Teleoperation Augmentation Primitives (TAPs)

• Teleoperation Augmentation Primitives (TAPs) are modular, parameterized routines integrated into teleoperation systems to mitigate operator limitations and enhance robotic autonomy.
• They encompass open-loop routines, motion primitives, and probabilistic or dynamical templates that support safe, efficient execution of complex tasks.
• Their integration in hybrid and learning-augmented frameworks has demonstrated improved performance, reduced operator burden, and enhanced overall safety.

Teleoperation Augmentation Primitives (TAPs) designate a class of modular, parameterized routines integrated into teleoperated and shared-control robotic systems to mitigate operator limitations, enhance robotic autonomy, and structure complex or long-horizon tasks. TAPs have emerged in recent literature as operationalizable units—open-loop, probabilistic, or feedback-embedded—that can be triggered by the human operator, an arbitration mechanism, or a policy—often within a model-based or learning-augmented control stack. TAP implementations encompass open-loop routines embedded in manipulation, polynomial trajectory primitives for dynamic multirotor flight, ProMP/DMP-based blending policies for assistive autonomy, and high-rate arbitration in mixed-reality teleoperation. TAPs have demonstrated increased performance, safety, and reduced operator burden across diverse robotic modalities (Haastregt et al., 4 Dec 2025, Goel et al., 2022, Spitzer et al., 2019, Penco et al., 1 Nov 2024, Maeda, 2022).

1. Formal Definition and Taxonomy

TAPs are formally defined as discrete, parameterizable routines that enrich human-in-the-loop robotic control with algorithmically encoded sub-behaviors or automatic augmentation. TAPs can be categorized broadly as:

• Open-loop subroutines: E.g., axis locking, perching-waypoint, repetitive manipulation actions (unscrewing), where routines are parameterized (DOF set, waypoint ID, phase count, subcommand sequence), directly inserted into the control flow (Haastregt et al., 4 Dec 2025).
• Motion-primitive libraries: Parameterized polynomial or unicycle-model trajectories, generated as feasible candidate motions, and selected or blended based on operator input and contextual constraints (Goel et al., 2022, Spitzer et al., 2019).
• Probabilistic/dynamical primitives: ProMPs and DMPs, encoding learned or prescriptive movement-to-goal policies, capable of on-the-fly conditioning, object-centric adaptation, and implicit blending with user command (Penco et al., 1 Nov 2024, Maeda, 2022).
• Composite routines: TAPs organized into higher-order planners, hierarchies, or blended arbitration pipelines, supporting context-sensitive handover or shared policy execution.

2. Algorithmic Integration

The integration of TAPs into teleoperation systems varies by modality and objective.

• Hybrid-Diffusion Framework: TAPs are inserted by the operator at demonstration time (logging routines, axis locks, open-loop sub-behaviors) and are subsequently predicted autonomously by a visuomotor diffusion policy trained on joint low-level actions and TAP triggers; the system switches between closed-loop control and TAP execution as needed (Haastregt et al., 4 Dec 2025).
• Multirotor Control Pipelines: TAPs correspond to polynomial or arc-based motion primitives generated by solving minimum-snap quadratic programs with endpoint and dynamic constraints. At runtime, joystick inputs are mapped to candidate primitives, which are efficiently filtered for safety (collision-free) using onboard local mapping, then executed at high rates (Goel et al., 2022, Spitzer et al., 2019).
• Mixed-Reality and Probabilistic Assistance: TAPs are ProMPs and affordance templates, selected, conditioned, and blended in a mixed-reality operator interface. Early motion is recognized, ProMPs are conditioned on observed partial trajectories, and blended with precise affordance templates using a logistic schedule. Arbitration is mediated via user acceptance and confidence-weighted blending in joint or task space (Penco et al., 1 Nov 2024).
• Implicit DMP Arbitration: TAPs are realized as DMP instances, one per hypothesis, each capable of disturbance rejection—operator command is treated as a deviation, and the primitive autonomously restores goal attraction, implicitly blending without explicit weights (Maeda, 2022).

3. Training Objectives and Policy Extensions

In learning-augmented frameworks, TAPs demand joint optimization of action generation and primitive identification.

• Hybrid-Diffusion Training: Training minimizes a composite loss

$$L_{\text{total}}(\phi) = L_{\text{diffusion}}(\phi) + \lambda L_{\text{routine}}(\phi) + \mu L_{\text{gate}}(\phi)$$

where $L_{\text{diffusion}}$ is the denoising score-matching loss for action blocks, $L_{\text{routine}}$ is a cross-entropy loss for TAP trigger prediction, and $L_{\text{gate}}$ penalizes overlapping TAPs. TAPs are appended as one-hot indicators to time embeddings, with routine embeddings fused into the diffusion network's bottleneck (Haastregt et al., 4 Dec 2025).

• ProMP Conditioning: ProMP weights are learned from a low number of demonstrations and conditioned offline:

$$y(t) = \Phi(t)^\top w + \epsilon,\quad w \sim \mathcal{N}(\mu_w, \Sigma_w)$$

with real-time conditioning via Bayesian update on partial observation (Penco et al., 1 Nov 2024).

• DMP Arbitration: DMPs adhere to

$\tau \ddot{y} = K_p (g - y) - K_d \dot{y} + f(s)$

where policy blending is implicit in the attractor dynamics without manual tuning (Maeda, 2022).

