Today on The Robot Beat: the gap between spectacle and operations. Figure ran a 24-hour livestream and Unitree filed a $7B IPO, but the more durable signal is underneath β dexterity foundation models hitting new benchmarks, Tier-1 auto suppliers quietly buying their way into embodied AI, and regulators sharpening their pencils on both surgical robots and autonomous trucks.
The Figure 03 '8-hour shift' story from yesterday extended past 24 hours: three units (Bob, Frank, Gary) processed 33,137 packages total. CEO Brett Adcock claimed 'zero failures' in autonomy and hardware, but observers caught package-handling errors and autonomous resets on-stream; Adcock pushed back by distinguishing task errors from system failures β a definitional fight the industry doesn't yet have a shared answer to. Agility Robotics and Apptronik publicly countered by citing months of uptime at paying customer facilities. Business Insider clocked 3M cumulative views on X.
Why it matters
The new development here isn't the runtime number β it's the semantic war that broke out around 'failure.' Figure picked a transparent endurance format, then defined success criteria narrowly enough that competitors called it marketing. The competitive backlash is more significant than the demo itself: Agility and Apptronik are now explicitly drawing a line between controlled-HQ endurance tests and customer-facility uptime, which sets the terms of every benchmarking conversation going forward. Separately, the '109k lines of hand-coded C++ replaced by Helix-02' claim is the most concrete architecture-transition datapoint Figure has disclosed.
Adcock's frame: the Helix-02 neural controller replaced 109k lines of C++ and ran a full production-shift duration without intervention β that's a software architecture milestone, not just a runtime number. Competitors' frame: months of uptime at a paying customer's facility is the only metric that matters to procurement, and a HQ livestream isn't that. Both frames can be simultaneously true.
WisdomTree launched WDRN, a 0.45% expense-ratio ETF tracking companies in physical AI, humanoid robots, autonomous drones, smart manufacturing, and supply-chain automation. It's the second public vehicle for the category this week after RoboStrategy's closed-end fund (NASDAQ: BOT) began trading May 11. WDRN is the open-ended index version; BOT is the concentrated-bets closed-end version β together they give retail and institutional capital two different risk profiles for the same underlying thesis.
Why it matters
Two distinct public vehicles for robotics and physical AI launching in the same week is the public-markets confirmation of the F Prime $20B-annual-investment thesis. WDRN is the open-ended index version, BOT is the closed-end concentrated-bets version β together they give retail and institutional capital two different ways to express the same thesis without venture timelines. The flows themselves become a signal worth watching.
Bull: liquidity arrives, multiples expand, late-stage privates have cleaner exit paths. Bear: thematic ETFs typically arrive at the top of a cycle, and the underlying basket is heavy with names trading at venture-scale multiples.
ECOVACS β known for robot vacuums β launched LilMilo, its first emotional AI companion robot at $799.99. The product features biomimetic fur, light-sensitive eyes, advanced vocal capability, and five personality types that evolve through daily interaction. It's a category-extension move from functional cleaning robotics into companion robotics.
Why it matters
Companion robots had a credibility problem until this year, but the category is now visibly consolidating around a $700β$800 price point with on-device LLMs: SwitchBot Kata Friends ($699), Familiar Machines (2027, 'comparable to pet ownership'), and now ECOVACS LilMilo ($800). A robot-vacuum OEM with global distribution and reliability infrastructure entering this segment changes the unit economics β ECOVACS can amortize sensor and motor BOM across two product lines in a way that pure-play companion startups can't.
Optimist: this is the segment where consumer robotics finally finds margin, because companionship is priced on emotion rather than task efficiency. Skeptic: the failure rate of companion robots β from Jibo to Anki β is essentially 100% over a 10-year window, and 'evolving personality' is a marketing line until proven otherwise.
The Allen Institute for AI released MolmoAct 2, an open-source robotics foundation model targeting real-world manipulation β dishwasher loading, sorting, bimanual operations β without task-specific programming. AI2 published full model weights plus a 720+ hour bimanual manipulation dataset. Reported inference latency is 180ms on the base configuration. Stanford School of Medicine is piloting it for CRISPR gene-editing workflows in a 'self-driving wetlab' project.
