Fiatlux: A Long-Horizon Benchmark for Humanoid Ladder Climbing and Light-Bulb Replacement

Fiatlux: A Long-Horizon Benchmark for Humanoid Ladder Climbing and Light-Bulb Replacement

Pavel Bushuyeu, Yujin Chen, Anton Nikolaev, Brian Shu, Igor Molybog · N/A · 2026

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Summary

Existing benchmarks evaluate tabletop manipulation, flat-floor household activity, or humanoid locomotion and manipulation as separate task groups; none scores vertical mobility and dexterous work on a fragile payload in one long-horizon episode. We present Fiatlux, a light-bulb replacement bench...

Abstract Summary

Existing benchmarks evaluate tabletop manipulation, flat-floor household activity, or humanoid locomotion and manipulation as separate task groups; none scores vertical mobility and dexterous work on a fragile payload in one long-horizon episode. We present Fiatlux, a light-bulb replacement benchmark built on NVIDIA Isaac Lab. In one episode, a Unitree G1 humanoid positions a step ladder under a ceiling or wall fixture, climbs it, exchanges a spent bulb in a socket for a fresh one, and leaves the spent one in a disposal crate. We decompose the episode into twelve subtask environments scored on difficulty-weighted gates. The goal is a successful replacement, with the fresh bulb seated, the spent one disposed of, neither dropped, and a fragility bound not crossed. Runs that fall short can earn partial credit. Observations are split into a standard mode (signals a physical robot could sense or estimate) and a privileged mode (exact simulator state). We specify the evaluation protocol and provide reference baseline implementations spanning RSL-RL PPO, zero-shot NVIDIA GR00T N1.7 Vision-Language-Action (VLA) models, and whole-body controllers. Additionally, we provide the teleoperated recordings used to specify and check the success gates. The benchmark code and the teleoperated recordings are available at fiatlux-bench.github.io.

Key Points

  • Existing benchmarks evaluate tabletop manipulation, flat-floor household activity, or humanoid lo...
  • We present Fiatlux, a light-bulb replacement benchmark built on NVIDIA Isaac Lab
  • In one episode, a Unitree G1 humanoid positions a step ladder under a ceiling or wall fixture, cl...
  • We decompose the episode into twelve subtask environments scored on difficulty-weighted gates
  • The goal is a successful replacement, with the fresh bulb seated, the spent one disposed of, neit...

Methodology and Implementation

Fiatlux structures the long-horizon light-bulb replacement task into a single scene built in NVIDIA Isaac Lab on the PhysX solver. The complete benchmark task evaluates the multi-stage pipeline as one flat episode ($T=72{,}000$ steps at 50 Hz, a $1{,}440\text{ s}$ horizon).

To establish clear, non-overlapping operational boundaries for stage-wise policy evaluation, Fiatlux registers 12 atomic subtasks as separate environments (Table I ).

Methodology and Implementation — Fiatlux: A Long-Horizon Benchmark for Humanoid Ladder Climbing and Light-Bulb Replacement
Fig. 1: Frames from the recorded teleoperated takes, one per subtask that has one. Each frame comes from a take that fires the subtask’s gate. The four climbing subtasks are omitted: none has a take that fires its gate. The two stance frames are recorded with the robot placed on the top tread, and show that it holds the on-ladder stance with and without the bulb; the transitions to and from that stance are not demonstrated.

Validation and Baselines

Evaluations use the benchmark CLI tools. Each policy runs one episode per subtask at each of four layout seeds, reported as mean and standard deviation over the seeds. Rollout trajectories are recorded as bags and scored offline, so the same recording can be re-scored under different rules without re-running the simulator.

The offline scorer reports the success rate, the clean success rate of Section III , the mean per-episode score, and the broken and dropped rates.

Sources and Demonstrations

Figures are reproduced from Bushuyeu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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