Real-world endurance and field maintainability take center stage today. As robotics companies prepare to scale beyond short-run lab demonstrations, we are tracking breakthroughs in battery efficiency, hot-swappable hardware modularity, and open-source execution layers designed to keep robotic fleets moving without continuous engineering oversight.
Tokyo-based robotics startup O-ID secured $1.2 million in pre-seed funding on Thursday, September 24, in a round led by TAWANI Ventures alongside Hustle Fund and Techstars. Founded in 2025, the company is building a modular industrial humanoid platform engineered with hot-swappable joints, limbs, and compute units. Designed specifically for manufacturing and logistics facilities, the robot relies on modular component swapping and predictive AI wear monitoring to allow on-site maintenance crews to replace broken actuators in minutes. O-ID also signed an exploratory letter of intent with Sumitomo Electric Industries to collaborate on wire harness integration for its upcoming Japanese plant pilots.
Why it matters
Monolithic bipedal architectures carry severe operational risks: a single failed harmonic drive or burnt motor winding can sideline a $100,000+ machine for days, halting factory operations. O-ID's modular approach shifts the unit economics of industrial humanoids toward maintainability, directly tackling the high cost of unscheduled downtime. For enterprise robotics deployment, isolating mechanical failures to field-replaceable units lowers the skill floor required for factory maintenance staff. If validated in Japanese manufacturing pilots, this modular blueprint will set a clear design precedent for Western competitors targeting labor-starved industrial sectors.
O-ID founders Stian Jakobsen and Simon Gormuzov argue that modularity and rapid field repairs are mandatory to make bipedal automation cost-competitive with fixed industrial arms. Conversely, hardware traditionalists point out that modular quick-connect interfaces add parasitic structural weight, increase electrical contact resistance, and introduce mechanical play that can degrade joint positional precision.
As Tesla pushes to scale Optimus assembly at its Fremont plant, decompiled digital assets from a recent Android app update reveal production-ready design changes for the Gen 3 humanoid. The leaked renders show matte black fairings replacing gold-plated panels, integrated flexible joint covers, and thicker forearms housing relocated actuators. The updated end effectors incorporate approximately 50 motors per hand to deliver 22 degrees of freedom.
Why it matters
Fine-motor manipulation remains a primary physical bottleneck for general-purpose bipedal deployment. By moving finger actuators up into the forearm—mimicking human tendon mechanics—Tesla frees up palm space to pack in tactile sensors while increasing finger payload capacity. The appearance of finalized production assets inside public app builds indicates that Tesla is moving past experimental prototypes toward automated line assembly.
Tesla supply chain trackers view the relocated forearm drives and sealed joint covers as evidence of mass-manufacturability for automotive assembly environments. Conversely, industrial robotics engineers note that high-density tendon-driven mechanisms introduce cable stretch and wear over time, questioning whether these 22-DoF hands can withstand multi-shift factory abuse without frequent maintenance.
Yesterday we covered AGIBOT's milestone of producing its 20,000th humanoid and deploying over 300 units at Chimelong Spaceship Park. The rollout utilizes a custom 5G-Advanced private network built with China Mobile to connect all units to a centralized fleet orchestration engine, covering guest guidance, retail, and live entertainment. The companies have also established a joint research institute to study multi-robot coordination in high-density public venues.
Why it matters
While most humanoid deployments remain limited to small pilot trials in automotive plants, AGIBOT's 300-unit rollout represents one of the largest fleet deployments in an unstructured consumer environment to date. Reaching a 20,000-unit production threshold demonstrates genuine commercial manufacturing capacity. Operating hundreds of interactive robots in crowded spaces stress-tests real-time obstacle avoidance, fleet-wide fleet management, and public safety protocols under continuous daily use.
AGIBOT leadership framing the deployment as proof that general-purpose humanoids have achieved commercial scale outside factory floors. Independent market analysts counter that theme park deployments function largely as high-visibility promotional showcases subsidized by regional tourism initiatives, rather than self-sustaining commercial automation contracts.
An industry report published by DeepRadar on Friday, September 25, analyzed primary disclosures from the International Federation of Robotics, Unitree stock filings, and Agility Robotics SPAC documents. The investigation revealed a significant gap between corporate press announcements regarding humanoid factory deployments and actual contracted industrial revenue. The data shows that over 80% of humanoid units delivered in 2025 and early 2026 were sold to university research laboratories, internal AI data-collection fleets, and corporate showrooms, with fewer than 15% performing active, paid industrial tasks on factory floors.