Why it matters
Open-weight robotics foundation models with substantial bimanual data are still rare, and the closed-vs-open dynamic in embodied AI is the strategic question of the year. AI2 dropping weights and data β and seeding a Stanford clinical pilot on day one β pressures the proprietary stacks (Ο, GR00T, RLDX-1) on both the data moat and the credibility front. For builders, this is the first credible open substrate for contact-rich bimanual tasks; the question is whether the dataset's diversity actually transfers to non-lab environments.
Bullish read: the 720-hour dataset is the real gift β robot data has been the bottleneck, and Stanford's wetlab deployment is a non-trivial validation. Skeptical read: 180ms inference is fine for benchmarks but tight for high-frequency manipulation, and 'open weights' rarely translates to drop-in production deployment without significant retraining infrastructure.
Researchers released IntentVLA, a history-conditioned VLA that encodes recent visual observations into compact 'intent' representations to address observation aliasing β the failure mode where identical-looking states require different actions depending on recent context. On the new AliasBench benchmark, IntentVLA hit 45.8% average success versus 9.0% for baseline VLAs.
Why it matters
Observation aliasing is one of the unglamorous bugs that breaks VLA deployments in the real world β and 'fix it with more demonstration data' has not worked. A history-conditioned intent representation is a cleaner architectural answer than chain-of-thought tokens (see LaRA-VLA's 90% latency cut from a few days ago) and the AliasBench benchmark itself is arguably the bigger contribution: a shared test for a failure mode the field has been hand-waving past.
Researcher view: this is exactly the kind of mechanistic fix that separates research VLAs from production VLAs. Skeptic view: 45.8% is still well below the threshold where you'd trust the system unattended; AliasBench is useful precisely because it's hard.
Anthro Energy and EnPower signed an MoU to jointly develop, manufacture, and scale high-density lithium-ion cells in the US, combining Anthro's Proteus electrolyte platform with EnPower's domestic electrode and pouch-cell production. Target capacity exceeds 750 MWh, with cells specced above 350 Wh/kg aimed at defense, robotics, and autonomous-systems applications.
Why it matters
Robotics has a battery problem and a sourcing problem at the same time. Most humanoid and UAV programs are constrained by sub-300 Wh/kg cells on multi-quarter lead times from Chinese suppliers β and US defense customers can't use those anyway. A domestic 350+ Wh/kg cell line scales the addressable customer base for any robot that needs to operate 6+ hours per charge without a swap, and Jungheinrich's sodium-ion field trial last week shows OEMs are actively looking for alternatives to the standard lithium supply chain.
Industrial-policy read: another piece of the deliberate US/EU effort to reshore the energy-storage stack underneath the robotics boom. Engineering read: 350 Wh/kg is the threshold where humanoid endurance stops being the binding constraint on shift-length deployments.
PAL Robotics announced a new lightweight robotic arm platform β under 10 kg, 3 kg payload, seven degrees of freedom β built around proprietary Series Elastic Actuators ('SEA Arms'). Official unveiling is at ICRA 2026 in Vienna; commercial sales start later this year. The arm is ROS 2 compatible and designed to integrate with PAL's TIAGo Pro OMNI mobile base for mobile-manipulation research.
Why it matters
Series Elastic Actuators are the right call for safe contact-rich manipulation β they trade some position-control crispness for inherent compliance, which is exactly what foundation-model-driven imitation policies need to fail safely. Combined with MolmoAct 2's open weights and 720 hours of bimanual data dropping the same week, the research stack for bimanual manipulation just got materially more accessible: open model + open dataset + a sub-10kg compliant arm at a research price point.
Researcher take: this is the kind of platform that materially changes what a single graduate student can build. Industrial take: 3 kg payload is too light for most factory work β this is a research-and-academic play, not a production manipulator.
South Korean robotics firm WIRobotics raised KRW 95B (~$68M) Series B led by JB Investment to scale its ALLEX humanoid platform. The company is leveraging real-world movement data from 3,000+ deployed WIM walking-assist wearables, has been selected for NVIDIA's Physical AI Fellowship, and counts AWS and global automotive OEMs as partners. Research-platform launch is targeted for later in 2026; mass production targeted for late 2027.
Why it matters
The wearable-to-humanoid transfer is one of the more credible data-strategy plays in the category: WIM's 3,000-unit installed base produces real human-movement data at scale, which is exactly the substrate that RLWRLD's RLDX-1 used to break 70 on RoboCasa Kitchen. Korean OEMs are also visibly accelerating β Holiday Robotics' near-unicorn round, Samsung-owned Rainbow Robotics, and now WIRobotics β turning Korea into the third credible humanoid cluster after Bay Area and China.
