NVIDIA is commanding the software conversation today out of the Conference on Robot Learning, releasing new open-world foundation models tightly coupled with accelerated physics simulation. On the hardware front, Boston Dynamics is pushing humanoid whole-body control to its breaking point by testing experimental high-speed wheels.
Boston Dynamics published video footage on Monday, November 9, detailing an experimental setup where custom single-adapter fittings attached off-the-shelf scooter wheels to the ankle actuators of its electric Atlas humanoid robot. Controls engineer Andy Wu explained that the testbed used a single whole-body reinforcement learning policy trained in MuJoCo to manage rolling locomotion, execute side dashes, and perform 360-degree spinning jumps without dedicated drive modules. Boston Dynamics clarified that the wheeled Atlas is strictly a research apparatus designed to test motor thermal limits and push control policies to failure rather than a commercial product roadmap item.
Why it matters
Subjecting bipedal joint actuators to continuous high-speed rolling and rapid directional changes provides critical empirical data on heat dissipation and mechanical stress under extreme duty cycles. Testing simulation-trained whole-body controllers on modified physical hardware validates how well reinforcement learning models adapt to unscripted dynamics without falling. For builders evaluating locomotion trade-offs, this experimental platform offers insight into combining the top speed of wheels with the balance control algorithms built for legs.
Boston Dynamics maintains that the wheeled platform is purely an experimental stress test aimed at refining core bipedal actuator designs. Conversely, robotics industry observers note that hybrid wheel-leg configurations represent a compelling pragmatic middle ground for structured warehouse environments where flat-surface speed outweighs pure legged obstacle traversal.
Speaking at the Web Summit opening night in Lisbon on Monday, November 9, Agibot European representative William Shi addressed industry competition by stating that Agibot currently has over 1,000 humanoid robots actively deployed in Chinese manufacturing facilities. During his presentation, Shi critiqued Western competitors, stating that Tesla relies heavily on pre-recorded videos rather than open live demonstrations, while Figure AI focuses on static chore videos. The panel featured four Chinese-manufactured humanoids registered as official conference speakers, though technical delays and network connectivity issues disrupted live question-and-answer interactions on stage.
Why it matters
The verbal exchanges at Web Summit reflect the escalating commercial and narrative competition between Chinese and Western humanoid manufacturers over real-world deployment claims. Citing over 1,000 active factory units positions Agibot as a volume leader in operational floor deployments. However, the stage delays experienced by the robotic speakers highlight the persistent gap between scripted commercial messaging and reliable, live conversational autonomy.
Agibot claims its focus on pragmatic industrial deployments has allowed it to scale physical factory numbers well ahead of Western peers. Conversely, industry critics point to the live demonstration stumbles at the event as evidence that public claims regarding fleet autonomy often outpace real-time operational reliability.
Anker introduced the Eufy Robot Vacuum Omni S2 Pro on Tuesday, November 10, pricing the flagship domestic floor cleaner at $1,699 in the U.S. and £1,599 in the UK. The unit pairs a 40,000 Pa suction motor with dual-action sanitation hardware, combining on-board electrolyzed tap water mopping with six downward-facing UV-C LEDs to achieve a 99.99% pathogen reduction rate. Mechanical features include HydroJet 3.0 self-cleaning hot water roller maintenance, dual-laser CleanMind AI obstacle avoidance, and a base station providing automated dust disposal for up to 68 days.
Why it matters
The consumer robot vacuum market is shifting from competing purely on peak suction power toward integrating active mechanical reach and clinical-grade sanitation. Incorporating UV-C LED arrays and electrolyzed water generation directly into automated floor care platforms targets consumer demand for active home hygiene. This trend forces smart home appliance makers to incorporate complex multi-modal cleaning systems into single autonomous consumer form factors.
Anker frames the Omni S2 Pro as a major upgrade in domestic health hardware, citing its Asthma & Allergy Friendly certification and chemical-free sanitation. On the other hand, consumer tech reviewers note that pricing automated floor cleaners near $1,700 pushes consumer robotics into premium price brackets, testing buyer willingness to pay for specialized sanitization features.
