Safe Flow Mpc Eval

---
name: safe-flow-mpc-eval
description: Evaluates a hybrid trajectory planning framework that fuses flow matching with model-predictive control. It probes the system's ability to generate adaptive, collision-free motions for a 7-DoF robot manipulator while strictly enforcing safety constraints in real-time across global planning, reactive replanning, and dynamic human-robot handover scenarios. Use when the user wants to benchmark on Custom Robot Manipulation Benchmarks (Exp 1-3), or asks about evaluating this task. Reports adherence to safety constraints.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2602.12794
  bibtex_key: [REDACTED]
  confidence: medium
---

# safe-flow-mpc-eval

> SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-based Policies — Oelerich et al. (2026) (arXiv:2602.12794, 2026)

## What this evaluates

Evaluates a hybrid trajectory planning framework that fuses flow matching with model-predictive control. It probes the system's ability to generate adaptive, collision-free motions for a 7-DoF robot manipulator while strictly enforcing safety constraints in real-time across global planning, reactive replanning, and dynamic human-robot handover scenarios.

## Datasets

- **Custom Robot Manipulation Benchmarks (Exp 1-3)** — total ?; splits: (unstated); repo https://github.com/TU-Wien-ACIN-CDS/SafeFlowMPC

## Metrics

- `adherence to safety constraints` **(primary)** — range: other
  - Comparative evaluation of trajectory quality, collision avoidance, and real-time execution at 10Hz against five baselines (VP-STO, BoundMPC, BC, FM, Ours with NL-Opt). Specific numerical metrics are not detailed in the provided excerpt.

## Input / output format

**Input**: Current robot state, previous trajectory (for behavior cloning baseline), and environmental obstacle/target configurations.

**Output**: Sequential trajectory points or control commands for the 7-DoF KUKA manipulator.

## Scoring recipe

```python
for env in [global_context, obstructed_replanning, human_handover]:
    for method in [VP_STO, BoundMPC, BC, FM, SafeFlowMPC]:
        trajectory = method.plan(state=env.current_state, target=env.target)
        safety_violated = check_safety_constraints(trajectory)
        success = trajectory_reaches_target(trajectory) and not safety_violated
        record(method, env, success, safety_violated, execution_time)
```

## Common pitfalls

- The excerpt does not specify exact quantitative metrics (e.g., success rate, collision count, or constraint violation magnitude), making direct metric reproduction impossible without the full paper.
- Real-time performance is tied to a fixed 10Hz execution frequency and specific solver configurations (e.g., IPOPT vs. real-time iteration), which heavily influence results.
- The human-robot handover experiment depends on an external dataset [7] that is not provided or described in this section.

## Evidence (verbatim from paper)

> Three experiments are presented in this section to evaluate performance, versatility, and adherence to safety constraints of the developed method. The first experiment utilizes our method to learn from a global trajectory planner to achieve fast local planning in a global context. The second experiment focuses on reactive online replanning in an obstructed environment. The third experiment is a dynamic human-robot object handover where the robot learns behavior from a human-human handover dataset [7].

## Citation

```bibtex
@misc{oelerich2026safeflowmpc,
  title={SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-based Policies},
  author={Oelerich et al. (2026)},
  year={2026},
  note={arXiv:2602.12794}
}
```

- arXiv: 2602.12794