Bull: walking-assist data is the cheapest legal way to get high-quality lower-body kinematics at scale. Bear: wearable data doesn't capture manipulation, which is where the value actually is β and ALLEX still needs to prove it can do contact-rich work.
Holiday Robotics closed a KRW 150B (~$112M) Series A at a KRW 1T (~$750M) valuation, reaching near-unicorn status two years after founding and targeting July 2026 commercialization of its Friday humanoid for logistics and manufacturing. Chosun's broader piece frames the Korean wave: WIRobotics' research platform later this year, Samsung-acquired Rainbow Robotics accelerating with manufacturing-scale backing, and Goldman Sachs projecting a $3.5B global humanoid market by 2035.
Why it matters
Korea is becoming the third pole of humanoid commercialization, with a distinct competitive recipe: Samsung/Hyundai manufacturing depth + startup velocity + existing wearable and quadruped IP. The Holiday Robotics valuation jump in two years signals that Korean capital is now willing to underwrite humanoid bets at parity with US Series A multiples.
Strategic read: Korea's chaebol-plus-startup model may resolve the manufacturing scaling problem faster than either pure-startup (Figure) or pure-OEM (Tesla) approaches. Skeptic read: $750M pre-product is the same warning sign that the F Prime report flagged last week β capital is concentrating in 40β50 names at multiples that won't all survive contact with deployment.
Microbot Medical announced first revenues in Q1 2026 following Limited Market Release of the LIBERTY Endovascular Robotic System, with mid-Q2 revenue already exceeding Q1 totals after Full Market Release. The system is now in six US states with doubled account numbers and recurring orders; it's been used for world-first robotic Prostatic Artery Embolization (BPH) and Genicular Artery Embolization (knee OA) procedures.
Why it matters
Endovascular robotics has lagged surgical robotics on adoption for a decade, largely because the unit economics and disposable model didn't pencil. LIBERTY's single-use architecture plus rapid procedure-class expansion (BPH and knee OA were not the original indications) suggests Microbot found a wedge that recurs and scales. The Stereotaxis-Robocath consolidation last week, plus the MAGiC catheter clearance, plus this Microbot ramp β endovascular is having its quiet inflection moment.
Operator view: world-first PAE and GAE procedures expand the addressable market well beyond the original neurovascular pitch. Skeptic view: 'first revenue' is still small absolute dollars, and recurring orders from a small account base don't yet prove scaled hospital adoption.
Shyld AI closed a $13.4M seed led by Aulis Capital to scale autonomous UV-C disinfection robots across US hospitals. The platform runs on VERTEX, a proprietary edge-native foundation model executing on-device. A Stanford study in the American Journal of Infection Control reported >93% contamination reduction versus control rooms. The company is expanding into pharmaceutical cleanroom manufacturing.
Why it matters
Healthcare-associated infections kill ~72,000 Americans annually, and the hospital-disinfection robotics category has been crowded but commodity. Shyld's combination of an edge foundation model plus a Stanford-published efficacy result is the rare pairing where the AI claim has actual peer-reviewed support. The cleanroom-pharma expansion is the move worth watching β pharmaceutical facilities pay 10x what hospitals do for validated automation.
Optimist: this is a wedge play that ends up in every hospital and bio-manufacturing facility on the same flywheel. Realist: UV-C autonomous bots are crowded (Xenex, Tru-D), and the differentiation is in the AI layer that hasn't yet been independently benchmarked outside Stanford.
MHRA published draft regulations on May 14 to reform UK pre-market requirements for medical devices and IVDs, aligning Great Britain's framework with EU MDR/IVDR and IMDRF standards. Key elements: an International Reliance pathway for US/Canada/Australia-cleared devices, mandatory Unique Device Identifiers, a Predetermined Change Control Plan pathway for software medical devices, and tighter equivalence rules. Consultation closes June 19, 2026.
Why it matters
The PCCP pathway is the part that matters most for AI-driven surgical and assistive robotics: it lets vendors pre-declare bounded software updates and ship them without re-clearance. Combined with CMS's NTAP rollback last week and the FDA's evolving guidance discussed at FDLI, the global regulatory grammar for adaptive medical AI is converging much faster than most operators are tracking. The International Reliance pathway also opens a faster UK entry route for US-cleared systems (LIBERTY, Zeta, Vivistim).