Ant Group subsidiary Robbyant open-sourced LingBot-Touch on Tuesday, November 10, releasing model weights and technical code on GitHub and Hugging Face under the Apache License 2.0. Pre-trained on nearly 5,000 hours of real-world tactile data using a JEPA-based architecture with a shared Transformer backbone, the foundation model unifies piezoresistive, magnetic, and visuotactile sensor signals into standardized tactile tokens. In evaluations on Sharpa Wave and XHand dexterous manipulators, integrating LingBot-Touch tokens into downstream diffusion policies improved action success rates by 14 to 26 percentage points across delicate manipulation tasks.
Why it matters
Tactile sensing in robotics has suffered from severe hardware fragmentation, as policy models trained on one sensor type typically fail when ported to another. LingBot-Touch solves this by mapping disparate voltage, visual, and magnetic signals into a common tokenized latent space that can plug directly into standard vision-language-action architectures. Open-sourcing this representation lowers the barrier for hardware developers seeking to add closed-loop tactile feedback to dexterous hands without collecting proprietary datasets for every sensor design.
Robbyant highlights that open-sourcing a unified tactile token layer democratizes touch-based robot learning across diverse hardware platforms. However, academic researchers note that achieving true zero-shot sensor transfer across custom soft tactile skins still requires broader standardization of physical contact calibration.
At the 2026 Conference on Robot Learning (CoRL) in Austin, NVIDIA introduced GR00T 2, an open-world action model trained on 100,000 hours of human egocentric video, 10,184 hours of handheld demonstrations, and 23,500 hours of teleoperation data. Leveraging X-Adapt cross-embodiment adaptation, the model achieved a 40 percent zero-shot success rate across 50 benchmark tasks, rising to 95 percent after fine-tuning on five demonstrations per task using a single RTX 5090 GPU. Concurrently, NVIDIA released Isaac Lab 3.0, incorporating the Newton physics engine developed with Skild AI to support deformable objects and boost simulation throughput 2.5x. NVIDIA also previewed GR00T 2 Turbo running on a Unitree G1 driven by an onboard Jetson Thor processor.
Why it matters
The requirement for thousands of hours of manual teleoperation data per robot form factor has been a primary financial bottleneck in scaling physical AI. By enabling zero-shot skill transfer across distinct physical architectures and reducing task fine-tuning to under five demonstrations, GR00T 2 lowers the friction of deploying novel manipulation behaviors. Tightly coupling this foundation layer with Newton physics inside Isaac Lab 3.0 allows developers to simulate complex, contact-rich tasks like managing flexible parts or deformable goods before deploying to physical hardware.
NVIDIA positions GR00T 2 as a universal skill-translation framework that unifies cross-hardware execution under open-weight foundation models. However, pilot integration partners like Siemens and Foxconn face the practical hurdle of verifying whether high simulation success rates hold up under non-standard industrial noise and mechanical tolerances.
UC Berkeley professor Ken Goldberg and NVIDIA engineering director Spencer Huang presented 'agentic robotics' at CoRL 2026 on Monday, November 9, introducing the Graph-as-Policy (GaP) framework. The architecture deploys teams of generative AI coding agents to write, test, and self-optimize modular robot control programs directly within NVIDIA Isaac simulations. In commercial deployments with Ambi Robotics across 30% of its U.S. warehouse fleet, the self-correcting system analyzed parcel-sorting failures and optimized control scripts, increasing throughput by over 15,000 packages annually per robot while cutting retasking times at industrial partners like Wistron from 8 hours to under two minutes.
Why it matters
Imitation learning policies trained purely on neural weights can act as opaque black boxes, making them difficult to debug or audit in safety-critical factory settings. Agentic robotics circumvents this by using language models to generate structured, human-readable code that can be inspected and updated before deployment. By automating the trial-and-error debugging cycle inside high-throughput simulation environments, this framework dramatically reduces the engineering overhead required for frequent product changeovers.
Researchers from UC Berkeley emphasize that code-generating AI agents eliminate data collection bottlenecks by converting high-level goals into inspectable control scripts. However, industrial integrators caution that code-as-policy frameworks rely heavily on simulator fidelity, meaning unmodeled physical edge cases on the factory floor can still cause unexpected code execution failures.
At electronica 2026 on Monday, November 9, GlobalFoundries subsidiary MIPS launched the Actus reference platform, a single-chip architecture designed for humanoid and industrial robot joints. Built on GlobalFoundries' FDX FD-SOI technology, Actus integrates two MIPS Atlas M8500 real-time RISC-V processors alongside an ARC VPX 512-bit vector processor dedicated to trigonometric acceleration and localized sensor fusion. The SoC incorporates three-port Time-Sensitive Networking (TSN) gigabit Ethernet, CAN-FD interfaces, and hardware-level post-quantum cryptography to execute field-oriented motor control locally within tight joint envelopes.
