Cycle-Based Task Manipulation Benchmark

• Cycle-based task manipulation benchmark is a framework that defines and evaluates robotic tasks requiring exact cycle repetition and smart termination.
• The benchmark employs standard environments, multi-modal data, and automatic cycle detection to compute performance metrics like Success Rate and Cycle Count Deviation.
• Evaluation results show high success rates and low cycle deviations, supporting robust policy performance in both simulated and real-world robotic applications.

A cycle-based task manipulation benchmark characterizes and systematically evaluates robotic policies and models on repetitive manipulation tasks requiring explicit cycle counting and autonomous termination. Such benchmarks address a fundamental gap in the evaluation of manipulation policies that must use sequential memory, effectively interpret language commands specifying the number of repetitions, and maintain accuracy in timing and event logging over multi-modal streams. Recent frameworks, most notably CycleManip (Wei et al., 30 Nov 2025), ground this class of tasks with rigorous definitions, standardized environments, and robust metrics, supporting both simulated and real-world settings. In parallel, analogous cycle-based task graph reusage and minimization strategies in high-performance computing (HPC) facilitate efficient parallelization and scalable execution (Álvarez et al., 2022).

1. Formal Definitions and Task Taxonomy

Cycle-based manipulation, as defined in CycleManip (Wei et al., 30 Nov 2025), comprises robotic tasks in which a primitive motion—such as hammering, shaking, rolling, or chopping—must be exactly repeated N times, with termination immediately upon completion of the prescribed repetition count. Formally, for policy $\pi$ and observation sequence $\{o_1,\ldots,o_T\}$, a successful episode outputs a sequence of actions $\{a_1,\ldots,a_T\}$ such that exactly N repetition events are detected and the trial ceases at expected terminal time $T^* = \sum_{i=1}^N \Delta t_i$, where $\Delta t_i$ denotes the duration of cycle $i$.

The CycleManip benchmark introduces eight task archetypes, each with loop counts configurable from 1 to 8 and cycle durations ranging from 0.3 seconds (Morse Tapping) to 3.0 seconds (Chemical Mixing). Tasks are instantiated using predefined event generators for contact (collision-based) and non-contact (peak-detection) cycles.

Task Name	Description	Duration per Cycle (s)
Block Hammering	Grasp and strike with hammer	0.8
Bottle Shaking	Oscillatory motion (±20°) of bottle	1.2
Carrot Cutting	Slice with knife per cycle	2.0
Egg Beating	Circular whisking motion	2.5
Morse Tapping	Tap Morse key	0.3
...	...	...

Cycles are counted by automatic detectors: collision state machines for contact tasks and pose peak detection for others.

2. Dataset Modalities and Scaling

The CycleManip dataset aggregates 1,600 simulated episodes (8 tasks × 200 trajectories) with trajectory lengths proportional to loop count (20–100 timesteps/task). Data modalities per timestep include:

• High-overhead: RGB frames (640×480 at 20 Hz), depth maps, point clouds.
• Low-overhead: joint angles/velocities, 6D end-effector pose deltas ($\Delta x_t = x_t - x_{t-1}$).
• Annotations: ground-truth cycles completed ($C_t^*$), cycle target ($N$), and object pose.

Real-world data, captured via teleoperation frameworks (Gello, TypeTele, OpenWBC), augments the simulated suite with up to 150 expert demonstrations per task and supports deployment on diverse hardware (single-arm, dual-arm, dexterous hands, humanoids). Recommended data splits are 70% train, 15% validation, and 15% test, enabling robust generalization and ablation studies across modalities and policy architectures.

3. Evaluation Methodology and Metrics

The automatic evaluator for cycle-based benchmarks counts executed cycles and verifies termination aligned to $T^*$. Two principal quantitative metrics are:

• Success Rate (SR): $SR = N_\text{succ} / N_\text{total}$, where $N_\text{succ}$ is the number of episodes with perfect cycle adherence and termination, and $N_\text{total}$ the total trials.
• Average Cycle Count Deviation (CCD): $$CCD = \frac{1}{N_\text{total}} \sum_{i=1}^{N_\text{total}} |C_i - N_i^*|$$, with $C_i$ executed cycles and $N_i^*$ commanded cycles.

The evaluation automatically discriminates between contact and non-contact scenarios using dedicated detectors. Manual verification (>100 episodes, >99% reliability) substantiates the robustness of the evaluator.