Regulatory read: this is the EU MDR pain finally producing a UK-shaped response, with the AI-software accommodation that EU MDR conspicuously lacks. Operator read: companies with FDA clearance and an active PCCP framework just got a materially shorter path to UK market entry.
Siemens Healthineers received FDA clearance for six new systems in its Artis interventional imaging portfolio, all featuring the Optiq AI imaging chain β deep-learning noise reduction for real-time image optimization during interventional procedures. The cleared lineup includes floor, biplane, ceiling, and robotic versions of Artis vision, plus floor configurations for Artis icono.explore and Artis genio.
Why it matters
The interesting clearance detail is that FDA is now granting AI-enhanced imaging across multiple hardware configurations including a robotic platform in a single tranche β a quiet signal that the agency is developing per-AI-chain acceptance criteria that travel across hardware SKUs. For surgical and endovascular robotics players (Stereotaxis MAGiC, Microbot LIBERTY, J&J OTTAVA), this is the regulatory pattern to copy: get the AI subsystem cleared once, then propagate across the hardware family.
Regulatory-strategy read: this is the right precedent at the right moment, and competitors will cite it. Skeptic read: noise-reduction AI is a much lower-risk clearance category than autonomous decision-making, so the precedent only carries so far.
Cerebras Systems debuted on Nasdaq May 14, opening at $350 (nearly 2x its $185 IPO price) and closing at a $100B market cap β the largest US tech IPO since Uber. The Wafer-Scale Engine-3 is pitched at up to 15x faster inference than GPU-based systems for communication-bound workloads. Disclosed partnerships include OpenAI ($20B+), AWS, and expanded cloud inference services; 2025 revenue was $151.6M.
Why it matters
Cerebras going public at $100B is a public-market validation of the thesis that the inference layer will fragment away from NVIDIA. The same day Fractile raised $220M in the UK and SanDisk pitched High-Bandwidth Flash for on-package weights β the architectural space around 'cheaper, faster, more specialized inference' is suddenly very crowded. For robotics, the relevant question is whether wafer-scale economics ever come down to edge form factors, or whether specialized edge silicon (Hailo, Jetson Thor, DEEPX) wins the on-robot tier while wafer-scale owns the training/teleop cloud side.
Bull case: 15x inference advantage on real workloads, hyperscaler customers, and the largest US tech IPO since 2019. Bear case: $100B on $152M of revenue is a ~650x multiple; OpenAI customer concentration is a single-point-of-failure; and the WSE form factor will never touch a robot directly.
UK-based Fractile closed a $220M round (Accel, Founders Fund, Factorial Funds, Gigascale Capital) for custom inference silicon using on-chip SRAM 'memory-compute fusion' instead of HBM, licensing the Andes RISC-V vector core. The company claims up to 100x faster LLM inference and 90% lower opex versus existing accelerators and is in early-stage talks with Anthropic. Datacenter deployment targeted for 2027. This is a materially larger raise than the $200M figure reported when we first noted Anthropic-Fractile conversations two weeks ago.
Why it matters
Fractile lands on the same day as Cerebras's $100B IPO and SanDisk's High-Bandwidth Flash pitch β three different architectural answers to the same inference-bottleneck question, all attracting serious capital in the same week. The memory-compute fusion approach is particularly interesting for robotics: if you can fit a large policy model's weights on-die, you collapse the latency budget that currently forces robots to compromise between policy size and reaction time.
Bull: 100x is the kind of generational leap that justifies a custom-silicon bet, and the Anthropic conversation is signal. Bear: pre-tapeout claims of 100x rarely survive contact with real workloads, and 2027 is a long way away in an inference-silicon market that's reshaping every six months.
Iceotope closed a $26M Series B led by Two Seas Capital and Barclays Climate Ventures to scale its precision liquid cooling for AI infrastructure. The funding deck cites liquid-cooled AI accelerator installations growing from 3 GW to 40 GW within two years as accelerators push past 1 MW per rack.
Why it matters
The thermal envelope is the new bottleneck for the inference-and-training-cloud side of the robotics stack β and a 13x increase in liquid-cooled GW in two years is the kind of step-function that reshapes datacenter design. For robotics specifically, the same thermal-density problem appears in compressed form on humanoids and quadrupeds running large policies: anyone shipping a robot with a Jetson Thor or comparable accelerator at sustained load is facing the same fluid-vs-air tradeoff at miniaturized scale.
