COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments

• URL: http://arxiv.org/abs/2604.10358v1
• Date: Sat, 11 Apr 2026 22:04:22 GMT
• Title: COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments
• Abstract summary: Model Predictive Control (MPC) has emerged as a suitable framework thanks to its capacity to handle hard constraints.<n>GB-MPC solvers have demonstrated limited performance for collision avoidance in complex environments.<n>We propose a collision avoidance framework called COSMIK-MPPI combining MPPI with the toolbox for human motion estimation.
• Score: 4.711880574101941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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)

Related papers

• Real-Time Dynamics-Based Torque-Sampling MPPI for Compliant and Force Aware Manipulation [9.745912505259314]
  This study proposes a novel Model Predictive Path Integral (MPPI)-based task-space control framework.<n>The proposed framework explicitly solves rigid-body dynamics within a real-time MPC formulation and enforces safety constraints.<n>It yields compliant behaviors for safe and effective physical interaction of robotic manipulators in unstructured environments.
  arXiv  Detail & Related papers  (2026-09-02T02:46:57Z)
• Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
  This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
  arXiv  Detail & Related papers  (2026-07-22T16:37:05Z)
• AI-Augmented Model Predictive Control for Safe and Adaptive Rendezvous and Proximity Operations [2.787067333542931]
  This work presents an adaptive Model Predictive Control (MPC) framework for autonomous spacecraft RPO in multi-agent adversarial scenarios.<n>The framework was evaluated in the official Kerbal Space Program Differential Game (KSPDG) Capture-the-Satellite environment through Monte Carlo simulations.
  arXiv  Detail & Related papers  (2026-07-18T04:20:23Z)
• Manifold-Constrained MPPI: Real-Time Sampling-Based Control Under Hard Constraints [8.747309323431802]
  Manifold-Constrained MPPI (MC-MPPI) is a real-time sampling-based control framework.<n>It enforces manifold-based equality constraints while preserving the computational advantages of MPPI.<n>Experiments on a 14-DoF closed-chain dual-arm system in both simulation and real-world settings demonstrate that MC-MPPI operates stably at 100 Hz.
  arXiv  Detail & Related papers  (2026-05-24T01:57:18Z)
• CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection [64.65196237023754]
  Manifold Competence Projection improves robustness against Out-of-Distribution perturbations.<n>We employ a Frame-Wise Safety Scheme that transforms the infinite-horizon safety constraint into a computationally efficient single-step manifold inclusion.<n>Experiments demonstrate that CMP achieves up to a 10-fold survival rate improvement in typical OOD scenarios.
  arXiv  Detail & Related papers  (2026-04-08T18:00:39Z)
• ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation [20.83011691536596]
  The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios.<n>It also introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments.<n>Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints.
  arXiv  Detail & Related papers  (2026-03-14T13:35:24Z)
• Provably Safe Trajectory Generation for Manipulators Under Motion and Environmental Uncertainties [26.41382315967256]
  This article proposes a novel risk-bounded motion planning framework to address the unmet need for safe optimal motion planning.<n>Our approach integrates a rigid manipulator deep Koopman operator (RM-DeKO) model to predict the robot's state distribution under motion uncertainty.<n>We then introduce an efficient hierarchical verification method that combines parallel physics simulations with sum-of-squares (SOS) programming as a filter for fine-grained, formal certification of collision risk.
  arXiv  Detail & Related papers  (2026-03-10T01:48:48Z)
• HPTune: Hierarchical Proactive Tuning for Collision-Free Model Predictive Control [55.015741360766945]
  We propose a hierarchical proactive tuning (HPTune) framework that combines both a fast-level tuning and a slow-level tuning.<n>We integrate HPTune with the Doppler LiDAR that provides obstacle velocities apart from position-only measurements for enhanced motion predictions.<n>It is found that HPTune enables situation-tailored motion planning by formulating a safe, agile collision avoidance strategy.
  arXiv  Detail & Related papers  (2026-01-29T07:15:39Z)
• Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion [38.363402738466334]
  We show that combining diffusion-based warm-starting conditioned with a latent object-centric representation of the scene yields reliable and efficient motion generation under strict time limits.<n>Our approach conditions a diffusion transformer on the system state, task, and surroundings, using an object-centric slot attention mechanism.<n>The sampled trajectories are refined by an optimal control problem that enforces rigid-body dynamics and signed-distance collision constraints.
  arXiv  Detail & Related papers  (2026-01-06T10:02:54Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty [77.18483084440182]
  Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
  arXiv  Detail & Related papers  (2021-06-13T23:08:49Z)
• Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization [76.51980153902774]
  Federated learning (FL) is vulnerable to external attacks on FL models during parameters transmissions. In this paper, we propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.
  arXiv  Detail & Related papers  (2021-01-28T03:28:18Z)
• Uncertainty-aware Contact-safe Model-based Reinforcement Learning [17.10030262602653]
  We present contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. We associate the probabilistic Model Predictive Control's (pMPC) control limits with the model uncertainty so that the allowed acceleration of controlled behavior is adjusted according to learning progress.
  arXiv  Detail & Related papers  (2020-10-16T05:11:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.