Force-Sensitive Manipulation Tasks

Force-sensitive manipulation tasks involve deliberate control of contact forces by robotic systems to achieve reliable, safe, and versatile interaction with physical environments. In such tasks, force is not merely a byproduct of position tracking but an essential variable—integral to robust grasping, delicate object handling, insertion, assembly, and more generally, any context where uncertain or variable contacts play a critical role. The following sections characterize the conceptual landscape, methodologies, and technical achievements underpinning force-sensitive manipulation, anchored in recent research.

1. Principles of Force-Sensitive Manipulation

Force-sensitive manipulation relies on the ability to sense, estimate, and control interaction forces during contact-rich tasks. Unlike pure position control, which prescribes trajectories irrespective of encountered disturbances, force control allows the robot to adapt its behavior dynamically—absorbing unexpected impacts, adapting compliance for safety, and ensuring suitable contact maintenance or release.

Core control paradigms include:

• Variable Impedance Control: Adjusts stiffness and damping (impedance) parameters on the fly, letting robots increase compliance (softness) during uncertain interactions or become stiffer when precise force application or trajectory tracking is essential (Bogdanovic et al., 2019 ).
• Explicit Force Control: Commands desired contact wrenches directly or through hybrid force/position controllers, allowing for accurate force trajectories in tasks like insertion or surface following (Portela et al., 2 May 2024 , Zhi et al., 27 May 2025 ).
• Action Space Design: The choice of action space—whether direct torque, desired position, or impedance/gain—profoundly affects robustness and tractability. Structured action spaces that decouple feedforward motion from feedback stiffness enable better performance under contact uncertainty (Bogdanovic et al., 2019 ).

2. Sensing, Estimation, and Learning Approaches

A diversity of sensing modalities is leveraged for force-sensitive manipulation:

• Direct Sensing: Traditional approaches use physical force/torque (F/T) sensors at the wrist or end effector, providing 6-axis wrenches; tactile arrays offer distributed local contact force estimation (Collins et al., 2022 ).
• Vision-Based Force Estimation: Recent methods estimate forces visually, such as by observing gripper and finger deformation through an external camera with deep neural networks, achieving accuracy sufficient for healthcare and service scenarios (Collins et al., 2022 ).
• Internal Signal-Based Estimation: Neural network approaches enable estimation of external wrenches based on internal robot signals (motor currents, positions, velocities) after suitable task-specific training, allowing retrofitting of force control to robots lacking physical F/T sensors (Shan et al., 2023 ).
• Hybrid Sensing and Learning: Systems combine tactile and proprioceptive signals, often using multimodal fusion (e.g., Mixture-of-Experts layers) or contact-prediction networks to adaptively weight sensory information depending on actual or imminent contact (He et al., 24 Nov 2024 , Yu et al., 28 May 2025 ).

Learning-based strategies include:

• Reinforcement Learning (RL): End-to-end training of controllers that directly or indirectly regulate forces, with recent progress in learning explicit force control policies even in legged robots and complex loco-manipulation (Portela et al., 2 May 2024 , Zhi et al., 27 May 2025 ).
• Inverse Reinforcement Learning (IRL) and Imitation Learning: Recovering reward functions and control policies from human or robotic demonstrations, enabling robust variable impedance skill transfer by learning in gain space rather than direct force space (Zhang et al., 2021 , Liu et al., 10 Oct 2024 ).
• Human Contact Modeling: Systems such as FeelTheForce (FTF) leverage annotated human tactile demonstrations (via instrumented gloves) to map human force application directly to robot policy training and closed-loop force tracking (Adeniji et al., 2 Jun 2025 ).

