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Digit

Digit is a bipedal developed by Agility Robotics, a company founded in 2015 as a spin-off from , designed for autonomous performance of repetitive logistics tasks such as picking, packing, and transporting payloads in and settings. Standing approximately 1.75 meters (5 feet 9 inches) tall and capable of carrying up to 16 kilograms (35 pounds), Digit features backward-bending knees for squatting and dynamic balance, integrated sensors for and object manipulation, and end-effectors optimized for handling totes and boxes. Introduced in its current commercial form in 2023, Digit emphasizes reliability for multi-hour autonomous operation, including self-docking for charging and integration with existing systems via signaling and visual fiducials like QR codes. Agility Robotics has achieved initial commercial deployments, including pilots with in 2024 and testing at facilities, marking Digit as the first to enter real-world trials at scale. In 2024, it was recognized as the RBR50 Robot of the Year for pioneering applications in industry, with production scaling via a dedicated factory in , and plans for deployment across over 100 sites by partners like Schaeffler. Recent advancements include extended battery life up to four hours and enhanced whole-body control for complex manipulation, positioning Digit as a frontrunner in economically viable amid labor shortages.

Development and history

Origins at Oregon State University

The foundational research for the Digit humanoid robot emerged from 's (OSU) Dynamic Robotics Laboratory (DRL), where efforts centered on developing principles for dynamic bipedal locomotion capable of navigating human environments. Led by robotics professor Jonathan Hurst, the lab's work emphasized physics-based models of stability and energy efficiency in legged systems, drawing from early prototypes like ATRIAS, a dynamic bipedal platform designed to explore agile walking and running gaits on varied terrains. This approach prioritized deriving control strategies from mechanical fundamentals, such as dynamics and passive , to enable robust performance without over-reliance on high-level abstractions. Key advancements included the robot, a bipedal platform developed at that demonstrated autonomous running on outdoor uneven using integrated with low-level controllers for balance and propulsion. Cassie's design incorporated lightweight actuators and spring-mass mechanics inspired by biological locomotion principles, allowing it to traverse rough surfaces like gravel paths while maintaining stability through real-time adaptation to perturbations—capabilities that directly informed Digit's eventual bipedal architecture for tasks. The research highlighted causal mechanisms of generation, such as optimizing foot placement and torques via hybrid zero , to achieve human-like versatility over wheeled alternatives. This academic work, partially funded by (NSF) grants supporting foundational studies, laid the groundwork for commercializing bipedal technologies without initial dependence on private . OSU's emphasis on empirical testing in real-world conditions, rather than simulation-heavy methods, ensured the robustness of locomotion algorithms tested on prototypes like , which completed endurance runs exceeding 5 kilometers on campus terrain in 2021 demonstrations rooted in earlier lab efforts. By 2015, these insights culminated in the spin-off of Agility Robotics to translate DRL's bipedal stability research into practical humanoid systems, marking the transition from university experimentation to industry application.

Founding of Agility Robotics and initial prototypes

Agility Robotics was founded in late 2015 as a from State University's Dynamic Robotics Laboratory by Jonathan Hurst, Damion Shelton, and Mikhail Jones, all affiliated with the university's research efforts. The venture aimed to commercialize advancements in legged , transitioning from academic prototypes to market-viable systems capable of dynamic bipedal . Initial funding included a seed round of $792,000 raised in 2016 to support early development. This capital enabled the company to build on prior lab work, producing the bipedal platform in 2016 as its first commercial , designed for traversing rough terrain and overcoming obstacles through efficient, energy-minimizing gait algorithms derived from passive dynamic principles. Cassie's architecture laid the groundwork for subsequent designs, incorporating lightweight actuators and whole-body coordination to achieve stable, human-like mobility without excessive computational overhead. By 2019–2020, advanced to Digit prototypes, with pre-production units demonstrated in partnerships such as with , showcasing bipedal manipulation for tote handling in unstructured environments. These early iterations prioritized practical utility over anthropomorphic novelty, focusing on cost-effective hardware that enabled autonomous navigation and payload carrying in real-world settings. The shift toward applications emerged from recognizing persistent labor shortages in warehousing—over 1 million unfilled roles in the U.S. alone—driving innovation toward robots that could integrate into existing human workflows rather than requiring facility redesigns. This market-oriented pivot emphasized scalable, deployable systems over purely research-oriented feats, informed by empirical testing of mobility constraints in industrial contexts.