Why it matters
Routing high-frequency motor control and sensor processing back to a central host computer introduces wiring mass and communication latency that limit joint performance in compact humanoids. Placing real-time RISC-V cores, vector math units, and deterministic networking directly inside the actuator housing allows robots to execute field-oriented control loops locally at low latency. This integrated silicon design directly tackles the thermal and space constraints that complicate multi-joint physical AI platforms.
MIPS executive leadership asserts that integrating vector processing and deterministic motor control on FD-SOI silicon solves the core compute-density challenge for production humanoid joints. On the other hand, system integrators must weigh the benefits of specialized joint SoCs against the software complexity of managing distributed multi-node control architectures.
During AI Day Seoul on Tuesday, November 10, NVIDIA announced direct equity investments in four South Korean technology startups, including robotics foundation model developer RLWRLD. Operating across Seoul, San Francisco, and Tokyo, RLWRLD develops Multi-Stream Action Transformer architectures designed to enable fine five-finger manipulation, such as component insertion and nut fastening. Concurrently, Korean NPU maker Rebellions signed an agreement to integrate its next-generation neural processor into NVIDIA's NVLink Fusion architecture, while Samsung SDI and Hyundai Motor Group formalized an alliance to develop dedicated humanoid robot batteries.
Why it matters
NVIDIA's direct investment strategy reflects an effort to secure regional physical AI ecosystems and anchor regional hardware developers to its software stack. For physical AI startups, gaining direct access to NVLink interconnect frameworks and GPU compute infrastructure accelerates the training of multi-stream manipulation models. Furthermore, the strategic pairing of Samsung SDI's battery manufacturing with Hyundai's robotics programs underscores how industrial conglomerates are consolidating supply chains for mass-production humanoids.
NVIDIA views these regional investments as essential for building out global supply chains for physical AI silicon and robot learning models. Conversely, industry analysts point out that integrating domestic specialized NPUs like Rebellions into proprietary NVLink architectures creates complex co-dependency dynamics between regional hardware makers and NVIDIA.
Chinese embodied intelligence startup Youliqi Robotics announced the completion of a 500 million yuan (approx. $69 million) Pre-A funding round on Tuesday, November 10. Existing investors Xie Nuo Investment and Yonghua Investment expanded their positions, alongside new strategic capital from Greenland Group, Keli Sensor, and Huagong Venture Capital. Youliqi confirmed that its commercial service robots have reached continuous monthly shipments exceeding 100 units, operating across hotel, property security, and commercial retail deployments.
Why it matters
Securing a 500 million yuan Pre-A round signals strong investor appetite for embodied AI startups that establish immediate monthly shipment volume. Partnering with real estate conglomerate Greenland Group provides Youliqi with direct access to physical commercial properties for rapid fleet scaling. Surpassing 100 monthly units shipped demonstrates a transition from early R&D prototyping to structured commercial deployment in service sectors.
Youliqi's leadership emphasizes that strategic backing from sensor makers and property developers accelerates the deployment of service robots across real estate assets. However, financial analysts caution that sustaining capital-intensive expansion in commercial services requires maintaining high unit margins as fleet deployment scales.
Following the regulatory deal collapse we tracked last week, ABB Ltd and SoftBank Group Corp are exploring structural options to rescue their $5.4 billion industrial robotics agreement, as reported on Monday, November 9. To navigate around the block by China's National Development and Reform Commission, the companies are assessing a plan to carve out ABB Robotics' Chinese operations into a separate standalone entity valued between $2 billion and $3 billion, isolating domestic operations from the global transaction.
Why it matters
Geopolitical tech scrutiny is actively reshaping major cross-border M&A transactions in industrial automation. Because China accounts for approximately 30% of ABB Robotics' global revenues, carving out domestic operations is critical to preserving transaction value for SoftBank. If executed, this deal structure sets a benchmark for how global robotics conglomerates navigate tightening cross-border regulatory and data sovereignty restrictions.