4. Implementation Details and System Architectures

TAP instantiations utilize diverse control and perception stacks:

• Visual Encoding and Policy Fusion: Hybrid-Diffusion tap networks employ ResNet-34 vision trunks, temporal transformers or LSTMs for history fusion, and U-Net-based denoisers for action/TAP prediction. Each TAP event is discrete and encoded for explicit policy attention (Haastregt et al., 4 Dec 2025).
• Trajectory Primitive Generation: Multirotor systems generate polynomial or arc-based trajectories at 10–30 Hz, performing hierarchical map resolution adaptation and coarse-to-fine collision checking to guarantee safety at compute-constrained frequencies (Goel et al., 2022, Spitzer et al., 2019).
• MR and Shared Control Interfaces: TAPs are exposed to users via MR HUDs with object-centric proxies, waypoint previews, and explicit “Assist” toggles; blending is visualized and validated before final autonomous execution (Penco et al., 1 Nov 2024).
• Blending and Arbitration Module: State estimation, hypothesis tracking, and DMP evolution operate in parallel, with continuous or alternated blending based on phase and user activity, and goal selection determined by angular or cost-based heuristics (Maeda, 2022).

5. Experimental Validation and Quantitative Impact

A variety of benchmarks demonstrate TAP efficacy:

Task/Domain	Key Metric Improvement	Source
Vial Aspiration (Manipulation)	Success +5% (57→62%) w/ Hybrid-Diffusion TAPs	(Haastregt et al., 4 Dec 2025)
Container Unscrewing (Manipulation)	Success +29% (38→67%) w/ open-loop routine TAPs	(Haastregt et al., 4 Dec 2025)
Multirotor Flight (Fixed- vs. Adaptive-Δ)	Task time ↓ 25% (137→103 s), zero collisions	(Goel et al., 2022)
Humanoid Door Opening (Teleop, MR-Aided)	Completion time ↓ ~50%, failure eliminated	(Penco et al., 1 Nov 2024)
Simulated Reaching (DMP policy blending)	Human input time ↓ 60% vs. pure teleop	(Maeda, 2022)

Experimental results indicate that open-loop and template TAPs yield large improvements in tasks where pure closed-loop human control is imprecise, slow, or limited by perception-action mismatch. Gains are especially pronounced in repetitive, high-precision, or long-horizon sub-tasks (e.g., unscrewing, precise perching, blended affordance-based grasping) (Haastregt et al., 4 Dec 2025, Penco et al., 1 Nov 2024). Adaptive primitive-based control reduces workload and increases task speed while preserving safety, as evidenced in multirotor navigation through clutter, where collision-adaptive TAP libraries outperform fixed-envelope baselines (Goel et al., 2022, Spitzer et al., 2019). Blending primitives in a shared-control paradigm substantially reduces operator intervention without measurably affecting speed or success, highlighting the implicit arbitration advantage (Maeda, 2022).

6. Design Considerations, Limitations, and Extensions

Several extensions and caveats are documented for TAP frameworks:

• Closed-Loop TAPs and Automatic Discovery: Embedding feedback controllers or learned subpolicies as closed-loop TAPs can further enhance robustness. Automatic mining of demonstration data to discover repetitive TAP candidates could expand applicability, especially where operator bottlenecks are unpredictable (Haastregt et al., 4 Dec 2025).
• Hierarchical and Modular Composition: Chaining primitives within learned or programmed hierarchical planners supports longer-horizon, multi-stage tasks, e.g., sequencing perching, grasping, and manipulation routines (Haastregt et al., 4 Dec 2025, Penco et al., 1 Nov 2024).
• Generalization and Transferability: TAP parameterizations should support deployment across similar tasks or platforms—object-centric definition and frame-agnostic routines are recommended for cross-domain generalization (Haastregt et al., 4 Dec 2025, Penco et al., 1 Nov 2024).
• Safety and Out-of-Distribution Gating: Incorporating OOD detection to veto TAP/switch activations outside the demonstration envelope can prevent catastrophic execution errors in unmodeled states (Haastregt et al., 4 Dec 2025, Penco et al., 1 Nov 2024). Some approaches, particularly DMP blending, are limited by the need for known goal candidates and may require task-specific gain tuning (Maeda, 2022).
• Limitations: TAP efficacy is reduced if primitives are poorly parameterized, operator acceptance mechanisms are misconfigured (e.g., MR inattention), or if speed adaptation is not dynamically considered (as in some ProMPs) (Penco et al., 1 Nov 2024). Arbitration in DMPs is purely implicit and may not optimally scale with varying user skill or task complexity (Maeda, 2022).

7. Related Approaches and Comparative Analysis

TAPs generalize and unify several trends in teleoperation augmentation:

• Policy Blending and Shared Control: TAPs formalize the blending of human and autonomous policy contributions previously instantiated as arbitration parameters, cost-based planning, or disturbance rejection. The movement-primitives approach (DMPs, ProMPs) supports principled blending and correction without requiring high demonstration counts or extensive tuning (Penco et al., 1 Nov 2024, Maeda, 2022).
• Motion Libraries and Primitive Synthesis: Classic motion-primitive approaches, as in high-speed multirotor control, create TAPs at the trajectory level—ensuring feasibility, safety, and dynamic consistency under operator input with embedded collision-aware selection (Goel et al., 2022, Spitzer et al., 2019).
• Object- and Affordance-Based Assistance: TAP methodologies increasingly incorporate object-centric templates and affordance constraints, leveraging perception modules for online localization and trajectory transformation, with adaptive conditioning or explicit handoff at task-relevant junctures (Penco et al., 1 Nov 2024).
• Learning-Augmented Execution: Integration with deep learning architectures (e.g., diffusion policies) allows end-to-end learning of TAP trigger conditions, leveraging both open-loop routines and closed-loop correction, and supports seamless human-overridden execution (Haastregt et al., 4 Dec 2025).

TAPs thus provide a systematic framework for encapsulating, blending, and arbitrating among diverse sub-behavioral routines within teleoperation and shared-autonomy systems, yielding modular, safe, and operator-relief-aligned augmentation across a spectrum of robotics domains.