4. Task-Based Programming Analogs and Graph Cyclicity

In HPC, cycle-based DAG reuse and minimization is realized through Directed Cyclic Task Graphs (DCTGs) (Álvarez et al., 2022). The taskiter construct enables programmers to declare per-iteration cyclicity, thereby reducing task creation, dependency, and scheduling costs. Each iteration $\ell$ of $N$ iterations sustains the same set of intra-iteration ($E_\text{a}$) and cross-iteration ($E_\text{c}$) dependencies:

• $E_\text{a}$: intra-iteration edges between template tasks.
• $E_\text{c}$: cross-iteration edges linking iteration $\ell$ to $\ell+1$.

DCTGs are materialized in OmpSs-2 and OpenMP using a single pragma at the loop level, allocating persistent task descriptors whose counters decrement with completion and requeue themselves if required. No new descriptors are allocated beyond the initial iteration, stabilizing overhead.

The immediate-successor locality-aware heuristic further bypasses scheduler contention by executing ready successors on the same core, exploiting cache-locality in the working set ($W(T_1) \cap W(T_2) \neq \emptyset$ for dependent tasks $T_1 \to T_2$), with negligible locking on the fast path.

5. Baseline Comparisons, Performance, and Real-World Transfer

CycleManip’s end-to-end approach is benchmarked against dynamic programming (DP), recurrent decision trees (RDT), and Vision-Language-Action (VLA) models. In simulation (8 tasks, 100 trials each), the CycleManip method attains 87% average SR and 0.5 CCD, dramatically surpassing DP (16%, 4.1), DP3 (32%, 3.2), RDT (35%, 2.3), Pi-0 (18%, 3.4).

Method	Average SR (%)	Average CCD
DP	16	4.1
DP3	32	3.2
RDT	35	2.3
Pi-0	18	3.4
Ours	87	0.5

Real-world evaluation (16 trials per task, various robots) confirms sustained superiority of CycleManip policies, e.g., block hammering at 93.8% SR (DP3 baseline: 37.5%). When historical perception and understanding modules are ablated (“Ours w/o understanding”), performance decreases, indicating the criticality of effective history encoding.

Efficiency overhead, illustrated on RTX 4090 (e.g., carrot cutting), shows marginal increases in GPU memory and runtime in exchange for much higher SR (86% versus 38% for DP3).

6. Artifact Availability, Implementation, and Best Practices

The CycleManip benchmark system provides open access to code, dataset generation, and standardized evaluation scripts (https://isee-laboratory.github.io/CycleManip/), supporting reproduction and benchmarking. Practitioners clone the repository, install PyTorch and RoboTwin2.0, regenerate synthetic data, and execute evaluate.py for automatic scoring.

Best practices for cycle-based benchmarks include:

• Using consistent cycle event detectors tailored for contact/non-contact modalities.
• Encoding history perception and context for accurate termination and cycle adherence.
• Validating on both simulation and physical platforms, with adaptable hardware configurations.

In parallel HPC workflows, adoption of taskiter and immediate-successor heuristics—pairing cyclic task graph reusage with locality-aware scheduling—unlocks sub-millisecond task granularities, strong scaling, and overhead minimization, as evidenced by 3.7× speedups on OmpSs-2 and up to 12.1× compared to GCC-OpenMP (Álvarez et al., 2022).

7. Context, Impact, and Future Directions

The introduction of the cycle-based task manipulation benchmark bridges a longstanding gap in robot learning and imitation, enabling quantifiable paper of policies under cyclic constraints. Its integration of multi-modal data, cycle event logging, programmatic evaluation, and real-world transfer capability catalyzes both academic reproducibility and system-level validation.

A plausible implication is that cycle-based benchmarks may inform development of more generalized history-aware imitation and reinforcement policies, improved physical deployment under repetition constraints, and enhanced diagnostic tooling for temporal accuracy in robot control loops. In HPC, the principles of cycle-based graph reusage and scheduling heuristics generalize beyond iterative solvers to any multi-step cyclic simulation or data flow, particularly as task granularities decrease and hardware parallelism scales.

Subsequent research may explore enriched taxonomy of cycles (hierarchical, stochastic, language-conditioned), integration with VLA models for plug-and-play policy deployment, and extending cycle event detection for non-rigid and deformable object manipulation. The systematic benchmarking methodology established by CycleManip supports comparative research in these domains, advancing both foundational and applied robotics.