SoftBank and ABB view the carve-out as a pragmatic path to finalize the multi-billion-dollar deal while complying with regional data security mandates. Conversely, market analysts warn that splitting ABB's Chinese operations away from global R&D could disrupt supply chain integration and weaken market share in Asian industrial sectors.
Virtuoso Surgical secured FDA De Novo authorization on Tuesday, November 10, for its Virtuoso Endoscopy System, creating a new regulatory classification for electromechanical endoluminal surgical devices. The platform features two needle-sized, superelastic nitinol robotic arms mounted at the tip of a rigid endoscope, providing two-handed dexterity inside narrow anatomical cavities. In the 20-patient VIABLE clinical trial conducted at The Chinese University of Hong Kong, the system achieved intact en bloc resection of bladder lesions with detrusor muscle present in 94% of specimens and zero intraoperative adverse events. Virtuoso plans a targeted U.S. commercial launch in 2027.
Why it matters
Traditional endoscopic tumor removals require piecemeal tissue scraping, which often fragments specimens and complicates accurate cancer staging by pathologists. Delivering micro-scale nitinol robotic arms through standard endoscopic channels allows surgeons to perform precise two-handed dissection inside enclosed organs. Achieving FDA De Novo clearance establishes a regulatory precedent for concentric-tube robotic mechanisms, opening pathways for miniature endoscopic tools in urology, gynecology, and neurosurgery.
Virtuoso Surgical leadership highlights that intact en bloc resection dramatically improves histological accuracy and tumor staging for urological oncology. However, surgical adoption will depend on demonstrating clear procedure-time parity and economic value over established monopolar loop resection tools during initial 2027 hospital rollouts.
Autonomous aerial logistics developer Matternet partnered with Swisslog Healthcare's TransLogic division on Tuesday, November 10, to link outdoor drone deliveries with internal hospital transport systems. The collaboration integrates Matternet's M3 autonomous drone platform with Swisslog's automated in-building pneumatic tube networks across hospital networks. Initial commercial deployments target regional health networks in California, Florida, and Arizona to enable end-to-end transport of diagnostic specimens and urgent medications without manual transfer stops.
Why it matters
Bypassing manual hand-offs between outdoor delivery vehicles and indoor transport systems solves a major timing bottleneck in hospital logistics. Leveraging Swisslog's installed base across 2,300 North American facilities provides Matternet with direct infrastructure integration into clinical supply chains. Automating the transfer from rooftop drone ports into pneumatic tube lines creates an uninterrupted transport pipeline for time-critical medical samples.
Matternet and Swisslog emphasize that seamless physical integration eliminates labor-intensive hand-offs and speeds up emergency specimen processing. However, hospital operations managers note that widespread adoption will require strict compliance with regulatory chain-of-custody standards for automated sample transfers.
Fleshing out the sub-6-milliwatt edge silicon preview we noted in October, Synaptics unveiled its Astra SR200 series of 'xMCUs' on Tuesday, November 10. The microcontrollers integrate Google Research's Coral NPU alongside Arm Cortex-M85 and Cortex-M52 cores within Synaptics' Torq AI engine, featuring a tiered memory architecture that pairs 8MB of on-chip SRAM with custom packaged DRAM scaling to hundreds of megabytes. Industrial variants add 1Gb Ethernet with IEEE 1588 Time-Sensitive Networking and CAN-FD interfaces to drive real-time deterministic robotics control loops without external application processors.
Why it matters
Standard microcontrollers have historically lacked the memory bandwidth and capacity needed to execute complex vision transformers without offloading inference to power-heavy processors. Packaging high-density custom DRAM directly into the microcontroller domain eliminates external memory buses and cuts system power consumption. This architecture allows embedded vision sensors and robotic joint controllers to run local neural inference while maintaining low power draw.
Synaptics asserts that combining custom DRAM with low-power neural engines establishes a new performance tier for edge microcontrollers. Conversely, embedded software engineers note that deploying complex multimodal models onto memory-constrained xMCU platforms requires optimized compilation workflows to avoid memory access bottlenecks.
At CeMAT ASIA 2026 in Shanghai on Tuesday, November 10, Knewbots and the Beijing Humanoid Robotics Innovation Center introduced an industrial embodied AI platform built on a 'single-brain, multi-machine' control architecture. The platform pairs Beijing Humanoid's 'Huisikaiwu' foundation model with Xiaowu's industrial kernel to coordinate heterogeneous fleet operations. Live demonstrations on the exhibition floor showed Tianyi humanoid series units, autonomous forklifts, and under-ride AMRs operating synchronously to execute precision parts assembly and material transport within a single scheduling loop.
