Research map / Reinforcement learning / Policy gradient and actor-critic
Constrained Diffusers for Safe Planning and Control
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Proposes Constrained Diffusers, which enforces static and time-varying (discrete control barrier function) constraints during reverse diffusion of pre-trained diffusion planners via projected, primal-dual, or augmented Lagrangian constrained Langevin sampling, plus an inverse dynamics model for actions.
Problem
Pre-trained diffusion planners do not guarantee safety under nonconvex and time-varying constraints, and existing constraint-conditioned or CBF-based diffusion methods need retraining or costly QP solves at each denoising step.
Approach
Reinterpret constrained trajectory generation as constrained Langevin sampling over the reverse diffusion process, applying projection, primal-dual, or augmented Lagrangian updates to a pre-trained score model, with discrete CBF constraints and an inverse dynamics model for online safe execution.
Key results
- On Maze2D umaze, ALM reduces total violations to 0.002 (from 7.316 for Diffuser) at ~0.0018 s per step, versus SafeDiffuser at 0.1208 s.
- On Hopper, the projected method reaches 0 planning violations and 0.29% violation rate, but takes 0.93 s per step, while primal-dual and ALM take ~0.003 s per step with 0.41% and 0.09% violation rates.
- On SafetyBallRun, ALM and primal-dual reduce violation rates to 1.6% and 2.2% versus 37.7% for Diffuser, with reward gains over Diffuser.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace unconstrained diffusion sampling with projected constrained sampling | Diffuser · Maze2d-umaze | Total Violations: 7.316 $\pm$ 0.884 -> \textbf{0.000 $\pm$ 0.000} | Helped |
| use primal-dual constrained sampling instead of SafeDiffuser | SafeDiffuser · Maze2d-umaze | Time/s: 0.1208 -> \textbf{0.0017} | Helped |
| replace unconstrained diffusion sampling with augmented Lagrangian constrained sampling | Diffuser · Maze2d-large | Total Violations: 12.400 $\pm$ 2.440 -> 0.033 $\pm$ 0.044 | Helped |
| use projected constrained sampling instead of unconstrained diffusion | Diffuser · Hopper | Planning Violations: 2.55 $\pm$ 0.21 -> \textbf{0.00 $\pm$ 0.00} | Helped |
| use primal-dual constrained sampling instead of unconstrained diffusion | Diffuser · Swimmer | Planning Violations: 8.38 $\pm$ 0.87 -> 0.17 $\pm$ 0.08 | Helped |
| use augmented Lagrangian constrained sampling instead of unconstrained diffusion | Diffuser · SafetyBallRun | Total Impl. Violations: 2.20 $\pm$ 0.77 -> \textbf{0.02 $\pm$ 0.02} | Helped |
| use augmented Lagrangian constrained sampling instead of projected sampling | Projected · SafetyBallRun | Total Impl. Violations: 1.01 $\pm$ 0.63 -> \textbf{0.02 $\pm$ 0.02} | Helped |
| use constrained sampling with primal-dual updates | Diffuser · SafetyBallRun | Reward: 646.4 $\pm$ 30.3 -> 722.2 $\pm$ 118.6 | Helped |
| use projected constrained sampling instead of unconstrained diffusion | Diffuser · SafetyBallRun | Reward: 646.4 $\pm$ 30.3 -> 586.7 $\pm$ 33.7 | Hurt |
| use primal-dual or augmented Lagrangian sampling rather than projected sampling | Constrained Diffusers · Hopper | Time/s: 0.9305 -> 0.0027; 0.9305 -> 0.0031 | Helped |
Surprising findings
- The projected method, despite perfect planning-stage constraint satisfaction, has higher implementation violation rates on SafetyBallRun than the baselines' ordering would suggest (25.8%), indicating planning-stage satisfaction does not transfer to execution.
Limitations
- Constraint satisfaction is approximate for primal-dual and ALM (small nonzero violations remain).
- Projected method is computationally expensive on locomotion tasks (~0.93 s per step) and has higher implementation violation rates on time-varying constraints.
- Relies on an inverse dynamics model assumed to exist with no input constraints.
Experimental setup
Datasets: Maze2D Hopper Swimmer PyBullet SafetyBallRun
Benchmarks: Maze2D umaze Maze2D large Hopper Swimmer PyBullet SafetyBallRun
Metrics: total constraint violations reward violation rate computation time per diffusion step distance from original trajectory
Baselines: Diffuser Conditional Diffuser SafeDiffuser
Builds on: Diffuser SafeDiffuser Conditional Diffuser stochastic gradient Langevin dynamics discrete control barrier functions
Closest papers by meaning
- DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising (NeurIPS 2026 · similarity 0.90)
- SafeDiffuser: Safe Planning with Diffusion Probabilistic Models (ICLR 2025 · similarity 0.88)
- Constrained Generative Modeling with Manually Bridged Diffusion Models (AAAI 2025 · similarity 0.87)
- Model-based Diffusion for Trajectory Optimization (NeurIPS 2024 · similarity 0.86)
- Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning (NeurIPS 2026 · similarity 0.86)
- DiffuserLite: Towards Real-time Diffusion Planning (NeurIPS 2024 · similarity 0.86)
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