Datacenter view: liquid cooling stops being a hyperscaler-only luxury this year. Robotics view: compact two-phase cooling solutions are exactly the kind of supplier base humanoid OEMs will need within 24 months.
Shanghai Chauffeur Robotics (ζ΄εζΊε¨δΊΊ) officially broke the Guinness World Record for industrial-robot payload with the CR5000-3700, lifting 5,000.36 kg β more than double the 2,300 kg FANUC mark held since 2016. The robot is already in commercial deployment in heavy manufacturing, ports, railways, tunneling, aerospace, and nuclear sectors across China.
Why it matters
Heavy-payload arms are a segment where Japanese (FANUC, Yaskawa) and German (KUKA) incumbents have historically owned the high end with multi-decade reliability records. A Chinese OEM doubling the public benchmark and shipping into nuclear and aerospace customers signals real progress on the precision-component side β RV reducers, servo drives, control loops β that has been the historical chokepoint for Chinese industrial robotics. The Fanuc-Google partnership last week is the other half of this story: incumbents are racing to add AI as the new differentiator just as the hardware moat erodes.
Industry read: this is the heavy-arm equivalent of Unitree's pricing assault on quadrupeds β Chinese OEMs are catching the spec sheet first, then competing on price. Skeptic read: a Guinness payload number is a marketing artifact; reliability over 20 years at full load is what FANUC actually sells.
Ambi Robotics' AmbiStack β an AI-powered system for autonomously stacking uniform and non-uniform packages without prior knowledge of item properties β won the 2026 RBR50 Innovation Award. The system is deployed in high-volume logistics hubs and Ambi reports 5x throughput improvements and 68%+ stack density relative to baseline operations.
Why it matters
Stacking is one of those problems that looks simple and isn't β irregular SKUs, friction-coefficient variation, and stack-stability planning kill most rule-based approaches. A learned system delivering 5x throughput in real customer facilities is exactly the operational evidence Universal Robots' CEO referenced last week when he argued purpose-built non-humanoid automation still wins most factory questions. The RBR50 award is a recognized signal in the category.
Operator read: 68% stack density is a real number; pallet-cube utilization is the metric that pays the warehouse mortgage. Competitive read: Ambi has been quietly accumulating reference customers while the humanoid press cycle ate the attention.
Voltempo, with Berkeley Coachworks building the prototype, is developing a 15.6-meter cab-less rigid autonomous truck that increases payload from 32 to 42 tonnes by removing the cab entirely. UK road trials are planned for late 2027, with production orders late 2027 and deliveries from mid-2028.
Why it matters
Most autonomous-trucking development still bolts a sensor stack onto a conventional Class 8 vehicle. Voltempo is one of the first programs willing to rethink the vehicle architecture around the absence of a driver β the same conceptual move Geely made with EVA Cab in robotaxis. The 31% payload uplift is the economically meaningful number; it's the kind of structural advantage that survives even modest autonomy maturity. It also tests the UK regulatory pathway opened by the WayveβDfBT MoU signed the same day.
Industry read: this is the first cab-less truck in the West with a credible build partner and a 2027β2028 timeline β and the form-factor advantage compounds with every percentage point of fuel and payload efficiency. Skeptic read: cab-less means no fallback driver and no maintenance shelter, which is precisely the operational problem FreightWaves' editorial flagged this week.
Lin Junyang, the lead researcher behind Alibaba's Qwen LLM family, has launched a new AI lab focused on world models and embodied intelligence for robots, raising capital at roughly a $2B valuation with backing from multiple large enterprises. The pitch is to apply foundation-model methodology to robotics β explicitly mirroring his Qwen trajectory but with proprietary robotics data flywheels rather than open releases.
Why it matters
This is a meaningful talent migration: one of the most credible LLM researchers in China just pivoted to embodied AI at a unicorn valuation pre-product. It signals the same conviction Jim Fan articulated last week β that the LLM playbook is now a written script for robotics β but with Chinese capital and Chinese data, and an explicit move away from the open-source posture Qwen made him famous for. The strategic tension between open and closed embodied-AI stacks (AI2 dropping MolmoAct 2 on the same day cuts the other direction) is now the defining question for the category.
