        dl_btn.click(fn=download_sample, inputs=[sample_picker], outputs=[dl_file])
+
+                        gr.Markdown("---")
+                        tmpl_btn  = gr.Button("⬇ Blank template CSV", variant="secondary")
                         tmpl_file = gr.File(label="Template")
                         tmpl_btn.click(fn=make_template, outputs=tmpl_file)
 
-                upload = gr.File(label="Upload rollout CSV", file_types=[".csv"])
+                with gr.Accordion("CSV format reference", open=False):
+                    gr.Markdown(FORMAT_HINT)
+
+                upload = gr.File(label="⬆ Upload rollout CSV (your own or downloaded above)", file_types=[".csv"])
                 run_upload_btn = gr.Button("▶ Analyse Uploaded Data", variant="primary", size="lg")
 
                 with gr.Row():

convert_to_eval_csv.py

@@ -0,0 +1,180 @@
+"""
+Converters: real robot datasets → eval harness CSV format.
+
+Usage:
+    python convert_to_eval_csv.py --dataset pusht    --out pusht_eval.csv
+    python convert_to_eval_csv.py --dataset franka   --out franka_eval.csv
+    python convert_to_eval_csv.py --dataset humanoid --out humanoid_eval.csv
+    python convert_to_eval_csv.py --dataset aloha    --out aloha_eval.csv
+
+Output CSV columns:
+    episode_id, policy_name, frame_id, timestamp,
+    state_0 ... state_N, action_0 ... action_N, success
+"""
+
+import argparse
+import numpy as np
+import pandas as pd
+from datasets import load_dataset
+
+# ── helpers ───────────────────────────────────────────────────────────────────
+
+def episode_success(group, reward_col="next.reward", done_col="next.done"):
+    """Infer episode success from reward signal or done flag."""
+    if reward_col in group.columns:
+        return int(group[reward_col].max() > 0)
+    # fallback: episode completed normally = success
+    return int(group[done_col].iloc[-1]) if done_col in group.columns else 1
+
+
+def to_eval_csv(hf_dataset_name, policy_name, state_col, action_col,
+                max_episodes=None, out_path=None):
+    print(f"Loading {hf_dataset_name} …")
+    ds = load_dataset(hf_dataset_name, split="train")
+    df = ds.to_pandas()
+
+    ep_ids = sorted(df["episode_index"].unique())
+    if max_episodes:
+        ep_ids = ep_ids[:max_episodes]
+
+    rows = []
+    for ei in ep_ids:
+        grp = df[df["episode_index"] == ei].reset_index(drop=True)
+        success = episode_success(grp)
+
+        states  = np.vstack(grp[state_col].values)
+        actions = np.vstack(grp[action_col].values) if action_col in grp.columns else states
+
+        for fi, (s, a) in enumerate(zip(states, actions)):
+            row = {
+                "episode_id":  int(ei),
+                "policy_name": policy_name,
+                "frame_id":    fi,
+                "timestamp":   round(grp["timestamp"].iloc[fi], 4) if "timestamp" in grp.columns else fi,
+                "success":     success,
+            }
+            for i, v in enumerate(s):
+                row[f"state_{i}"] = round(float(v), 6)
+            for i, v in enumerate(a):
+                row[f"action_{i}"] = round(float(v), 6)
+            rows.append(row)
+
+    out = pd.DataFrame(rows)
+    if out_path:
+        out.to_csv(out_path, index=False)
+        print(f"Saved {len(ep_ids)} episodes ({len(out):,} frames) → {out_path}")
+    return out
+
+
+# ── dataset-specific converters ───────────────────────────────────────────────
+
+DATASETS = {
+    # Real tabletop push-T (Columbia / CAIRLAB)
+    # Robot: custom delta robot, 2-DOF end-effector + contact sensors
+    # Task: push a T-shaped block to a goal region
+    # State: 8-dim (EE pos/vel + block pose estimate)
+    "pusht": dict(
+        hf="lerobot/columbia_cairlab_pusht_real",
+        label="Push-T (Columbia real robot)",
+        state_col="observation.state",
+        action_col="action",