Key milestones from 2020 to 2025

In January 2020, Agility Robotics partnered with to deploy Digit for testing in automotive facilities, showcasing its bipedal capabilities including squatting and carrying loads up to 40 pounds (18 kg) in human environments such as warehouses and delivery tasks. On March 20, 2023, Agility unveiled the next generation of Digit at ProMat, incorporating improved AI-driven perception, manipulation, and mobility for multi-purpose logistics applications like tote handling in unstructured settings. In June 2024, Digit achieved its first commercial deployment under a multi-year robots-as-a-service agreement with at a warehouse, following proof-of-concept pilots, with additional testing in facilities to handle repetitive heavy lifting tasks. Concurrently, Agility's RoboFab facility in —the world's first full-scale factory dedicated to production—ramped up operations, enabling initial customer deliveries of Digit units. At ProMat 2025 on March 31, announced enhancements to Digit, including advanced protocols compliant with ISO standards, extended life for longer shifts, autonomous for recharging, and expanded task versatility such as integrating with autonomous robots for end-to-end workflows.

Design and technical specifications

Hardware components

Digit employs a bipedal structure designed for navigation and tasks in human-centric environments, with a standing height of 5 feet 9 inches (175 cm) and a weight of approximately 65 kg. This configuration includes extended arms that contribute to balance during locomotion and manipulation, a housing sensors and computing elements, and legs with a compliant, dynamic for on varied surfaces. The robot's actuation system utilizes custom electric actuators, primarily brushless DC motors paired with series-elastic actuators (SEAs) to enable torque-controlled joints across its . These components provide high density and , allowing for force-sensitive interactions and fatigue-resistant operation in repetitive cycles without the wear associated with pneumatic or hydraulic alternatives. Manipulation is facilitated by customizable end-effector optimized for grasping and handling payloads up to 35 pounds (16 kg), such as boxes or totes, with designs emphasizing reliability over extreme dexterity. is supplied by a 1.2 kWh lithium-polymer , delivering up to 4 hours of continuous operation under typical loads, complemented by an autonomous system for efficient recharging that minimizes manual intervention.

Software architecture and AI integration

Digit's software architecture employs a hierarchical whole-body stack that coordinates , manipulation, and balance across its , enabling dynamic task execution in unstructured environments. This stack integrates low-level control with higher-level planning modules, where (RL) policies handle decision-making for tasks such as picking, placing objects, and navigating obstacles. The system prioritizes onboard computation for latency-sensitive operations, reducing dependence on processing to ensure robustness in variable real-world conditions like uneven floors or dynamic clutter. AI integration relies heavily on RL trained through simulation-to-real (sim-to-real) transfer, leveraging platforms like Isaac Lab to generate millions of parallel training episodes in photorealistic digital twins of Digit. These simulations model detailed physics, including joint dynamics and environmental interactions, allowing policies to refine whole-body controllers—termed Digit's ""—before zero-shot deployment on hardware without further real-world fine-tuning. For instance, locomotion policies trained in simulation as of April 2024 demonstrated effective to Digit's bipedal , adapting to perturbations and variations observed in empirical tests. Manipulation capabilities extend this approach, with RL enabling end-effector for grasping irregular payloads in cluttered spaces, informed by dense free-space maps generated from onboard sensors. Perception systems incorporate causal models to interpret sensor data—such as from RGB-D cameras and —focusing on object affordances and environmental rather than purely data-driven correlations, which enhances to novel scenarios without extensive retraining. This is supported by NVIDIA-accelerated for feature extraction and , as expanded in Agility's announced March 2025, allowing Digit to process visual cues for path planning and collision avoidance independently of external . Empirical validation shows these components enable sustained for hours-long operations, with failure rates minimized through sim-pretrained safeguards against edge cases like occlusions or lighting changes.

