Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model Predictive Control (MPC) has emerged as a suitable framework thanks to its capacity to handle hard constraints, provide strong guarantees and zero-shot adaptability through predictive reasoning. However, Gradient-Based MPC (GB-MPC) solvers have demonstrated limited performance for collision avoidance in complex environments. Sampling-based approaches such as Model Predictive Path Integral (MPPI) control offer an alternative via stochastic rollouts, but enforcing safety via additive penalties is inherently fragile, as it provides no formal constraint satisfaction guarantees. We propose a collision avoidance framework called COSMIK-MPPI combining MPPI with the toolbox for human motion estimation RT-COSMIK and the Constraints-as-Terminations transcription, which enforces safety by treating constraint violations as terminal events, without relying on large penalty terms or explicit human motion prediction. The proposed approach is evaluated against state-of-the-art GB-MPC and vanilla MPPI in simulation and on a real manipulator arm. Results show that COSMIK-MPPI achieves a 100% task success rate with a constant computation time (22 ms), largely outperforming GB-MPC. In simulated infeasible scenarios, COSMIK-MPPI consistently generates collision-free trajectories, contrary to vanilla MPPI. These properties enabled safe execution of complex real-world human-robot interaction tasks in shared workspaces using an affordable markerless human motion estimator, demonstrating a robust, compliant, and practical solution for predictive collision avoidance (cf. results showcased at https://exquisite-parfait-ffa925.netlify.app)
Figures & tables
Fig. 1 : Visualization of the NLF-based human mesh recovery [ 11 ] (left) and human collision capsules (rendered as cylinders only visually) and output of the inverse kinematics pipeline used to position collision primitives (right).
Fig. 2 : Experimental validation, reported in [ 21 ] , comparing RT-COSMIK human motion estimation (green) with a reference marker-based optical motion capture system (grey) during a collaborative human–robot scenario.
Fig. 3 : Views of the 6 scenarios used to assess collision avoidance abilities in simulation.The red and green dots are the alternative targets.
Sc.
Method
Success Rate
Dist. to Target (cm)
Dist. to Obst. (cm)
Smooth- ness
Comp. Time (ms)
1
GB-MPC
1.00
1.05
–
1.50
15
Vanilla MPPI
1.00
1.88
–
18.59
22
COSMIK-MPPI
1.00
1.98
–
20.14
22
2
GB-MPC
1.00
0.51
8.78
2.31
89
Vanilla MPPI
1.00
1.79
9.32
20.83
22
COSMIK-MPPI
1.00
1.62
10.65
17.54
22
TABLE I : Simulation comparison of GB-MPC, vanilla MPPI and COSMIK-MPPI for collision avoidance.
Fig. 4 : Five tasks used to assess COSMIK-MPPI safety and robustness for collision avoidance with with human. a) Static environment during baseline pick-and-place. b) Physical perturbations and compliance. c) Human-aware collision avoidance. d) Concurrent human activity. e) Highly constrained human–robot interaction.
Fig. 5 : Analysis of the minimum collision distance between the robot and the environment and the human with the Cartesian end-effector velocity during scenario 5.
This study proposes a novel Model Predictive Path Integral (MPPI)-based task-space control framework. The proposed framework explicitly solves rigid-body dynamics within a real-time MPC formulation and enforces safety constraints, enabling accurate motion and force control that yields compliant behaviors for safe and effective physical interaction of robotic manipulators in unstructured environments. By leveraging MPPI, the proposed framework efficiently handles nonlinear dynamics that are difficult to solve with conventional MPC approaches in real-time. Furthermore, we develop a torque-sampling-based control architecture that enables efficient exploitation of GPU-based parallelization, resulting in effective compliant and force-aware behaviors. As a result, the proposed framework achieves a solver update rate of over 166 Hz with a 0.18 s prediction horizon, and its performance is validated through real-world experiments on a 7-DoF manipulator.
Euncheol Im, Taehyun Kim, Yonghwan Oh +2
The Center for Humanoid Research, Korea Institute of Science and Technology (KIST), 02792 Seoul, South Korea · The School of Electrical Engineering, Korea University, 02841 Seoul, South Korea
Model Predictive Path Integral (MPPI) control is widely used in manipulation for its gradient-free, parallel handling of non-convex costs. Manipulation tasks, however, often impose constraints that hold throughout the motion: a closed kinematic chain that two grasping arms keep exactly, or joint limits and obstacle clearances that are never crossed. MPPI handles such constraints only through the cost, as soft penalties that hold approximately and fail under a strong task cost. To address this, we propose Projection-Retraction MPPI (PR-MPPI), which enforces the constraints inside the sampled dynamics. At every rollout step, the sampled velocity is projected to satisfy both constraint types: the equality restricts it to a subspace, and each inequality to a half-space within that subspace, so inequality handling never breaks the equality. This projection, however, satisfies the constraints only to first order, and a finite step leaves a small drift off the equality. Therefore, we retract the returned command back onto the constraint to numerical tolerance and independent of task weighting. We validate PR-MPPI on 14-DoF dual-arm systems. In simulation, the returned commands satisfy the closed-chain equality to numerical tolerance through a joint-limit stress test and randomized obstacle avoidance. On real hardware, the arms of a Unitree H1-2 humanoid reactively avoid a moving obstacle. Code and experiment videos are available at https://rcilab.github.io/prmppi.
Seulchan Lee, Leesai Park, Minhyeong Kang +1
School of Mechanical Engineering, Kyung Hee University, Yongin-si 17104, Republic of Korea · Institutes of Convergence Technology, Suwon-si 16229, Republic of Korea
Safe and socially compliant navigation remains a fundamental challenge for autonomous robots operating in human-populated environments. Beyond collision avoidance, robots must anticipate human motion and respect personal space to ensure human comfort. Model Predictive Control (MPC) offers a robust alternative to classical and data-driven methods, although its effectiveness strongly depends on accurate human motion prediction and efficient computation. This paper introduces SFM-NMPC, a Social Force Model-based Non-linear Model Predictive Control framework that embeds human motion prediction directly within the optimization loop. By incorporating the Social Force Model into the dynamic model of surrounding agents, the controller jointly predicts the trajectories of humans and robots over the prediction horizon, thereby enabling socially-aware planning. A tailored set of social cost functions guides the optimization toward human-compliant behaviors. Despite the increased model complexity, the proposed formulation runs in real time at 20 Hz. Extensive simulated testing in crowded environments demonstrates that SFM-NMPC outperforms state-of-the-art baselines in social compliance metrics while maintaining efficient and smooth navigation. Visual trajectory analysis and an ablation study further highlight the contribution of the embedded SFM dynamics and social cost terms, confirming the effectiveness of the proposed approach for real-world social navigation.
Stefano Trepella, Andrea Ostuni, Mauro Martini +5
Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Torino, Italy. · School of Engineering, Pablo de Olavide University, Crta. Utr-era km 1, Seville, Spain