Force-Grounded Manipulation Techniques

• Force-grounded manipulation is a framework where force and torque signals are primary control targets, enabling robust, adaptive behavior in dynamic, contact-rich tasks.
• Architectures combine hybrid force–motion control, force-centric MDPs, and multi-modal sensor fusion to deliver human-like dexterity and reliable error correction.
• Empirical results show substantial performance gains in assembly and manipulation tasks, with improved success rates and zero-shot policy transfer across diverse platforms.

Force-grounded manipulation refers to robotic or virtual manipulation techniques and learning frameworks in which the control, perception, and/or policy representation are explicitly grounded in force and torque signals—rather than being limited to purely kinematic trajectories or vision-based goals. In force-grounded systems, time-varying wrenches (forces and moments) at the robot–environment interface serve as first-class control and learning targets, enabling robust, adaptive, and human-like behavior in contact-rich, deformable, and dynamic manipulation scenarios. This paradigm underlies a growing class of policy learning, demonstration, simulation, and control architectures, and it is increasingly central for advancing dexterity, safety, and robustness in both physical and simulated manipulation platforms.

1. Principles and Definitions of Force-Grounded Manipulation

In force-grounded manipulation, the central tenet is the explicit incorporation of force and torque variables into the system's action, state, and feedback loops, spanning both the data collection (demonstration), policy learning, and execution phases. This contrasts with traditional methods that focus on pose or trajectory tracking, where interaction forces are at best implicit consequences of motion but not directly sensed or regulated.

Key system-level features include:

• Direct force/torque sensing at the interface: Either with 6-axis F/T sensors, distributed tactile arrays, or inference from high-dimensional tactile images.
• Action space in force (or hybrid force–position) domain: Policies command, or condition on, desired wrenches as well as, or instead of, positions or velocities.
• Learning objectives incorporating force fidelity: Policy losses include explicit matching of target vs. demonstrated force trajectories.
• Control schemes stabilized by force feedback: Hybrid or pure force controllers close the loop, often using admittance, impedance, or projection-based hybrid control laws.

This approach enables policies that generalize across variable contact dynamics, objects, and platforms, and allows for reliable behavior in tasks where force regulation is critical (e.g., insertion, peeling, screw tightening, object placement) (Liu et al., 2024, Helmut et al., 15 Oct 2025, Fang et al., 25 Feb 2026).

2. Architectures and Learning Frameworks

Multiple architectural motifs underpin force-grounded manipulation frameworks:

• Demonstration capture with force fidelity: Handheld rigs (e.g., ForceCapture, UMI) decouple hand/tool forces, use synchronized kinematic and wrench streams at high rates (≥1 kHz wrench, >200 Hz pose), and correct for gravity/inertia effects (Liu et al., 2024, Lee et al., 23 Sep 2025).
• Policy representations:
  • Force-centric MDPs: The system state includes both pose and wrench, e.g., $s_t = [x_t ; f_t] \in \mathbb{R}^{12}$ (Liu et al., 2024).
  • Global-local decoupling: Global vision policies plan free-space movements (low frequency), while local force policies regulate contact (high frequency) using estimated interaction frames and hybrid control (Fang et al., 25 Feb 2026).
  • Diffusion or transformer-based policies: Architectures predict future sequences of both poses and wrenches, conditioned on point clouds or multi-modal embeddings fused from visual, tactile, and force streams (Liu et al., 2024, Lee et al., 23 Sep 2025, Helmut et al., 15 Oct 2025).
  • Hybrid control decoupling: Actions are partitioned orthogonally to contact direction, often using interaction frame estimation to split force vs. motion DOFs (Fang et al., 25 Feb 2026).
• Learning algorithms:
  • Imitation learning with force–motion losses: Combined objectives match both demonstration poses and interaction forces (Liu et al., 2024).
  • Reinforcement learning in force space: Policies output 3D force vectors applied to object-centric features, enabling robot-agnostic skills and sim-to-real transfer (Fang et al., 17 Mar 2025).
  • Sensor-invariant force encoders: Models such as UniForce learn unified latent force spaces to bridge diverse tactile sensors and enable zero-shot policy transfer (Chen et al., 1 Feb 2026).