Capabilities and operations

Mobility and manipulation features

Digit utilizes bipedal locomotion engineered for settings, enabling it to traverse uneven surfaces at speeds up to 5 km/h while maintaining stability. Its legged design incorporates arm-assisted balance mechanisms, which provide dynamic support during loaded operations, preventing falls and ensuring reliable forward progress even under payloads reaching 16 kg. For manipulation, Digit is equipped with specialized end effectors and robust limbs that facilitate dexterous grasping of diverse objects, such as totes weighing up to 16 kg and bins requiring precise handling. Recent enhancements, announced in March 2025, expand grasping angles and motion ranges, allowing for more versatile in repetitive tasks. A suite of integrated sensors supports these capabilities by providing for detection, , and accurate positioning during and grasping sequences. This , including elements for and local navigation, ensures mechanical precision without reliance on higher-level systems.

Autonomy and task performance in logistics

Digit demonstrates AI-driven autonomy in managing repetitive logistics workflows, including stacking and unstacking totes, palletizing and depalletizing loads, and sorting items within warehouse environments. These capabilities enable end-to-end execution of task sequences with reduced human intervention, as Digit perceives, manipulates, and navigates unstructured indoor spaces using full-stack AI without reliance on pre-mapped environments or external fiducials. Integration with warehouse management systems via the Agility Arc cloud platform allows real-time adaptation to workflow variations, such as handling differently shaped or positioned objects through perception-based learning. Performance in these tasks includes handling payloads up to 35 pounds while maintaining , with demonstrations showing Digit processing 66 totes per hour at a cycle success rate exceeding 98%. extends to coordinating with autonomous mobile robots (AMRs), where Digit independently dispatches them to relocate items—such as from sorting stations to packout areas—streamlining multi-step processes without constant oversight. This foundation, powered by foundation models for whole-body control, supports generalization to novel item configurations encountered in dynamic settings. The Robots-as-a-Service (RaaS) model underpins scalable deployment, enabling fleets of Digits to integrate into facilities with minimal setup—often in hours or days—via Agility Arc's tools for monitoring, task assignment, and autonomous docking for recharging. Enhanced battery management yields up to four hours of continuous operation per charge, minimizing downtime and facilitating 24/7 task performance in repetitive, labor-intensive roles like tote manipulation from conveyors or racks.

Commercial deployment and scaling

Early pilots and partnerships

In December 2023, Agility Robotics initiated a proof-of-concept pilot with at a warehouse facility outside Atlanta, Georgia, where Digit robots performed initial tasks such as alongside human workers. This collaboration marked one of the earliest real-world tests of a in a commercial environment, emphasizing safe integration with existing operations and task validation for repetitive picking and moving activities. Building on the pilot's success, GXO and announced an industry-first multi-year Robots-as-a-Service (RaaS) agreement on June 27, 2024, to deploy Digit for tote and handling in high-volume settings. Deployment commenced on October 3, 2024, at the same GXO-managed facility, representing the first formal commercial application of robots, with Digits autonomously transporting plastic totes between workstations to assess and rates in dynamic environments. These operations prioritized capture on robot-human interactions and task , enabling iterative software refinements for enhanced reliability. Concurrently, expanded pilots with in early 2024, with public confirmation of testing in fulfillment centers by late , focusing on broader integration such as package manipulation and navigation in unstructured spaces. These R&D efforts, building on prior demonstrations announced in October 2023, involved deploying early Digit units to gather performance metrics under varying workloads, validating adaptability for demands without disrupting human workflows. The pilots underscored Digit's potential for scalable data-driven improvements, though full operational metrics remained proprietary at this stage.

