Cobot Safe pHRI Technology Landscape 2026 — PatSnap Eureka
Cobot Safe Physical Human-Robot Interaction 2026
Collaborative robots must guarantee physical safety during direct human contact — the central challenge blocking broader Industry 4.0 deployment. This landscape maps sensing architectures, motion planning, and emerging LLM-based safety controls across retrieved patent and literature records.
Five Sub-Domains Defining Safe Cobot-Human Interaction
Safe physical human-robot interaction (pHRI) in collaborative robotics spans five overlapping sub-domains: kinetic energy limitation and collision avoidance governed by ISO 10218-1/2 and ISO/TS 15066; perceptual safety monitoring via visual and tactile sensor fusion; real-time motion and trajectory planning; human intent and state inference; and simulation, digital twins, and extended reality safety validation.
The field has evolved through three discernible phases across retrieved records dating 2006–2025. Approximately 60% of retrieved records originate from the 2018–2021 development phase, when multimodal sensing architectures, formal safety verification, socio-technical risk models, and ergonomics-integrated design methods matured simultaneously.
A foundational review characterizes the challenge: ‘safety is a fundamental issue when talking about cobots, because there are requirements in terms of materials and design, kinetic limitations, and the implementation of sensors and algorithms that guarantee a safe workspace’ — including obstacle avoidance, human-machine interfaces, and cybersecurity protocols per ISO 10218 and ISO/TS 15066.
In this dataset, the patent assignee picture shows innovation distributed across a heterogeneous set of actors — automotive OEMs such as Honda Motor Co., Ltd., semiconductor platforms such as Intel Corporation, and dedicated cobot startups such as Collaborative Robotics — rather than concentrated in a single incumbent robotics manufacturer. Only 4 patent documents carry full assignee metadata in retrieved records.
Publication Activity and Technology Phase Distribution
Across retrieved records spanning 2006–2025, the dataset reveals three distinct innovation phases — Foundational (2006–2015), Development (2018–2021), and Emerging (2022–2025) — with the Development phase accounting for approximately 60% of retrieved records.
Retrieved Records by Innovation Phase — pHRI Dataset Snapshot
The Development phase (2018–2021) accounts for approximately 60% of retrieved records in this dataset, reflecting the maturation of multimodal sensing architectures, formal safety verification, and ergonomics-integrated design methods during that period.
↗ Click bars to explorePatent Filings by Assignee — pHRI Dataset Snapshot (Retrieved Records)
In this dataset, Collaborative Robotics accounts for 2 patent filings covering LLM-based cobot control, while Intel Corporation and Honda Motor Co., Ltd. each have 1 filing — all retrieved records date from 2024–2025, reflecting the nascent but accelerating patent activity in this sub-domain.
↗ Click bars to exploreKey Application Domains for Cobot Safe pHRI Technology
Safe physical human-robot interaction technology has been deployed and validated across four major application domains in this dataset — industrial manufacturing, healthcare and surgery, domestic and care environments, and logistics — each presenting distinct safety requirements and regulatory contexts.
Industrial Manufacturing and Assembly
The largest application cluster in this dataset, covering cobots in assembly, material handling, and order picking alongside human workers. A 2021 multi-criteria design method balances safety, ergonomics, productivity, and flexibility for smart factory deployment. A 2021 study establishes ergonomic requirements as design inputs — not outputs — for safer HRC workstations, per ISO 10218-compliant speed limiting and real-time workspace monitoring.
Industrial AutomationHealthcare and Surgical Robotics
Cobots in clinical and surgical settings face amplified safety requirements due to patient vulnerability, demanding sub-Newton force control. A 2021 study examined cobot integration into maxillofacial surgical workflows with human-centered workplace design validated via phantom studies. Honda Motor Co., Ltd.’s 2024 US patent introduces a constraint-prioritized wrench feedback system mediating physical contact between two humans via robot end-effectors.
Medical RoboticsDomestic and Care Environments
Safety in unstructured home environments presents distinct challenges including unpredictable human movement, non-expert users, and absence of industrial safety infrastructure. A 2021 study used ROS/Gazebo simulation to develop a speed-reducing reactive controller for domestic settings, and a 2020 risk-minimizing controller demonstrated pre-collision speed reduction in non-industrial simulations. A 2022 study introduced spatial AR visualization to communicate cobot perception to users with physical impairments.
Care RoboticsLogistics and Warehouse Operations
Order-picking cobots in high-volume distribution centers raise human factors and safety implementation challenges at scale. A 2021 four-company case study identified resistance to change and poor communication as the primary safety implementation barriers, placing organizational factors on par with unresolved technical problems in blocking cobot deployment in logistics environments.
Logistics AutomationKey Patent Assignees in Cobot Safe pHRI — Retrieved Records Snapshot
In this dataset, only 4 patent documents carry full assignee metadata, spanning three named assignees: Collaborative Robotics (US/WO, 2024) with 2 filings in retrieved records, Intel Corporation (DE, 2025) with 1 filing, and Honda Motor Co., Ltd. (US, 2024) with 1 filing. These filings in retrieved records represent the most recent and technically novel IP activity captured in this snapshot.
Patent Filings per Assignee in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreCollaborative Robotics
Collaborative Robotics holds 2 filings in retrieved records — a 2024 US patent and a 2024 WO/PCT filing — both covering the method of using large language models to generate human-readable discrete task sets that are audited before cobot execution, preventing unsafe commands from reaching the robot. The WO filing explicitly constrains LLM output to safety-verified code before cobot implementation. Both patents are pending as of the dataset snapshot date.
