Human-Robot Contact Force MPC — Safe Collaboration Through Predictive Force Control

Public reference use case (by others): This page summarizes publicly available reference implementations and papers. Not client results of Dr. Rafal Noga.

Why This Matters (Executive Summary)

• Collaborative robots operating near humans risk injuring workers if collision forces exceed safe thresholds (ISO/TS 15066 defines body-region limits). Traditional controllers treat contact as a fault; MPC treats it as an optimizable constraint.
• MPC-based contact force control reduces peak collision forces by up to 77% (65 N to 15 N in reported DLR experiments) while maintaining task feasibility and motion tracking accuracy.
• Hybrid motion/force MPC enables robots to perform contact-rich tasks (wiping, guiding, grasping, door opening) while safely handling unexpected human contact — position RMSE stays below 1.2 cm even during disturbance events.
• For DACH manufacturers deploying cobots on production lines, this pattern provides a principled, auditable safety framework that directly maps force limits to optimizer constraints — no ad-hoc gain tuning required.

The Design Pattern Explained

Contact force MPC treats physical interaction not as a disturbance to reject, but as a first-class variable to optimize. The controller simultaneously tracks position trajectories and regulates contact forces, switching or blending modes based on the detected contact state.

Why MPC over alternatives? Classical impedance controllers set a fixed relationship between position error and force. MPC goes further: it predicts contact evolution over a receding horizon, enforces hard force limits as constraints (not soft penalties), and optimally trades off task speed against contact safety. This is especially valuable when ISO/TS 15066 limits must be guaranteed — the optimizer can prove force feasibility before commanding motion.

Architecture: The typical pipeline runs: (1) contact/force sensing or estimation, (2) predictive model of contact dynamics (sometimes with model switching between free-motion and contact regimes), (3) constrained optimization at 50-500 Hz enforcing force ceilings, and (4) torque-level execution on the robot joints.

Applications & Reference Implementations

Application 1: Collision Force Limiting on a 7-DoF Lightweight Arm — Industrial Safety

Researchers at DLR implemented contact-feedback MPC on a 7-DoF KUKA LWR IV+ lightweight robot. The controller monitors contact forces in real time and modifies the planned trajectory to keep peak forces below a defined ceiling. In collision-limiting tests, the maximum contact force without MPC feedback was 65.87 N; with MPC enabled, the peak dropped to 14.74 N — a 77% reduction. The force constraint was set at 15 N, and the controller maintained feasibility throughout. This demonstrates that MPC can enforce ISO/TS 15066-style body-region limits directly in the optimization loop. ¹

Application 2: Hybrid Motion/Force Control Robust to Human Contact — Contact-Rich Tasks

The same DLR platform was used to validate hybrid motion/force MPC, where the robot regulates end-effector force against a surface while a human unexpectedly touches the robot body. Without additional contact, position RMSE was 0.86 cm and force RMSE 0.58 N. With unexpected human contact, these degraded gracefully to 1.14 cm and 0.89 N respectively. Final force regulation achieved 0.20 cm position RMSE and 0.10 N force RMSE. This shows the controller can maintain task-level force tracking even under unmodeled disturbances from human interaction. ¹

Application 3: Scenario-Based NMPC for Shared Workspace Safety — Cobot Coexistence

A Control Engineering Practice paper from IMT Lucca proposes scenario-based NMPC with probabilistic human-motion predictions for shared workspaces. The approach uses higher-order Markov chains to build a scenario tree of likely human trajectories, then modulates robot speed so it can always stop before collision. Experiments on a Kinova Gen3 robot interacting with a human operator showed superior performance compared to both an NMPC scheme without human predictions and a fixed-path speed-and-separation monitoring strategy. The key advantage: the robot avoids unnecessary slowdowns when the human is predicted to move away, increasing throughput without sacrificing safety. ²

Application 4: Tactile-Reactive Grasping at 25 Hz — Delicate Object Handling

LeTac-MPC combines GelSight tactile sensing with a differentiable MPC layer to achieve reactive grasping at 25 Hz control frequency. In dynamic shaking tests, the system achieved 8/10 successful grasps compared to 2/10 for open-loop control. Under obstacle collision scenarios, success improved to 10/10 versus 3/10 open-loop. The tactile embedding feeds directly into the MPC optimization, enabling the controller to adjust grip force in real time without crushing delicate objects. This pattern is directly relevant to handling fragile components in electronics or food manufacturing. ³

Application 5: Adaptive MPC for Door Opening — Mobile Manipulation

ETH Zurich researchers combined MRAC (Model Reference Adaptive Control) with MPC for door-opening tasks on a mobile manipulator. The adaptive layer compensates for unknown door dynamics (weight, friction, spring force), while MPC plans the opening trajectory. On a light door, RMSE dropped from 6.7 degrees (baseline) to 1.4 degrees (MRAC+MPC). On a heavy door, RMSE improved from 3.2 to 1.6 degrees. Applied forces were maintained at 10-15 N for light doors and 20-25 N for heavy doors, with the controller adapting online to the changing dynamics without manual retuning. ⁴

Application 6: Model Predictive Interaction Control — Industrial Hand-Guiding and Wiping