Why it matters
Venture capital valuations in the bipedal robotics market have soared on claims of imminent mass factory automation. DeepRadar's analysis provides a reality check, highlighting that multi-thousand-unit order books often represent non-binding letters of intent or internal pilot allocations rather than paid commercial contracts. For hardware founders and investors, separating research sales from recurring industrial deployments is essential for setting realistic manufacturing ramp timelines.
Industry analysts behind the report emphasize that transparency regarding deployment metrics is critical to prevent a venture capital bubble in physical AI. Humanoid startup executives counter that placing platforms in research labs and corporate evaluation centers is a necessary prerequisite to train foundation models before committing machines to high-speed production lines.
China Mobile open-sourced its Open-RAIL deployment architecture on Thursday, September 24, releasing the codebase on GitHub. The software stack addresses the motion jerkiness caused by the frequency gap between slow Vision-Language-Action (VLA) inference and high-speed motor controllers. Open-RAIL decouples system execution into three independent threads: visual observation (30 Hz), model reasoning (5–10 Hz), and motor actuation (200 Hz–1 kHz). Coupled with a two-stage trajectory smoothing routine, the framework reported a 100-fold reduction in motion jerkiness, doubled physical execution speeds, and increased task success rates by 10% to 73% across NVIDIA GR00T and Physical Intelligence π0 models.
Why it matters
The 30x frequency mismatch between cognitive neural planners and low-level motor loops has historically forced engineers to artificially throttle robot movements, causing stuttered, robotic locomotion during live demonstrations. Open-RAIL delivers an open-source, hardware-agnostic solution that allows generalist VLA models to run on physical bipedal and arm hardware smoothly. By eliminating custom middleware development, this release accelerates the commercial transition of academic vision-language policies into responsive, fluid factory workers.
China Mobile engineers contend that threading decoupling and mathematical trajectory smoothing solve real-time control latency without altering underlying model weights. Conversely, control theory researchers argue that software-based smoothing masks fundamental lag in large reasoning models, warning that predictive tactile feedback cannot be safely managed through multi-threaded interpolation alone.
NVIDIA and Hugging Face announced an expansion of the open-source LeRobot robotics library on Thursday, September 24. The update directly integrates NVIDIA's Isaac GR00T 1.7 foundation model, Isaac Teleop workflows, and upcoming Cosmos 3 simulation utilities into the LeRobot framework. The collaboration establishes a unified, open-source pipeline spanning real-world egocentric data collection, cloud-based policy training, sim-to-real alignment, and edge deployment across bipedal and dexterous manipulation hardware.
Why it matters
Standardizing physical AI development tooling on Hugging Face's platform bridges the gap between web-scale AI developers and traditional robotics hardware engineers. By making enterprise-grade simulation assets and GR00T foundation checkpoints accessible within an open repository, NVIDIA lowers the technical barrier for training custom robot policies. This integration threatens proprietary software walled gardens by giving independent researchers standardized tools for cross-embodiment post-training.
Hugging Face and NVIDIA maintain that open-sourcing standardized data structures and teleoperation workflows will drastically accelerate global physical AI research. On the other hand, proprietary platform builders argue that generic open libraries lack the low-latency hardware integration necessary to guarantee sub-millisecond safety stops in heavy industrial environments.
Japanese autonomous driving leader TIER IV released its complete open-source Autoware racing-kart software stack on GitHub on Thursday, September 24. Developed for the 2026 Autonomous Driving AI Challenge, the release includes Docker containers, AWSIM simulation assets, and vehicle integration files for a physical TOM'S EV racing kart equipped with GNSS, IMU, and ROS 2 middleware. The repository provides a reproducible end-to-end development workflow bridging AWSIM simulation to physical kart hardware over Zenoh protocols.
Why it matters
Setting up a complete, production-grade autonomous vehicle software pipeline from scratch requires months of toolchain integration. TIER IV's release gives researchers and open-source developers a pre-validated, Dockerized reference implementation that connects simulation directly to physical drive-by-wire EV karts. Standardizing on ROS 2 and Zenoh lowers the development barrier for high-speed motion planning and edge localization algorithms.