3. Robustness Under Uncertainty and Complex Contact

Robustness to model errors, contact location ambiguity, sensor noise, and physical disturbances is critical in real-world force-sensitive manipulation:

• Action Space Structuring: Variable impedance policies with reward regularization for physical interpretability enable robust contact handling and sim-to-real transfer (Bogdanovic et al., 2019 ).
• Explicit Uncertainty Modeling: Geometric frameworks—such as GeoDEx—model feasible force sets using planes, cones, and ellipsoids, enabling robust planning that accounts analytically for tactile sensor inaccuracy (Chen et al., 1 May 2025 ).
• Multimodal and Phase-Adaptive Fusion: Reactive policies dynamically fuse high-frequency force/torque signals and vision using contact prediction, gating in or out force cues depending on current and anticipated task phase (He et al., 24 Nov 2024 ).
• Online Force-Aware Replanning: Methods in agricultural robotics and cable manipulation adapt motion plans online in response to measured forces exceeding safe thresholds, triggering re-planning to stay within damage limits (Rijal et al., 10 Mar 2025 , Süberkrüb et al., 2023 ).

4. Applications and Demonstrated Capabilities

Force-sensitive manipulation enables new performance thresholds in numerous domains:

• Precision Assembly: Variable impedance and force estimation approaches permit tight-tolerance insertion (down to 100 μm clearance), pin-in-hole tasks, and gear assembly without physical F/T sensors (Shan et al., 2023 , Correll et al., 8 Feb 2024 ).
• Delicate and Dexterous Grasping: Compliant, tactilely instrumented grippers (e.g., fin-ray geometries with pneumatic or optical sensors) achieve nearly error-free handling of soft or fragile objects (chips, berries, eggs) (Shang et al., 23 Jun 2025 , Ford et al., 2023 ).
• In-Hand Manipulation and Extrinsic Tasks: Multi-modal (vision, force, tactile) policies enable rolling, pivoting, or flipping of objects within a gripper, maintaining correct slip regimes and avoiding lifting or dropping (Xu et al., 2023 , Chen et al., 1 May 2025 ).
• Healthcare and Domestic Tasks: Visual force estimation supports blanket grasping, limb cleaning, and blanket-pulling in assistive scenarios, with force closed-loop control to avoid excessive pressure (Collins et al., 2022 ).
• Manipulation of Deformable or Linear Objects: Pure force-based DLO (cable, branch) manipulation can be accomplished blindly in fixture-rich environments by keeping the object under tension and executing force-driven primitives (sliding, clipping, winding) (Süberkrüb et al., 2023 , Rijal et al., 10 Mar 2025 ).
• Text- and Language-Conditioned Robotic Manipulation with Force-Awareness: Visual-force goal prediction models, such as ForceSight and ForceVLA, interpret language instructions together with RGB-D and force-torque data to generate both kinematic and force actions, improving performance by more than 20% over strong vision-language baselines in insertion, peeling, and wiping tasks (Collins et al., 2023 , Yu et al., 28 May 2025 ).

5. Technical Formulations and Performance

Technical methods and mathematical models underpinning force-sensitive manipulation include:

• Impedance Control Laws: Joint or Cartesian PD control with state-adaptive gains, e.g.,

$$\bm{\tau} = \bm{K_p}(\bm{\xi}) (\bm{q}_{des}(\bm{\xi}) - \bm{q}) - \bm{K_d}(\bm{\xi}) \bm{\dot{q}}$$

with critical damping enforced by $\bm{K_d} = 2 \sqrt{\bm{K_p}}$ (Bogdanovic et al., 2019 ).

• Force-Equilibrium Plane and Uncertainty Ellipsoid: Analytic projection of contact force estimates onto the force-equilibrium (FE) plane, intersected with ellipsoidal uncertainty from tactile readings, ensures grasp and manipulation plans remain robust to noise (Chen et al., 1 May 2025 ).
• Regression and Slip Detection: Data-driven regression maps multi-channel air pressure readings in compliant gripper fingers to grip force, while analytical slip detectors leverage transient sensor frequency content to flag impending loss of grip within 100 ms (Shang et al., 23 Jun 2025 ).
• Policy Structures: Mixture-of-Experts (MoE) fusion modules with dynamic gating route sensory tokens (force, vision, language) to expert subnetworks during phase- and contact-aware manipulation (Yu et al., 28 May 2025 ).
• Reinforcement and Imitation Learning Equations: Joint loss functions and reward structures that couple force tracking, compliance, position error, and safety; e.g., hybrid force-position control primitives with orthogonal controller switching (Liu et al., 10 Oct 2024 , Zhi et al., 27 May 2025 ).