MPIC (Model Predictive Interaction Control) uses model switching between free-motion and contact regimes to handle tasks like hand-guiding and table wiping on a 6-joint lightweight robot arm. The MPC runs with a 0.5 s prediction horizon at 50 discretization steps, predicting contact transitions and adjusting behavior preemptively. This eliminates the instability often seen when conventional controllers abruptly switch between position and force modes during contact/release events. ⁵

Application 7: Disturbance-Rejection MPC for Exoskeletons — Medical Mechatronics

A Scientific Reports paper reports disturbance-rejection MPC for lower-limb exoskeleton position control. The approach incorporates a disturbance estimator into the MPC framework, enabling the controller to anticipate and compensate for patient-induced perturbations. Virtual experiments demonstrated over 34% improved control accuracy compared to a baseline controller. For rehabilitation and assistive device manufacturers, this pattern reduces the risk of uncomfortable or unsafe joint tracking errors during therapy sessions. ⁶

What This Means for Your Operations

• If you deploy cobots on production lines, contact force MPC provides an auditable, constraint-based safety guarantee that maps directly to ISO/TS 15066 limits — replacing heuristic safety zones with mathematically provable force bounds.
• Readiness indicators: You need force/torque sensing (or estimation from motor currents), a dynamic model of the robot, and a real-time compute platform capable of 50+ Hz optimization cycles. Most modern cobot platforms (Universal Robots, KUKA iiwa, Franka Emika) provide the sensor infrastructure.
• The pattern scales from single-arm cobots to mobile manipulators performing door opening, bin picking, or surface finishing — any task where contact is intentional and must be precisely managed.

How We Deliver This (Engagement Model)

• Phase 0: NDA + data request — we review your robot platform, sensor setup, and safety requirements
• Phase 1: Fixed-scope discovery — concept feasibility, force model identification, constraint specification (typically 4-6 weeks)
• Phase 2: Implementation + validation + commissioning — MPC design, solver integration, hardware-in-the-loop testing
• Phase 3: Monitoring + training + scaling — operator training, parameter handover, extension to additional cells

Typical KPIs to Track

• Safety: Peak contact force (N), ISO/TS 15066 compliance margin, collision detection latency
• Task performance: Position tracking RMSE (mm), force regulation RMSE (N), cycle time
• Robustness: Success rate under disturbances, adaptation time to new payloads/objects
• Operator burden: Manual tuning interventions per shift, safety incident rate

Risks & Prerequisites

• Model fidelity matters: Contact dynamics models (stiffness, damping) must be identified or estimated online. Poor models degrade force prediction accuracy.
• Sensor requirements: Force/torque sensing at the end-effector or joint level is essential. Tactile skins improve performance but add integration complexity.
• Real-time compute: The MPC must run at 25-500 Hz depending on the application. Embedded platforms need sufficient compute headroom.
• Certification path: While MPC can enforce ISO/TS 15066 constraints mathematically, the certification process for safety-critical systems requires additional validation (e.g., worst-case solver timing guarantees).

FAQ

Q: Can this work with our existing Universal Robots or KUKA iiwa cobots? A: Yes. Both platforms provide the joint torque sensing and real-time interfaces needed for contact force MPC. The MPC runs on an external real-time PC connected via the robot’s low-level control interface.

Q: How does this compare to simply reducing robot speed for safety? A: Speed reduction is the simplest approach but sacrifices throughput. Contact force MPC allows the robot to move at full speed in free space and only modulate behavior when contact is predicted or detected — typically recovering 30-50% of the cycle time lost to blanket speed limits.

Q: What if the contact model is wrong? A: Adaptive variants (like MRAC+MPC) update the contact model online. Additionally, the constraint formulation provides inherent robustness — even with model mismatch, the force ceiling is enforced as a hard constraint, not a soft target.

Q: What is the typical implementation timeline? A: For a single cobot cell with known payload and contact scenarios, expect 8-12 weeks from discovery to validated deployment, including model identification, MPC design, and on-site commissioning.

Book a 30-Minute Discovery Call

Ready to explore whether this pattern fits your system?

Dr. Rafal Noga — Independent APC/MPC Consultant

Email me | noga.es

Fixed-scope discovery . NDA-first . DACH on-site available

Public References

Footnotes

1. Hamad et al., “Contact Feedback MPC” (ICRA 2024, DLR). https://elib.dlr.de/208582/1/IEEE_ICRA_2024__Contact_Feedback_MPC_copyright.pdf ↩ ↩²

2. Bemporad et al., “Safe Human-Robot Workspace Sharing via Scenario-Based Nonlinear MPC” (Control Engineering Practice). https://cse.lab.imtlucca.it/~bemporad/publications/papers/cep-smpc-robot.pdf ↩

3. Xu & She, “LeTac-MPC: Learning MPC for Tactile-Reactive Grasping” (arXiv, 2024). https://arxiv.org/html/2403.04934v1 ↩

4. “Adaptive Interaction Control for Robot Door Opening” (ETH Zurich, 2021). https://arxiv.org/pdf/2106.04202 ↩

5. Gold et al., “Model Predictive Interaction Control for Industrial Robots” (IFAC PapersOnLine, 2020). https://www.ifac-papersonline.net/article/S2405-8963(20)31305-4/pdf ↩

6. “Disturbance-Rejection MPC for Exoskeleton Position Control” (Scientific Reports, 2023). https://www.nature.com/articles/s41598-023-43344-3 ↩