TIER IV engineering leads highlight that providing open hardware schematics and simulation digital twins allows the broader community to stress-test autonomy stack edge cases safely. Independent AV developers note that while the stack is ideal for karting and small research rovers, adapting the architecture to heavy passenger or freight vehicles requires extensive restructuring of safety-critical control layers.
Yesterday we covered Black Forest Labs' release of the 7-billion-parameter FLUX 3 Action model. Additional details confirm the open-weight architecture achieved a 42.92% success rate on the RoboLab-120 benchmark, running up to 3.95 times faster than prior open alternatives. The model weights and training code are hosted on Hugging Face in partnership with Positronic Robotics, which verified a 93.3% real-world task success rate on a Franka arm.
Why it matters
Generalist physical AI has been hindered by high inference latency on edge silicon. FLUX 3 Action demonstrates that a compact, parameter-efficient world action model can outperform larger architectures like NVIDIA's 16B Cosmos3-Nano while running locally on embedded hardware. Releasing open weights allows independent developers to bypass expensive closed API layers, providing a practical foundation model for real-time manipulation and local trajectory planning.
Black Forest Labs and Positronic Robotics emphasize that predicting environmental state changes before generating action vectors dramatically reduces physical trial-and-error costs. Conversely, robotics researchers note that benchmarking on 7-DoF single-arm setups does not guarantee equal parameter efficiency when scaling to 28+ DoF bipedal locomotion.
Researchers at South Korea's KAIST demonstrated their RAIBO2 quadruped robot completing a full 42.195-kilometer marathon on Wednesday, September 23, in Sangju, South Korea. Operating untethered on a single battery charge, the machine finished the course in 4 hours, 19 minutes, and 52 seconds, maintaining an average speed of 2.64 meters per second. Published in Nature, the study details how the engineering team achieved a cost of transport (COT) of 0.25 by combining custom force-transparent mechanical legs, low-resistance motor drives, and energy-aware reinforcement learning locomotion controllers. Over the entire run, the robot consumed 1,280 Wh of energy, achieving roughly three times the operational range per charge of conventional quadrupedal platforms.
Why it matters
High energy consumption during sustained gait cycles has long restricted legged robots to short inspection runs or tethered industrial setups. By treating thermal, mechanical, and electrical dissipation as a unified co-optimization challenge, the KAIST team proved that quadrupeds can achieve endurance comparable to biological runners without increasing battery pack size. This breakthrough significantly expands the feasible mission radius for remote environmental monitoring, perimeter security, and search-and-rescue operations where mid-mission charging is impossible. The low COT benchmark provides a clear mechanical baseline for commercial quadruped manufacturers.
The KAIST research team emphasizes that system-level optimization across mechanical linkages and motor circuitry yields far greater range extensions than incremental battery density improvements. However, independent robotics analysts note that maintaining low cost-of-transport metrics on flat asphalt during a marathon does not guarantee similar efficiency gains when traversing unpaved, high-friction, or mud-laden terrain.
Silicon Valley startup Feather Robotics announced a $7.6 million pre-seed round on Thursday, September 24, led by Gradient Ventures. Founded by former 1X and Tesla engineers Hoa Mai and Parsa Bakhtiari, the company is building a modular $30,000 bipedal robot designed as an open hardware-and-software toolkit. Rather than developing a proprietary end-to-end model stack, Feather's architecture allows customers to run third-party physical AI brains from developers like NVIDIA, Skild, or Physical Intelligence. The startup disclosed it has already passed $1 million in top-line revenue from early small-batch deployments in commercial kitchens and research facilities.
Why it matters
Feather's launch represents a direct challenge to the vertically integrated strategies of Tesla and Figure AI, attempting to create an 'Android of robotics' hardware layer. By pricing its modular platform at $30,000—roughly half the cost of comparable research humanoids—the company lowers the capital barrier for software-focused AI labs seeking real-world testbeds. If Feather can maintain hardware reliability at this price tier, it will accelerate the deployment of specialized domain-specific models without forcing developers to build custom physical platforms.
Feather's founders argue that decoupling physical hardware from foundational AI software will mirror the open PC and smartphone booms, enabling thousands of specialized robotics startups to build on standard bodies. Skeptics maintain that tight hardware-software co-design is essential for complex physical balance and manipulation, claiming that generic hardware platforms inevitably sacrifice dynamic performance and energy efficiency.