3. Experimental Systems, Hardware, and Sensing

A variety of physical and virtual platforms have implemented force-grounded manipulation:

• Handheld demonstration rigs: ForceCapture (6D F/T + SLAM camera), UMI gripper (F/T and pose, integrated tactile sensing), and ManipForce (rack-and-pinion with high-frequency AIDIN F/T) enable natural human demonstrations (Liu et al., 2024, Lee et al., 23 Sep 2025, Helmut et al., 15 Oct 2025, Engelbracht et al., 4 Dec 2025).
• Robotic platforms: Dual-arm Flexiv, UR5, Kinova, Panda, and whole-body humanoids have demonstrated closed-loop force-grounded control and learning (Liu et al., 2024, Fang et al., 17 Mar 2025, Zhang et al., 10 May 2025).
• Tactile sensor arrays: GelSight (optical), TacTip (magnetic), uSkin (magnetic), and custom skin-like magnetic sensors for aerial grasping (Chen et al., 1 Feb 2026, Hoi et al., 9 Feb 2026).
• Virtual and hybrid environments: Unity/PhysX simulations and VR platforms for grip force learning (Han et al., 11 Mar 2025), and the Hoi! multimodal dataset for articulated manipulation with ground-truth force and tactile labels (Engelbracht et al., 4 Dec 2025).

Sensor system integration is a key challenge, involving precise temporal synchronization, gravity/inertia compensation, and cross-modal alignment between force, visual, and tactile streams to enable high-fidelity learning and control.

4. Force-Grounded Control and Policy Execution

Execution of force-grounded manipulation relies on hybrid control and explicit force feedback:

• Hybrid force–motion control: Low-level controllers combine position and force commands,

$u = K_p(x_d - x) + K_f(f_d - f)$

often projected orthogonally to the nominal motion direction to achieve both trajectory following and stable force regulation (Liu et al., 2024, Fang et al., 25 Feb 2026).

• Interaction frame decomposition: The local contact stiffness is spectrally decomposed to identify subspaces for force vs. motion regulation, with learned or recovered interaction frames $\Sigma$ dictating which axes are under force or position control (Fang et al., 25 Feb 2026).
• Admittance and impedance control: For deformable or dynamic environments (e.g., aerial grasping), admittance-type controllers stabilize position and grasp force using compliant, energy-based laws (Hoi et al., 9 Feb 2026, Liu et al., 2024).
• Task switching via contact or force events: Policies monitor force cues to trigger transitions between free-space motion and contact-rich local control, enabling robust behavior under uncertainty, object variability, and sensor noise (Fang et al., 25 Feb 2026, Liu et al., 2024).

5. Empirical Impact, Performance, and Benchmarking

Force-grounded systems consistently yield large empirical gains across contact-rich benchmarks:

• Success rate improvements: Explicit force modeling raises continuous successful manipulations by substantial margins—e.g., vegetable peeling from 55% (vision-only) to 85% (force-grounded) (Liu et al., 2024), box rotation errors halved via FDLC models (Lee et al., 3 Feb 2026), insertion tasks from 0% (vision-only) to 95% (force-aware) (Helmut et al., 15 Oct 2025).
• Generalization and transfer: Policies operating in force space transfer with zero-shot generalization across diverse sensors (Chen et al., 1 Feb 2026), objects (Fang et al., 17 Mar 2025), and robot morphologies (Fang et al., 17 Mar 2025, Zhang et al., 10 May 2025).
• Robustness to dynamics and multi-stage errors: Feedback grounded in force enables real-time error correction (object placement under pose noise (Lerner et al., 2024), dynamic load compensation in aerial grasping (Hoi et al., 9 Feb 2026), or slip/force-triggered state machines (Süberkrüb et al., 2023)).
• Data efficiency: Force-aligned representations reduce demonstration requirements by up to 2× compared to vision-only baselines (Huang et al., 28 Jan 2026).