Why it matters
Industrial automation frequently struggles with siloed operations where mobile robots, automated forklifts, and robotic arms run on disconnected control software. Unifying heterogeneous robot architectures under a single foundation model architecture enables dynamic task distribution across distinct form factors. This integrated approach signals a shift toward coordinated, lights-out factory cells managed by centralized physical AI engines.
Beijing Humanoid and Knewbots highlight that centralized foundation models resolve fleet orchestration bottlenecks across mixed robot form factors. However, industrial control engineers stress that deploying single-brain multi-machine systems in active plants demands deterministic fail-safes to prevent single-point fleet disruptions.
Urban logistics company PORT secured €14.5 million in funding on Tuesday, November 10, led by the European Bank for Reconstruction and Development (EBRD) and Balnord, bringing total capital raised to €29 million. Founded in 2020 by CEO Kamil Suda, the UK firm operates urban microhubs and e-bike commercial fleets while developing proprietary AI Driver software and custom autonomous delivery vehicles. PORT confirmed a new commercial trial partnership with Royal Mail to deploy its robotic delivery system across existing UK urban postal networks.
Why it matters
Scaling last-mile autonomous logistics relies heavily on establishing physical microhub infrastructure in high-density urban zones. Leveraging real-world telemetric data gathered from e-bike operations provides PORT with localized training data for its autonomous driving stack. Partnering with established postal operators like Royal Mail accelerates commercial validation by integrating autonomous delivery vehicles directly into existing delivery routes.
PORT leadership contends that pairing physical microhub networks with proprietary AI software provides the necessary operational base for scaling last-mile delivery. In contrast, urban logistics analysts emphasize that regulatory approvals for sidewalk and road-going delivery pods remain fragmenting factors across European municipalities.
Geek+ announced its 24/7 Warehouse Autonomy Plan on Tuesday, November 10, outlining an operational roadmap designed to achieve continuous, lights-out warehouse execution. The strategy centers on integrating the company's Gravity embodied AI framework with its Geek+ Warehouse Brain software to manage Robot Management Systems and Intelligent Operations Platforms. The roadmap focuses on automating exception handling, closing continuous material workflows, and scaling multi-robot coordination across global distribution centers.
Why it matters
Achieving true 24/7 warehouse autonomy requires moving beyond routine path execution to handle unforeseen physical exceptions without human intervention. Integrating physical AI frameworks directly into warehouse management software allows automated systems to dynamically re-route tasks when bottlenecks occur. This facility-wide approach aims to transform isolated automated storage cells into fully self-optimizing logistics hubs.
Geek+ asserts that combining embodied AI with centralized operational brains is key to eliminating manual shift dependencies in logistics. Conversely, warehouse operators note that achieving full lights-out autonomy requires proving high system uptime across high-SKU seasonal peaks.
In study results published on Tuesday, November 10, researchers demonstrated sub-micrometer light-driven nanorobots capable of controlled directional propulsion and microscopic bacteria collection. The nanorobots utilize up to four plasmonic nanoantennas that absorb ambient light and directionally re-emit photons to generate recoil propulsion. By adjusting light polarization, the team achieved precise 90-degree steering turns, allowing the untethered nanorobots to scan targeted microscopic areas, capture live bacteria, and relocate them without mechanical tethers.
Why it matters
Manipulating biological targets at sub-micron scales traditionally requires complex microfluidic channels or fixed optical tweezers. Utilizing plasmonic nanoantennas for directional photon recoil propulsion enables microscale machines to steer freely through fluidic media using light polarization alone. This untethered control mechanism opens up new possibilities for targeted cellular sampling and non-invasive micro-interventions.
The research team highlights that photon recoil propulsion provides precise, tetherless manipulation of biological entities without damaging cell structures. However, bioengineers note that translating light-driven propulsion from transparent laboratory slides into opaque biological tissues presents significant optical attenuation challenges.
Researchers at Eindhoven University of Technology detailed a fingertip-sized soft tactile display in Frontiers in Robotics and AI on Tuesday, November 10. The system integrates a three-by-three array of nine individually addressable molded silicone pneumatic actuators within a compact 20-by-20 millimeter footprint. Utilizing flexure-based structural isolation to eliminate cross-actuator interference, human psychophysical evaluations confirmed high proportional pattern localization accuracy and functional single-cell Braille reading capabilities across simultaneous static and dynamic vibrotactile modes.