Optimist view: Lin's pedigree plus enterprise backing accelerates China's embodied-AI lead and pressures Western labs on data scale. Skeptic view: $2B pre-product is exactly the bubble metric people were warning about; robotics flywheels are vastly harder to spin than language ones, and proprietary data strategies have a worse track record than open ones in early-stage foundation work.
Gasgoo reports a coordinated Tier-1 push into embodied AI: Bosch partnering with Qianxun Intelligence to close the data-to-deployment loop, Aptiv and Valeo pulling automotive perception and control IP into robotics platforms, and Schaeffler β already a long-running thread here with its 1,000-unit Hexagon AEON commitment, VinDynamics data-sharing deal, and three-digit-million-EUR revenue forecast for 2030 β explicitly targeting thousands of deployed humanoids by 2035. China is the primary deployment and testing ground across all four.
Why it matters
This piece surfaces the structural pattern behind the individual Schaeffler deals we've been tracking since April. The Tier-1 auto supply chain has precision actuator manufacturing at scale, decades of perception/control IP, and global factory footprints for both deployment and real-world data capture β exactly the three things humanoid OEMs currently lack. If the consolidation pattern holds, the humanoid supply chain ends up looking like Bosch/Schaeffler/ZF owning the joints and a handful of software-differentiated OEMs assembling around them. The Fanuc-Google/Intrinsic pairing last week is the incumbents' counter-move: add AI to the existing hardware moat before Chinese Tier-1s and software players can triangulate around it.
Auto-supplier read: this is exactly the playbook that worked for ADAS β own the modules, sell to anyone. Humanoid-OEM read: Tier-1 dependency is the same trap that hollowed out auto OEMs; the differentiation has to move up the stack to software and behavior fast.
The deployment phase, not the demo phase AGIBOT explicitly framed 2026 as humanoid robotics' 'deployment phase,' and the day's news supports it: Schaeffler-Humanoid moves to multi-thousand RaaS contracts, AmbiStack wins RBR50 with 5x throughput in live logistics hubs, M&S breaks ground on a Β£340M automated DC. The competitive question is shifting from 'can it work?' to 'can you run it for a year?'
Tier-1 auto suppliers buy their way into embodied AI Bosch (with Qianxun), Schaeffler (Humanoid + Hermes Award for its actuator platform), Aptiv, and Valeo are all pulling automotive perception and actuation IP into robotics. The strategic implication: the humanoid supply chain may end up looking less like consumer electronics and more like Tier-1 auto, with a handful of integrated module suppliers owning the high-margin joints.
Inference silicon is fragmenting fast Cerebras IPO'd at $100B, Fractile raised $220M in the UK, SanDisk pitched High-Bandwidth Flash for on-package model weights, and DEEPX shipped a 25-TOPS edge module at 1β5W. NVIDIA's 85β90% share is intact, but the alternatives now have real capital and real customers β and robotics is one of the workloads driving the diversification.
Regulators are catching up to autonomy in two domains at once MHRA proposed an EU/IMDRF-aligned UK medical device overhaul with a Predetermined Change Control Plan for AI software; CMS continues tightening Breakthrough Device reimbursement; California finalized heavy-duty AV rules after a 13-year process; UK signed an MoU with Wayve. The compliance surface for both surgical robots and autonomous trucks is solidifying in 2026.
Dexterity is the new locomotion RLDX-1 broke 70 on RoboCasa Kitchen, AI2 open-sourced MolmoAct 2 with 720+ hours of bimanual data, IntentVLA tackled observation aliasing. The center of gravity in robot foundation models has moved from 'can it walk?' to 'can it pour, fold, and assemble?' β the 25β50% of industrial work that legacy automation can't touch.
What to Expect
2026-05-28—Texas SB 2807 takes effect β DMV authorization required for commercial AV operations, AVRAC stands up.
2026-06-15—IEEE Signal Processing Society 'Networked AI' paper submission deadline.
2026-06-19—MHRA UK medical devices reform public consultation closes.
2026-06-XX—ICRA 2026 in Vienna β PAL Robotics' new 7-DoF SEA arm, Clearpath research papers, and dexterity benchmarks expected to dominate.
2026-12-XX—HumanoidβSchaeffler Phase 1 deployment begins at Herzogenaurach (box handling) and Schweinfurt.
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