+        max_eps=40,
+        note="2-DOF delta robot, tabletop push task, 136 episodes total"
+    ),
+
+    # Franka Panda free-play dataset (NYU)
+    # Robot: 7-DOF Franka Emika Panda — the most common research arm
+    # Task: unstructured manipulation play (no fixed goal)
+    # State: 13-dim (7 joint pos + 6 EE pose)
+    "franka": dict(
+        hf="lerobot/nyu_franka_play_dataset",
+        label="Franka Panda Play (NYU)",
+        state_col="observation.state",
+        action_col="action",
+        max_eps=50,
+        note="7-DOF Franka Panda, 456 episodes of free-play manipulation"
+    ),
+
+    # Unitree H1 humanoid — warehouse task
+    # Robot: full-size humanoid, 19-DOF state, 40-DOF action
+    # Task: pick and place in warehouse setting
+    # No reward signal — we treat episode completion as success
+    "humanoid": dict(
+        hf="lerobot/unitreeh1_warehouse",
+        label="Unitree H1 Humanoid (warehouse)",
+        state_col="observation.state",
+        action_col="action",
+        max_eps=24,
+        note="19-DOF humanoid state, 40-DOF action, 24 episodes"
+    ),
+
+    # ALOHA bimanual static (cups open) — same as demo tab
+    "aloha": dict(
+        hf="lerobot/aloha_static_cups_open",
+        label="ALOHA Bimanual (cups open)",
+        state_col="observation.state",
+        action_col="action",
+        max_eps=50,
+        note="14-DOF bimanual ALOHA, 50 episodes, cup-opening task"
+    ),
+}
+
+
+# ── multi-policy comparison helper ───────────────────────────────────────────
+
+def make_comparison_csv(datasets_and_names: list[tuple[str, str]],
+                        max_eps_each: int = 20,
+                        out_path: str = "comparison_eval.csv"):
+    """
+    Combine multiple datasets as different 'policies' for A/B comparison.
+
+    datasets_and_names: list of (dataset_key, policy_label)
+    Example:
+        make_comparison_csv([("pusht","Push-T"), ("franka","Franka"), ("aloha","ALOHA")])
+    """
+    dfs = []
+    for key, label in datasets_and_names:
+        cfg = DATASETS[key]
+        df  = to_eval_csv(cfg["hf"], label, cfg["state_col"], cfg["action_col"],
+                          max_episodes=max_eps_each)
+        # Truncate to common state dim
+        dfs.append(df)
+
+    # Align state/action columns across datasets (fill missing with 0)
+    out = pd.concat(dfs, ignore_index=True).fillna(0.0)
+    out.to_csv(out_path, index=False)
+    print(f"\nSaved multi-policy comparison CSV → {out_path}")
+    print(f"Policies: {out['policy_name'].unique().tolist()}")
+    print(f"Total episodes: {out['episode_id'].nunique()}")
+    print(f"Total frames: {len(out):,}")
+    return out
+
+
+# ── CLI ───────────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", choices=list(DATASETS.keys()) + ["compare"],
+                        default="pusht", help="Dataset to convert")
+    parser.add_argument("--out", default=None, help="Output CSV path")
+    parser.add_argument("--max-eps", type=int, default=None,
+                        help="Max episodes to convert (default: all)")
+    args = parser.parse_args()
+
+    if args.dataset == "compare":
+        out = args.out or "comparison_eval.csv"
+        make_comparison_csv(
+            [("pusht","Push-T"), ("franka","Franka"), ("humanoid","H1-Humanoid")],
+            max_eps_each=args.max_eps or 15,
+            out_path=out,
+        )
+    else:
+        cfg = DATASETS[args.dataset]
+        out = args.out or f"{args.dataset}_eval.csv"
+        print(f"\n{cfg['label']}")
+        print(f"Note: {cfg['note']}\n")
+        to_eval_csv(cfg["hf"], cfg["label"], cfg["state_col"], cfg["action_col"],
+                    max_episodes=args.max_eps or cfg["max_eps"], out_path=out)
+
+    print("\nDone. Upload the CSV to the HuggingFace Space:")
+    print("  https://huggingface.co/spaces/ShubhamRasal/robot-policy-eval")