With Chinese securities regulators continuing to halt traditional IPOs for humanoid startups over subsidized revenue concerns, Beijing-based Shenpu Intelligence is taking an alternative route. The commercial service robot builder was selected on Tuesday, September 22, as the lead investor to acquire a controlling stake in struggling home renovation firm *ST Meizhi for RMB 810 million (~$115 million). The startup is utilizing this reverse takeover of a distressed listed shell to secure a public listing and an alternative capital pipeline.
Why it matters
Tighter regulatory scrutiny surrounding subsidized revenues and technical metrics has drastically slowed traditional tech IPOs for Chinese robotics startups. Shenpu's reverse takeover demonstrates an unconventional financial workaround for early-stage, cash-burning hardware companies seeking public equity access. If successful, this restructuring pathway will likely be replicated by other Asian physical AI startups facing capital constraints.
Shenpu's financial advisors present the reverse takeover as a pragmatic capital strategy that bypasses lengthy IPO approval backlogs and secures immediate access to public credit lines. Market auditors warn that acquiring distressed listed shells exposes young hardware startups to inherited corporate liabilities, legal risks, and operational distractions that can undermine core R&D focus.
The US Food and Drug Administration issued formal draft guidance on Friday, September 25, titled 'Robotically-Assisted Surgical Devices—Premarket Submissions.' The document outlines updated non-clinical testing, software verification, human factors engineering, and clinical evaluation standards for teleoperated and software-controlled surgical platforms. Covering open, minimally invasive, and endoluminal procedures, the guidance establishes clear regulatory expectations for multi-arm and remote telesurgical systems. The FDA opened a 60-day public comment period running through November 24, 2026, before finalizing the rules.
Why it matters
As surgical robotics ventures expand into multi-port, single-port, and long-distance telerobotic operations, regulatory uncertainty has remained a major timeline risk for premarket 510(k) and PMA approvals. The FDA's draft guidance clarifies testing requirements for software safety interlocks and teleoperation latency. Standardizing these submission benchmarks gives medical device entrepreneurs a clearer path to clearance while raising the barrier to entry for unvalidated surgical startups.
Medtech regulatory consultants welcome the draft guidance as a necessary modernization that provides clear testing protocols for software-driven surgical platforms. However, early-stage surgical robot developers warn that stringent human-factors and latency testing mandates could lengthen development cycles and increase pre-market clinical trial costs.
NVIDIA launched Isaac ROS 5.0 on Thursday, September 24, introducing GPU-accelerated ROS 2 packages designed to bring agentic foundation models directly into physical robot control loops. The release features optimized perception kernels and spatial reasoning modules engineered for Jetson Thor edge modules. Built to handle real-time semantic environment mapping and dynamic spatial reasoning, Isaac ROS 5.0 includes reference implementations validated on industrial mobile platforms and bipedal humanoids like Agility's Digit 5.
Why it matters
Integrating real-time spatial reasoning into ROS 2 pipelines allows autonomous robots to shift from pre-programmed path execution to dynamic task planning. Isaac ROS 5.0 leverages NVIDIA's hardware-software lock-in, providing pre-optimized ROS nodes that process multi-camera feeds and VLA instructions with minimal compute overhead. This release provides a turnkey software foundation for enterprise teams deploying physical AI agents in dynamic factory environments.
NVIDIA highlights that Isaac ROS 5.0 drastically reduces developer integration time for spatial foundation models on Jetson hardware. Independent open-source developers note that while the ROS 2 interfaces are open, the underlying hardware acceleration remains tightly locked to NVIDIA's proprietary CUDA and NITROS ecosystems.
Yesterday we covered EdgeCortix's introduction of the RAIDEN AI chiplet platform. Built on the firm's DNA-X architecture and managed via its MERA software stack, the platform scales from a single compute die (X1) to a four-die flagship configuration (X4) delivering up to 3.36 PFLOPS of FP4 performance with 256 GB of unified memory. Kawasaki Heavy Industries and Unigen Corporation have already secured design wins, with commercial sampling scheduled for early 2027.
Why it matters
Edge robotics hardware typically forces a tradeoff between low-power embedded microcontrollers and high-power, thermally constrained desktop GPUs. RAIDEN's modular chiplet platform addresses this by allowing system designers to scale compute and memory bandwidth within a single unified software environment. Securing early commitments from heavy industrial players like Kawasaki validates market demand for scalable edge silicon in off-grid robotic platforms.
