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Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames
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Proposes training behavioral cloning on "blindfolded" human experts whose observations are masked, showing the resulting policies generalize better to unseen tasks in Procgen maze/heist and real-robot peg insertion, backed by a bound scaling with sqrt(I/m).
Problem
Behavioral cloning from demonstrations on a few tasks generalizes poorly to unseen task variations; prior work focuses on algorithms, representations, and data diversity but not on the behavior of the demonstrating expert.
Approach
Deliberately hide task information from the human demonstrator (a "blindfold" information bottleneck) so the expert must explore, and clone the resulting exploratory trajectories with an ordinary BC algorithm; theory shows generalization error scales with sqrt(I_{T;Z}/m), so lowering task information from the expert helps.
Key results
- On Procgen maze, the blindfolded-expert BC policy reaches test score 6.37 ± 0.47 versus 3.35 ± 0.29 for standard-expert BC with 2K trajectories, beating a 1M-step PPO-expert BC baseline (4.46 ± 0.16).
- In real-robot peg insertion, cloning blindfolded experts achieves higher success on withheld shapes than standard-expert BC across k=2 and k=5 training-shape settings, with the largest gain when fewer shapes are used for training.
- Blindfolded demonstrations are longer and more exploratory (e.g., maze 51.6 vs 28.6 steps average; map coverage 0.247 vs 0.137 averaged over peg shapes).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| blindfold demonstrators by hiding most of the level | behavioral cloning on Procgen maze and heist · unseen Procgen test levels | game score | Helped |
| blindfold demonstrators by masking the target hole shape | behavioral cloning for peg insertion · withheld and training peg shapes | success rate | Helped |
| add random observation noise with P=100 (ablation) | peg insertion behavioral cloning · plus and ellipse test shapes | success rate: 20% -> 96%; 17% -> 79% | Helped |
| add random observation noise with P=170 (ablation) | peg insertion behavioral cloning · plus and ellipse test shapes | success rate: 20% -> 58%; 17% -> 62% | Mixed |
| add random observation noise with P=200 (ablation) | peg insertion behavioral cloning · plus and ellipse test shapes | success rate: 20% -> 21%; 17% -> 54% | Mixed |
| add random observation noise with P=200 (ablation) | peg insertion behavioral cloning · expert demonstrations | success rate: 100% -> 58% | Hurt |
| train on twice as many non-blindfolded trajectories to match the blindfolded demonstrations' total steps (ablation) | Procgen maze behavioral cloning · unseen test levels | game score: 3.35 ± 0.29 -> 3.65 ± 0.3, versus π_BF-BC 6.37 ± 0.47 | Hurt |
Surprising findings
- Adding uniform random noise to expert observations as a non-task-specific blindfold also improved generalization to unseen peg shapes (e.g., plus 20% to 96% at noise level 100), though less than the SAM2 mask.
- Even on training peg shapes, the blindfolded-expert policy generalized better than the standard-expert policy, attributed to robustness against lighting and control-loop errors.
Limitations
- Each domain requires a tailored blindfold; too little obstruction does not induce exploration while too much impedes informative exploration.
- Theory assumes deterministic expert, finite action and policy classes, and discrete actions; extension to continuous actions is not established.
- Peg insertion simplified: peg fixed to gripper and holes 3D-printed, with human demonstrations from the authors only.
Experimental setup
Datasets: Procgen maze and heist human demonstrations robotic peg insertion human demonstrations (FMB-based shapes)
Benchmarks: Procgen maze Procgen heist robotic peg insertion (10 shapes, FMB-based)
Metrics: game score success rate average steps map coverage score state visitation entropy
Baselines: standard BC from fully-informed expert (pi_BC) BC with extended non-blindfolded data (pi_BC-ext) 1M-step PPO-expert BC dataset from prior work uniform noise blindfold
Builds on: behavioral cloning information bottleneck generalization bounds Procgen Functional Manipulation Benchmark SERL
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