Production expansion and fleet operations

In September 2023, Agility Robotics announced the opening of RoboFab, a dedicated manufacturing facility in , designed as the world's first factory for commercial robots. Construction of the 70,000-square-foot plant began in 2022, with initial operations commencing in late 2023 and full production ramping up in 2024. The facility targets initial output of hundreds of Digit units in its first full year, with scalable capacity exceeding 10,000 units annually as demand grows into the late 2020s. This expansion supports through modular lines optimized for humanoid components, enabling faster iteration and volume efficiencies. Agility's fleet operations center on the Agility cloud-based platform, launched in March 2024, which facilitates remote monitoring, software updates, and task orchestration for deployed Digit units. The system integrates fleet-wide diagnostics, autonomous charging coordination, and performance analytics, allowing operators to manage multiple robots across sites without on-site intervention. By enabling over-the-air deployments of new skills and maintenance protocols, Agility reduces downtime and supports scalable operations in environments. High-volume manufacturing at RoboFab is projected to drive cost reductions via , with initial unit economics targeting under two years compared to human labor at $30 per hour fully loaded. Leasing models priced at $10–$12 per hour further optimize fleet , prioritizing operational uptime over upfront outlays. These strategies position Digit fleets for broad deployment, focusing on repetitive tasks while minimizing oversight requirements.

Reception, impact, and controversies

Industry achievements and adoption

Digit earned prominent industry accolades in 2024, including selection as one of TIME's 200 Best Inventions of the year for its role in advancing AI-trained capabilities for tasks, and the RBR50 Robot of the Year award from The Robot Report for leading commercial trials of bipedal in environments. These recognitions highlight Digit's early success in transitioning from prototypes to operational deployments, with initial pilots at facilities operated by third-party provider GXO and apparel manufacturer marking it as the first commercially deployed for tote handling and material movement. In addressing persistent labor shortages in —where repetitive, physically demanding tasks like contribute to high turnover—Digit has demonstrated operational reliability surpassing human workers' consistency, achieving 98-100% availability rates in real-world settings while processing over 300,000 orders without fatigue-related interruptions. This uptime edge stems from Digit's design for continuous operation alongside autonomous mobile robots, enabling scalable in dynamic warehouse flows without the scheduling constraints of human labor. Agility Robotics' adoption of a Robotics-as-a-Service (RaaS) model further propels Digit's by offering subscription-based access—including robots, software, and maintenance—which lowers upfront costs and facilitates fleet scaling for operators. Priced at approximately $30 per hour under this framework, the model supports broader integration of technology into existing workflows, as evidenced by multi-year agreements with providers like GXO for deployments across multiple sites.

Economic benefits and labor market realities

The introduction of Digit robots into warehouse operations mitigates U.S. labor shortages, where approximately 7 million jobs remain unfilled despite an rate of 4.1% in April 2025, driven by demographic shifts including a doubled proportion of workers over 60 in roles. Digit's capacity for continuous 24/7 deployment without fatigue or shift constraints addresses these gaps, enabling sustained throughput in repetitive tasks like tote handling and thereby enhancing efficiency in facilities facing persistent hourly worker demand, with over 320,000 unique job openings posted between December 2024 and April 2025. This operational resilience supports broader economic productivity, as in can yield efficiency gains projected to increase global output by up to 240% in labor-intensive sectors through near-zero marginal costs for scaled robotic labor. Agility Robotics substantiates Digit's at under two years per unit, calculated against labor costs of about $30 per hour for equivalent tasks, with conventional purchase models providing immediate hardware upgrades for systems . Pilot deployments demonstrate Digit's performance in dynamic environments, handling 35-pound payloads over a 5.5-foot range to achieve labor savings that offset the $250,000 through reduced turnover and injury-related downtime, as reallocates workers to supervisory roles while lowering overall operational expenses. Such verifiable counter efficiency loss fears, with systems like Digit enabling 20-30% uplifts in key industries by 2030 via optimized routing and error minimization. Historical data on automation-prone occupations reveal net growth rather than contraction, as seen in expansions of roles in and services post-technological since 1999, where displaced routine tasks spurred demand for complementary higher- positions in programming, maintenance, and oversight. Fears of mass displacement from humanoid robotics overlook this pattern, evidenced by stable U.S. labor markets post-AI introductions like , with no broad spikes and projections indicating automation's role in augmenting rather than supplanting workforce expansion through skill shifts. In specifically, Digit's focus on unstructured, repetitive work preserves human in adaptive , aligning with empirical trends where technological has historically elevated aggregate job quality and GDP contributions via efficiency compounding.