EdgeCortix asserts that modular chiplet architectures allow robotics manufacturers to reuse software investments across small drones and heavy industrial arms. Semiconductor analysts point out that while chiplet designs offer excellent performance scaling, success depends heavily on yield rates and manufacturing execution ahead of 2027 volume production.
Amazon announced a $100 million investment on Friday, September 25, to establish a 585,000-square-foot advanced manufacturing facility in Greenwood, Indiana. Scheduled to become operational by 2028, the plant will co-locate automated fabrication, robotic welding, powder coating, and assembly lines driven by AWS smart manufacturing software. The facility will directly manufacture proprietary fulfillment hardware—including drive units and Proteus autonomous mobile robots—for Amazon's North American logistics network, creating 300 engineering and technical manufacturing jobs.
Why it matters
Amazon's multi-million-dollar commitment marks a major step in the vertical integration and localization of its automation supply chain. Rather than relying on external industrial suppliers, Amazon is securing its fulfillment infrastructure against international logistics disruptions and component tariffs. Building proprietary manufacturing capacity in-house accelerates the rollout rate of custom robotics across its distribution network.
Amazon logistics executives state that insourcing hardware production ensures tight software-hardware integration across their fulfillment centers. Industry supply chain analysts observe that building dedicated robotics factories highlights how large tech firms are behaving like industrial OEMs to maintain competitive advantages in fulfillment speed.
A research team at The Chinese University of Hong Kong (CUHK), led by Professor Zhang Li, published a study in Science Robotics on Thursday, September 24, introducing an AI-driven magnetic tracking system for untethered medical microrobots. The platform addresses a long-standing sensing barrier by using deep learning to suppress high-power magnetic actuation noise, achieving sub-millimeter localization precision at penetration depths up to 10 centimeters in biological tissue. The system was validated in vivo using a goat model, successfully tracking and navigating wireless magnetic micro-agents through the spinal subarachnoid space and bile duct inside an unshielded clinical environment.
Why it matters
Real-time non-invasive tracking inside deep tissue has been a major barrier preventing untethered medical microrobots from advancing to human clinical trials. High-intensity magnetic fields required for locomotion typically blind external sensors, forcing reliance on harmful X-ray fluoroscopy. By filtering magnetic interference through an AI model, CUHK's platform enables closed-loop autonomous navigation for targeted drug delivery and micro-surgeries without radiation exposure.
The CUHK team emphasizes that real-time AI noise suppression enables true closed-loop feedback control during delicate micro-endoscopic procedures. Independent biomedical engineers note that while the goat model results are promising, clinical translation to human trials will require rigorous validation across variable tissue densities and complex anatomical motion.
Researchers led by Anupam Pandey at Syracuse University published findings in Science Advances detailing how microscopic, self-contacting folds in compressed elastomeric substrates can act as rewritable fluidic gates. Operating via elastocapillary repulsion, these creases create localized energy barriers that block small liquid droplets while allowing larger droplets to cross. By adjusting a single mechanical compression force (e.g., squeezing the substrate 15% further), the critical droplet volume threshold can be quadrupled. The team built rewritable crease networks to route fluid streams, maintain droplet memory, and construct a functional microfluidic half adder that executes binary arithmetic directly with liquid drops.
Why it matters
Traditional lab-on-a-chip diagnostic tools rely on complex microfluidic valves, external pumps, or integrated electronic controls that increase fabrication costs and failure rates. Syracuse's mechanical instability approach enables fully passive, rewritable fluidic routing driven purely by substrate compression. For soft micro-robotics and field-deployable medical diagnostics, executing onboard fluidic logic without electronics simplifies system architecture and enables resilient zero-power processors.
The Syracuse research team highlights that converting mechanical material instabilities into functional computing elements eliminates the need for embedded electronic sensors in microfluidic chips. Fluidics researchers point out that elastomer degradation and surface contamination over repeated compression cycles could alter surface energy barriers and cause logic errors in long-term field deployments.
Kodiak AI launched a commercial freight collaboration with DTL Transport on Tuesday, September 22, hauling perishable agricultural goods along California's I-5 and CA-99 corridors between Fresno and Los Angeles. Utilizing heavy-duty Autonomous Vehicle Testing permits granted by the California DMV in August, the pilot deploys Kodiak's Level 4 autonomous truck stack with a human safety driver behind the wheel. The operation tests time-sensitive produce transportation across a 225-mile agricultural shipping route ahead of planned driverless Texas highway operations in late 2026.