Why it matters
Miniaturizing pneumatic soft actuators without causing physical cross-talk between adjacent cells has been a persistent challenge in wearable haptic design. Using flexure-guided silicone unit cells achieves high spatial actuator density on human fingertips without increasing structural rigidity. This design advances lightweight haptic interfaces for tele-operated surgical consoles, immersive virtual environments, and wearable accessibility tools.
The engineering team highlights that structural flexure isolation allows high-density pneumatic arrays to deliver distinct tactile patterns directly to human skin. On the other hand, commercial hardware developers note that integrating micro-scale pneumatic pumps into lightweight wearable form factors remains a key hardware constraint.
Tesla officially launched its unsupervised Robotaxi service in San Antonio at 5 a.m. local time on Monday, November 9, establishing its eighth market nationwide and fifth in Texas. The opening deployment covers a 275-square-mile geofence—marking Tesla's largest initial day-one operating area to date. The launch brings Tesla into head-to-head competition with Alphabet's Waymo, which operates commercial driverless rides in San Antonio following its service restoration after spring flooding. Simultaneously, independent tracker data indicated Tesla expanded its active Austin robotaxi service boundary by 62.5% to approximately 442 square miles.
Why it matters
San Antonio represents the first U.S. metropolitan market where two competing fully driverless commercial robotaxi services operate simultaneously across overlapping coverage zones. Opening with a 275-square-mile boundary tests Tesla's vision-only autonomous architecture across dense urban and suburban transit corridors without a lengthy phased mapping period. The head-to-head performance against Waymo's sensor-fusion fleet will provide a clear comparative test of vision-based neural networks versus multi-sensor LiDAR architectures in commercial ride-hailing.
Tesla supporters contend that deploying massive day-one geofence boundaries proves the rapid scalability of its pure-vision autonomy model across permissive regulatory regions. In contrast, municipal transit analysts note that scaling geographic coverage must be matched by consistent fleet availability, where Waymo retains an edge due to established airport access agreements and storm-tested operational protocols.
Cross-Embodiment Skill Transfer Reduces Teleoperation Data Demands Foundation architectures like GR00T 2 and LingBot-Touch map physical trajectories and heterogeneous sensory inputs into unified latent spaces. By decoupling demonstration data from specific robot morphologies, developers can fine-tune policies across distinct hardware setups using minimal target-platform demonstrations.
Agentic Code Generation Complements Black-Box Imitation Policies Frameworks presented at CoRL leverage generative AI coding agents to write, test, and debug interpretable robot control programs directly inside GPU-accelerated simulators. This approach provides an alternative to massive human demonstration collections, allowing industrial integrators to retask cell behaviors rapidly.
Deterministic Motor Control Integrates Directly with Edge AI Silicon Semiconductor architectures like the MIPS Actus reference platform and Synaptics Astra series combine real-time RISC-V cores, vector processors, and Time-Sensitive Networking onto single chips. Moving field-oriented control and neural inference into joint actuators addresses thermal, space, and latency limits in compact physical platforms.
Extremely Unconventional Testbeds Uncover Physical Actuator Failure Modes Mounting off-the-shelf scooter wheels directly onto bipedal ankle actuators allows whole-body controllers to be pushed through continuous high-torque and high-speed maneuvers. These experiments stress-test simulation-trained reinforcement learning policies against mechanical limits without risking catastrophic legged falls.
Microscale Systems Shift Navigation Logic to Physical Material Geometry Researchers in microrobotics and soft actuation are using 3D microprinting and light-responsive polymers to generate autonomous locomotion. By relying on structural feedback and optical forces rather than onboard microelectronics, these micro-systems navigate complex biological environments without heavy control hardware.
What to Expect
2026-12-02—FDA public workshop on benefit-risk evaluation frameworks for autonomous and remote surgical robotics.
2027-01-05—Closing date for public comments on FDA discussion paper covering surgical teleoperation and autonomous robotics guidance.
2027-01-01—Agility Robotics scheduled commercial rollout and public demonstrations of its Digit 5 humanoid model.
2027-04-01—Minimal scheduled start of on-road customer trials for its autonomous Bot delivery vehicle.
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