Criticisms regarding job displacement and safety

Critics have raised concerns that deployment of Digit robots in warehouses could lead to significant job displacement for blue-collar workers, particularly in repetitive logistics tasks such as tote handling. In October 2023, Amazon initiated pilots with Digit at its facilities, prompting fears that the robots' ability to autonomously move empty containers would shift humans out of these roles, exacerbating unemployment in an industry already strained by automation trends. However, data from early commercial deployments indicate that Digit primarily augments human labor by addressing persistent shortages in undesirable tasks, rather than outright replacement. As of November 2024, Agility Robotics reported Digit units operating in factories and warehouses to fill labor gaps, performing dull or hazardous duties alongside workers, with no verified instances of net job losses in partnered sites; instead, such integrations have coincided with opportunities for human retraining toward supervisory or maintenance roles. Safety criticisms focus on the inherent risks posed by Digit's form in shared human environments, including potential for collisions, pinching injuries, or severe harm from falls given its weight exceeding 100 pounds (45 kg). analyses highlight physical as the primary concern, with malfunctions in actuators or sensors potentially leading to unintended interactions; itself acknowledges that falls are unavoidable in dynamic settings, raising questions about real-world reliability despite ISO-aligned design principles like emergency deceleration protocols. Counterarguments emphasize Digit's engineered safety features, such as rapid stop mechanisms and human-centric programming, which have enabled its status as the first humanoid to achieve commercial deployment without reported incidents, potentially yielding lower error rates than humans in monotonous tasks prone to fatigue-related accidents. While early glitches in pilots have been noted, aggregated data from suggests humanoid systems like Digit reduce overall workplace injuries by handling repetitive loads, though long-term empirical validation remains limited. Regulatory hurdles and union opposition further amplify these debates, with calls for bespoke standards addressing humanoid-specific risks like stability and liability, which have delayed broader rollout as bodies like OSHA grapple with uncharted liabilities. Unions have critiqued such integrations for prioritizing capital over labor without compensatory measures, yet this stance is argued to impede amid documented mismatches like chronic staffing shortfalls—exacerbated by demographic shifts—where robots target vacancies rather than incumbents, potentially hindering economic adaptation without resolving underlying skill gaps.

Future developments

Announced innovations and roadmap

In March 2025, Agility Robotics announced enhancements to Digit's capabilities, including expanded battery life enabling up to four hours of continuous operation and autonomous docking to charging stations for seamless workflow integration. These updates also introduced improved safety features compliant with industry standards, facilitating safer collaborative operations with human workers through advanced perception and obstacle avoidance systems. Concurrently, the company expanded its partnership with NVIDIA to leverage accelerated computing for training Digit's whole-body control foundation model, enhancing perception for dynamic environments and human-robot interaction. Looking further ahead, Agility Robotics outlined plans for Digit v5 rollout by the end of 2025, incorporating refinements to navigation stacks optimized for unstructured real-world sites beyond controlled settings. The company envisions scaling Digit toward general-purpose applications in sectors like and , driven by iterative model improvements that capitalize on compute scaling to achieve broader task versatility without sector-specific redesigns. Agility has committed to deployment strategies emphasizing empirical safety validation through field data collection, rather than reliance on unproven regulatory frameworks, to ensure verifiable risk mitigation in humanoid operations. This approach prioritizes measurable outcomes from ongoing pilots to inform iterative hardware and software upgrades.

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