Why it matters
California's regulatory decision to permit heavy autonomous trucks on public freight corridors marks a critical policy shift for commercial logistics. Hauling temperature-sensitive agricultural cargo along the heavily congested I-5 corridor provides a rigorous real-world test for Kodiak's motion planning and sensor perception. Successfully commercializing autonomous freight on these routes offers a path to alleviate chronic long-haul driver shortages and lower refrigerated supply chain costs.
Kodiak AI and DTL Transport contend that autonomous trucking will improve freight safety and keep agricultural supply chains moving continuously. Labor representatives and freight unions maintain that deploying heavy autonomous vehicles on public highways poses safety risks and threatens long-haul trucking jobs, advocating for permanent human safety driver mandates.
Popular Mechanics published a detailed technical review of the Eufy Omni S2 robot vacuum and mop combo on Wednesday, September 23. The flagship consumer platform features a space-saving vertical base station that washes mops with 140°F water, dries them with 131°F air, auto-dispenses detergent, and holds dust for 68 days. The robot delivers up to 30,000 Pa of suction, utilizes an anti-tangle main brush, and applies 15 Newtons of downward pressure through a rolling mop system. While testing validated strong pet hair pick-up and obstacle avoidance, reviewers noted minor navigation jitter during front-sensor wall mapping.
Why it matters
Consumer floorcare robotics has reached a point where raw suction metrics (30,000 Pa) are outpacing practical home cleaning needs, shifting competitive differentiation toward base station ergonomics and maintenance automation. Eufy's move toward narrow vertical dock designs directly addresses urban apartment footprint constraints. Evaluating real-world anti-tangle and heated mop-washing performance helps benchmark consumer willingness to pay premium prices for hands-off home maintenance.
Hardware reviewers praise the Omni S2's vertical footprint and heated mop-cleaning capabilities as significant usability upgrades for busy households. Consumer privacy advocates continue to raise concerns regarding the detailed LiDAR floor plans and visual obstacle data collected and uploaded by high-end home cleaning bots.
Modular Joint Architectures Target Industrial Stoppage Overhead Startups like O-ID and Feather are abandoning monolithic, fully integrated bipedal frames in favor of hot-swappable joint modules and open compute layers. By enabling on-site technicians to swap failed actuators or upgrade local NPUs in minutes, hardware teams aim to eliminate the multi-day factory line stoppages that make traditional humanoids economically unviable.
Multithreaded Runtimes Smooth the VLA-to-Motor Frequency Gap The severe frequency mismatch between slow high-level VLA inference (5–10 Hz) and rapid low-level motor actuation (200–1000 Hz) is being addressed via decoupled middleware like Open-RAIL. By introducing asynchronous threading and trajectory smoothing algorithms, developers are doubling physical execution speeds without inducing high-vibration mechanical wear.
Whole-System Efficiency Optimization Extends Untethered Range Endurance breakthroughs are moving beyond battery chemistry upgrades toward holistically co-optimized mechanics, force-transparent drives, and reinforcement learning control. As demonstrated by KAIST's full-marathon quadruped run, balancing energy dissipation across structural, electrical, and policy layers dramatically reduces the cost of transport.
Public IPO Pathways Pivot Toward Reverse Mergers Amid Scrutiny With Asian securities regulators auditing state-subsidized data revenues and traditional public listings facing tighter scrutiny, early-stage robotics ventures are seeking alternative capitalization routes. Restructuring consortia and reverse takeovers of listed shell companies are emerging as preferred workarounds to secure public market liquidity.
Commercial AV Infrastructure Expands Into Heavy Logistics Corridors Autonomous vehicle operators are aggressively scaling beyond passenger robotaxi pilots into heavy-duty freight and agricultural supply chains. Supported by updated state DMV testing permits and partnerships with legacy truck builders, driverless freight routes along major interstate corridors are establishing a foundation for commercial long-haul operations.
What to Expect
2026-09-30—University of Washington team presents DirectHop micro-hopping robot at IROS 2026.
2026-10-08—Medevise Consulting hosts the Robotics in Ophthalmology Workshop ahead of AAO 2026 in New Orleans.
2026-11-24—FDA public comment period closes for draft guidance on Robotically-Assisted Surgical Devices premarket submissions.
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