United States / WO-PCTIntel Corporation
Intel Corporation holds 1 pending filing in retrieved records — a 2025 DE patent — covering an ergonomic human-collaborating robot interaction system that integrates joint strain scores with object-grasp intent prediction to preemptively reposition the cobot and reduce musculoskeletal risk. This is the only retrieved filing explicitly treating musculoskeletal harm prevention as a cobot safety function, representing a significant white space in the IP landscape of this dataset.
Germany — DEFour Converging Frontiers in Cobot Safe pHRI (2022–2025)
The most recent records in this dataset (2022–2025) signal four converging directions: LLM-constrained safe task planning, ergonomic intent prediction, physics-based digital twin pre-deployment certification, and safe reinforcement learning with interactive behaviors — each representing a shift from hardware-only safety toward AI-governed behavioral safety.
LLM-Constrained Safe Task Planning
Collaborative Robotics’ 2024 US and WO patents establish a new safety architecture: LLMs generate human-readable task sequences that are audited by automated rule-based systems before any computer-readable code reaches the cobot. This ‘human-readable audit layer’ represents a fundamental shift from hardware-only safety to AI-governed behavioral safety. Competitors entering LLM-driven cobot control must design around this dual US/WO architecture or license it, making it a near-term IP chokepoint.
Ergonomic Intent Prediction as a Safety Input
Intel Corporation’s 2025 pending DE patent integrates joint strain scoring with object-grasp intent prediction, positioning the cobot to preemptively optimize placement and reduce musculoskeletal risk. This extends safety beyond collision avoidance into proactive ergonomic harm prevention. It is the only retrieved filing in this dataset explicitly treating musculoskeletal harm prevention as a cobot safety function, representing a significant IP white space.
Hardware-Layer vs. AI-Governed Behavioral Safety Approaches
Click any row to explore further.
| Dimension | Hardware-Layer Safety (Speed/Force Limiting) | AI-Governed Behavioral Safety (LLM/RL/Intent) |
|---|---|---|
| Primary Standard | ISO 10218-1/2, ISO/TS 15066 | No dedicated standard yet; builds on ISO 10218 framework |
| Core Mechanism | Speed/separation monitoring, force-torque limiting, compliant mechanical design | LLM-audited task planning, safe RL exploration, game-theoretic human models |
| Key Dataset Records | ISO/TS 15066 NMPC study (2022, 48 subjects); SEEROB digital twin (2022); power-and-force-limiting simulation (2018) | Collaborative Robotics LLM patents (2024, US/WO); safe RL framework (2023); Intel ergonomic intent patent (2025, DE) |
| Maturity Phase | Development phase (2018–2021); well-established in dataset | Emerging phase (2022–2025); most nascent cluster in dataset |
| Certification Pathway | Physics-based simulation (SEEROB), power/force parameter studies, established ISO compliance paths | Human-readable audit layer before code execution (Collaborative Robotics); formal verification still emerging |
| IP Status in Dataset | Primarily literature-documented; limited dedicated patents retrieved | 2 pending patents (Collaborative Robotics, US/WO); 1 pending patent (Intel, DE) |
| Key Adoption Barrier | Complex standards, lack of safety knowledge, organizational resistance (per 2019–2022 studies) | Auditability requirements, trust in AI decision-making, absence of AI-specific safety standards |
Frequently Asked Questions: Cobot Safe pHRI Technology
Safe pHRI in cobots is primarily governed by ISO 10218-1/2 and ISO/TS 15066. These standards cover kinetic energy limitation, speed/separation monitoring, force-torque limiting, and compliant mechanical design requirements. Multiple records in this dataset explicitly reference these standards, including the SEEROB digital twin framework, which computes force and pressure criteria specifically for ISO certification.
The dataset identifies five overlapping sub-domains: (1) kinetic energy limitation and collision avoidance; (2) perceptual safety monitoring via visual and tactile sensor fusion; (3) real-time motion and trajectory planning; (4) human intent and state inference including BCI and physiological signals; and (5) simulation, digital twins, and extended reality safety validation. Perceptual safety monitoring is the dominant cluster by retrieved record count.
Collaborative Robotics’ 2024 US and WO patents describe an architecture in which LLMs generate human-readable discrete task sets that are audited by automated rule-based systems before any computer-readable code reaches the cobot. This ‘human-readable audit layer’ prevents unsafe commands from reaching the robot and represents a shift from hardware-only safety to AI-governed behavioral safety.
Intel Corporation’s 2025 pending DE patent covers an ergonomic human-collaborating robot interaction system that integrates joint strain scores with object-grasp intent prediction. The system preemptively repositions the cobot to reduce musculoskeletal risk to the human operator. According to the dataset, this is the only retrieved filing explicitly treating musculoskeletal harm prevention as a cobot safety function.
The SEEROB framework, documented in a 2022 record in this dataset, uses a physics-based digital twin with an extended reality (XR) display to compute force and pressure criteria required for ISO certification of cobotic workstations. It enables safety and ergonomics evaluation before physical deployment, reducing costly real-world testing iterations. It is part of a broader cluster of simulation-based safety certification approaches in the dataset.
Multiple records in this dataset (2019–2022) identify non-technical barriers as equally significant. A 2021 four-company case study of high-volume distribution centres identified resistance to change and poor communication as primary safety implementation barriers. Broader studies cite lack of safety knowledge, complex standards, and organizational factors as equal or greater barriers to cobot deployment than unsolved technical problems.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.