Empirical performance metrics across the literature illustrate the impact of force awareness:

• Success rates: E.g., 98.6% in delicate grasping with FORTE (Shang et al., 23 Jun 2025 ); 81–90% in vision-force instructed mobile manipulation (Collins et al., 2023 ); +54.5% success relative to vision-only imitation learning using force-centric demonstrations (Liu et al., 10 Oct 2024 ).
• Force errors: Sub-0.2 N RMSE over 0–8 N range in real-world parallel gripper fingers (Shang et al., 23 Jun 2025 ); <2 N RMSE in contact-rich NN-estimated wrench tasks (Shan et al., 2023 ).
• Speed and adaptivity: Tactile control pipelines running at >250 Hz support real-time gentle grasping and human-robot handover (Ford et al., 2023 ).
• Sim-to-real robustness: Variable impedance and gain-space policies show direct transfer to physical hardware without additional tuning (Bogdanovic et al., 2019 , Zhang et al., 2021 ).

6. Practical Limitations and Design Implications

Despite these advances, force-sensitive manipulation poses ongoing challenges:

• Sensor calibration/uncertainty: Tactile arrays and barometric sensors are less accurate than high-grade F/T sensors, facing drift, hysteresis, and temperature effects (Chen et al., 1 May 2025 , Shang et al., 23 Jun 2025 ). Robust algorithms must compensate or model such uncertainty explicitly.
• Modality integration: Naïve fusion of force and vision data can degrade performance unless adaptively controlled, e.g., through learned contact predictors or expert routers (He et al., 24 Nov 2024 , Yu et al., 28 May 2025 ).
• Task/alignment dependency: The utility of force feedback is task and phase-dependent; in certain manipulation scenarios, additional sensing modalities may be superfluous or even detrimental if not carefully filtered (Mir et al., 2020 ).
• Physical integration: Some designs require careful fabrication, e.g., air channel seals within compliant fingertips; or tuning for specific environmental characteristics (species-specific injury thresholds in agriculture) (Shang et al., 23 Jun 2025 , Rijal et al., 10 Mar 2025 ).

Practical implications for deployment include:

• Cost-effectiveness: Neural network force estimation using internal signals enables retrofitting existing fleets without hardware upgrades (Shan et al., 2023 ).
• Open-source software/hardware: Recent platforms and datasets are released open-source, facilitating benchmarking and replication within the research community (Collins et al., 2023 , Yu et al., 28 May 2025 , Adeniji et al., 2 Jun 2025 ).
• Adaptability and error recovery: Integration with 3D perception and symbolic planning allows high-level re-planning and error recovery based on force events in long-horizon tasks (Correll et al., 8 Feb 2024 ).

7. Future Directions

Active research avenues include:

• Broadening multimodal, phase-adaptive architectures for general-purpose dexterous manipulation (Yu et al., 28 May 2025 ).
• Extending robust force-aware policy architectures to new robot morphologies (e.g., legged, humanoid), contact-rich mobile manipulation, and high-speed applications (Portela et al., 2 May 2024 , Zhi et al., 27 May 2025 ).
• Scaling human demonstration learning with advanced tactile annotation and leveraging real-world, task-specific force traces for data-driven generalization (Adeniji et al., 2 Jun 2025 , Liu et al., 10 Oct 2024 ).
• Further integration of force-based geometric reasoning and optimization, with real-time feedback for safety-critical or rapidly varying manipulation contexts (Chen et al., 1 May 2025 , Rijal et al., 10 Mar 2025 ).

These trends underscore a growing consensus: closing the force-control loop—physically, algorithmically, and at the behavioral design level—is essential for achieving robust, versatile, and intelligent robotic manipulation in unstructured environments.