{
 "generated": "2026-10-31",
 "count": 6,
 "note": "Every value carries a verbatim quote verified against the paper text. Missing values are not reported by the paper.",
 "rows": [
  {
   "task": "multi-arm contact-rich manipulation",
   "robot": null,
   "sim_platform": null,
   "transfer": "zero-shot",
   "sim_result": {
    "value": "65.5%",
    "metric": "mean policy success",
    "quote": "We evaluate ScheduleGen on 28 simulation task variants, including seven externally authored RoboDojo tasks and settings inaccessible to prior systems; it achieves 79.6% mean data-generation success and trains policies with 65.5% mean success.",
    "kind": "success_rate",
    "low": 65.5,
    "high": 65.5
   },
   "real_result": {
    "value": "50–80%",
    "metric": "success across eight real-world variants",
    "quote": "ScheduleGen policies outperform those trained on a state-of-the-art data generation baseline, and achieve 50–80% success across eight real-world variants through zero-shot sim-to-real and real-to-sim-to-real transfer.",
    "kind": "range",
    "low": 50.0,
    "high": 80.0
   },
   "missing": [
    "real-world mean success rate",
    "per-task real-world success rates",
    "number of real-world trials per variant"
   ],
   "checkable": true,
   "delta_pp": null,
   "arxiv": "2610.39429",
   "source": "https://arxiv.org/html/2610.39429",
   "verified": true,
   "title": "ScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitation Learning"
  },
  {
   "task": "steep deformable slope traversal",
   "robot": "ERNEST rover",
   "sim_platform": "DARTS",
   "transfer": "online adaptation",
   "sim_result": {
    "value": "approximately 90%",
    "metric": "slope-climbing success",
    "quote": "In simulation, TERRA reaches approximately 90% slope-climbing success within five trials under severe out-of-distribution pretraining conditions, outperforming exploration baselines in reward, completion time, and energy to first success.",
    "kind": "success_rate",
    "low": 90.0,
    "high": 90.0
   },
   "real_result": {
    "value": "within three to five real-world trials",
    "metric": "recovery of coordinated active-suspension locomotion",
    "quote": "On the ERNEST rover, TERRA recovers coordinated active-suspension locomotion on a deformable Mars-simulant slope within three to five real-world trials.",
    "kind": "other",
    "low": null,
    "high": null
   },
   "missing": [
    "real-world slope-climbing success rate",
    "sim-to-real performance gap",
    "real-world energy or completion time"
   ],
   "checkable": true,
   "delta_pp": null,
   "arxiv": "2610.38657",
   "source": "https://arxiv.org/html/2610.38657",
   "verified": true,
   "title": "Explore Where It Matters: Targeted Exploration for Efficient Rover Adaptation"
  },
  {
   "task": "retractor take-over in open surgery",
   "robot": "Trossen WidowX AI arm",
   "sim_platform": "Isaac Sim",
   "transfer": "zero-shot",
   "sim_result": {
    "value": "94%",
    "metric": "grasp success rate on held-out simulated presentations",
    "quote": "The same checkpoint grasps 94 % of held-out simulated presentations, with a post-release tip offset of 8.0 ± \\pm 6.0 mm and axial twist of 3.8 ± \\pm 6.4 ∘ over grasped trials.",
    "kind": "success_rate",
    "low": 94.0,
    "high": 94.0
   },
   "real_result": {
    "value": "90%",
    "metric": "grasp success rate on surgeon-defined real presentations",
    "quote": "Deployed zero-shot, it grasps the retractor in 90 % of 50 surgeon-defined phantom presentations and preserves retraction at handoff in 78 %.",
    "kind": "success_rate",
    "low": 90.0,
    "high": 90.0
   },
   "missing": [
    "real post-release instrument tip offset",
    "real axial twist",
    "real-world instrument segmentation/plane-estimation error"
   ],
   "checkable": true,
   "delta_pp": 4.0,
   "arxiv": "2610.38234",
   "source": "https://arxiv.org/html/2610.38234",
   "verified": true,
   "title": "SimRetract: Zero-Shot Sim-to-Real Robotic Assistance for Retractor Take-Over in Open Surgery"
  },
  {
   "task": "Image-goal navigation for drones with omnidirectional vision",
   "robot": "drone with Jetson Orin NX, OAK-4P-New quad-fisheye rig, Livox Mid-360s LiDAR",
   "sim_platform": "Habitat, IsaacSim",
   "transfer": "zero-shot",
   "sim_result": null,
   "real_result": null,
   "missing": [
    "simulation Success Rate (SR) on Habitat benchmarks",
    "real-world Success Rate (SR)",
    "collision rate in real-world flights"
   ],
   "checkable": true,
   "delta_pp": null,
   "arxiv": "2610.36968",
   "source": "https://arxiv.org/html/2610.36968",
   "verified": true,
   "title": "Omni-IGN: Onboard Image-Goal Navigation for Drones with Omnidirectional Vision"
  },
  {
   "task": "pressing buttons and moving sliders",
   "robot": "Unitree G1",
   "sim_platform": "MuJoCo",
   "transfer": "not stated",
   "sim_result": null,
   "real_result": null,
   "missing": [
    "manipulation success rate values for Midi Slider/Toggle Switch tasks",
    "sim-to-real transfer success rate"
   ],
   "checkable": true,
   "delta_pp": null,
   "arxiv": "2610.36859",
   "source": "https://arxiv.org/html/2610.36859",
   "verified": true,
   "title": "ActiveAlign: Aligning Egocentric Manipulation via Active Perception"
  },
  {
   "task": "soft robot shape reconstruction",
   "robot": "SoftSnap",
   "sim_platform": null,
   "transfer": "zero-shot",
   "sim_result": {
    "value": "93%",
    "metric": "shape reconstruction success rate",
    "quote": "On 5,000 test samples generated from previously unseen threading patterns, the method achieves a 93% shape reconstruction success rate under surrogate-based evaluation.",
    "kind": "success_rate",
    "low": 93.0,
    "high": 93.0
   },
   "real_result": null,
   "missing": [
    "physical reconstruction error",
    "real-world success rate",
    "sim-to-real error"
   ],
   "checkable": true,
   "delta_pp": null,
   "arxiv": "2610.36681",
   "source": "https://arxiv.org/html/2610.36681",
   "verified": true,
   "title": "From Biological Curves to Soft Modular Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement"
  }
 ]
}