A selection of empirical results across representative tasks is summarized below:

Task	Vision Only (%)	Force-Grounded (%)	Citation
Vegetable peeling (>10cm)	55	85	(Liu et al., 2024)
Gear Assembly (FMT)	35	95	(Lee et al., 23 Sep 2025)
Plant insertion	85	95	(Helmut et al., 15 Oct 2025)
Grape picking	0	95	(Helmut et al., 15 Oct 2025)
Object placement (basic)	~0	100	(Lerner et al., 2024)

Success rates and control metrics consistently support the claim that force-grounded architectures deliver both more reliable and more generalizable behavior across diverse manipulation settings.

6. Applications and Extensions

Force-grounded manipulation is foundational for a wide spectrum of applications:

• Contact-rich assembly and insertion: Gear assembly, plug insertion, battery assembly, and complex electronics manufacturing require fine-grained force regulation to avoid jamming, misalignment, or damage (Lee et al., 23 Sep 2025, Liu et al., 2024, Helmut et al., 15 Oct 2025, Fang et al., 25 Feb 2026).
• Deformable object and environment interaction: Branch manipulation in agriculture, cable harness assembly with tension-only feedback, and compliant interaction with humans or fragile structures (2503.07497, Süberkrüb et al., 2023).
• Whole-body and aerial manipulation: Robust loco-manipulation under significant external disturbances (payloads, doors, carts) (Zhang et al., 10 May 2025), real-time force-aware grasping for quadrotors (Hoi et al., 9 Feb 2026).
• Teleoperation and dataset generation: Datasets such as Hoi! provide force-grounded multimodal data for benchmarking, transfer, cross-embodiment analysis, and simulation-to-real studies (Engelbracht et al., 4 Dec 2025).
• Sim-to-real transfer and robot generalization: Robot-agnostic policies trained purely in object-centric force space bypass sim-to-real mismatch and facilitate cross-platform deployment (Fang et al., 17 Mar 2025).

A plausible implication is that as force-grounded frameworks mature, they will be indispensable for physically robust and semantically aware manipulation in unstructured, uncertain, and dynamic environments.

7. Outlook, Limitations, and Future Directions

Contemporary research highlights several open challenges and avenues:

• Limitations: Many methods assume ideal or known contact points, linear environment elasticity, or ideal stiffness maps. Torque and contact localization, non-reversible or destructive tasks, and high-frequency force regulation expose remaining deficiencies (Fang et al., 25 Feb 2026).
• Integration with high-level semantics: Combining force-grounded control with language, vision, and task abstraction to deliver physically-grounded, semantics-aware agents (Huang et al., 28 Jan 2026, Lee et al., 23 Sep 2025).
• Hardware and bandwidth: Expanding to capacitive, piezoresistive, and distributed sensor arrays, with higher spatial and temporal resolution, as well as integrating with lightweight, low-cost platforms (Chen et al., 1 Feb 2026, Hoi et al., 9 Feb 2026).
• Unified multi-modality and interpreter learning: Vision, tactile, and force representations trained jointly for full cross-modal transfer and robust policy deployment (Huang et al., 28 Jan 2026, Engelbracht et al., 4 Dec 2025).
• Precision and real-time feedback: Improving noise floor, latency, and control architecture to guarantee high-frequency and stable force regulation in rapid or highly dynamic interactions (Liu et al., 2024, Hoi et al., 9 Feb 2026).

Overall, force-grounded manipulation establishes a rigorous, physically interpretable, and robust foundation for the next generation of dexterous robotic and virtual manipulation, with growing impact across both research and deployed systems.