An AI platform to deliver human-like reasoning & autonomy for commercial and defense applications February 28, 2024

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SARCOS AT-A-GLANCE Figures on this page are current as of January 2024. Robotics DNA 30+ years in robotics and robotics software. Legacy leadership in dexterous mobile robot business across aviation, construction, energy, and defense sectors NASDAQ STRC Experience 30+ years of robotics engineering excellence. Technology team led by CTO with 25+ years of AI/ML expertise Salt Lake City, UT Innovation and operations 60+ team members, world-class robotics & AI/ML software engineers

Sarcos: 40 years of innovation and evolution Sarcos Robotics starts trading - STRC (2021) Purchase from Raytheon (2015) 1983 2015 2021 2024 New AI software focus (2023) Anticipated AI/ML Software Framework Launch and Customer Trials Government/DoD R&D Sarcos spins out of University of Utah (1983) Dexterous Robotic Systems Purpose-Built Solutions AI Software 2023 Start AI/ML Software Development (2019) Raytheon buys Sarcos (2007)

Our Vision: To enable machines to observe, learn, reason and act like humans Substantially accelerate speed of programming and training Increase agility, task sets and use cases Reduce need for human intervention and oversight Reduce cost of standing up and maintaining automation For mobile machines, evolve from human-in-the-loop to human-on-the-loop Eliminate need for continuous cloud connectivity

Sarcos AI Tackles SOME OF the biggest challenges in robotics Implementation of industrial robotics can take a long time and be very costly Most industrial robots are highly programmed for a specific task Today’s AI approaches (e.g., LLM1 for generative AI) require vast amounts of training data and are power hungry Sensors are generally discrete and not optimized to work in unison or to adapt and adjust for lost functionality of a single sensor 1. Large language models.

enable machines to observe, learn, reason and act like humans Real-time Closed-loop Autonomy solves tasks for dynamic and unstructured environments Completing complex tasks Disparate point solutions Addresses key challenges in robotic deployments: High cost of programming, deployment, and downtime Adapting to unexpected events require manual intervention or re-programming Commercial Robots Hardware-agnostic, real-time closed-loop autonomy software solution Addresses key challenges in traditional robotic deployments: Infrastructure Maintenance & Repair Construction Industrial Mfg. Aerospace/Aviation Logistics Defense Energy High cost and complexity of programming and deployment Unsafe and inefficient operations in unstructured and dynamic environments Point solutions unable to learn and adapt in real time, require re-training to perform new or modified tasks

AI for the Real (physical) world Most AI today lives In the digital world Occurs on the robot without a connection to the cloud. “The key for us is enabling autonomy in an unstructured environment that can dynamically change. We focus on generalized autonomy, providing closed-loop functionality to adapt to tasks continuously.” -Denis Garagić, Sarcos Chief Technology Officer Objective is to predict outcomes and make recommendations to empower humans - make more efficient, make better decisions, optimize processes, develop new products, etc. Harnesses enormous amounts data utilizing significant cloud-based computing to gather, ingest, integrate, analyze and learn from data Digital World AI/ML Approach Objective is to enable machines to effectively operate autonomously in real world environments (structured, dynamic, and unstructured) Algorithms enable machines to react to changing circumstances and complete tasks without re-training or reprogramming Requires less data – uses on-robot1 compute to ingest, integrate (fuse), analyze, learn, and react to changing circumstances without connecting to the cloud Sarcos’ Real-World AI/ML Approach

Sarcos’ AI and ML Software Platform Real-time Closed-loop autonomy software framework + edge computing & training hardware Observe Learn Reason Act Learning occurs with minimal demonstrations (1-5) Dynamic Reasoning and Learning for novel task combinations or entirely new tasks Model adaptation to specific environments Perceives environments with different sensor modalities e.g., vision, LiDAR, Radar, acoustic, etc. Utilizes Multi-Modal Sensor Fusion to make perception more robust to sensor occlusion and noise Improves Situational Awareness (SA) Adapting to unexpected events New motion plan based on observations Closes the autonomy loop by applying human-like reasoning to determine the best course of action; completes the task Control manipulator arm, robot and / or end effector Achieve goal in a stable, safe, and controlled manner Real-time Closed-loop Autonomy Framework Real-time perceiving, learning & decision-making occurs on-robot without retraining or cloud connectivity 1 2 3 4

Sarcos AI/ML Software Platform for robotics Certain teleoperation devices may not be included as part of our sales package. Laptops are not sold as a part of the system; assumes customers will source separately or use existing company assets. 3. Designed to work with most industrial robots being sold today. According to the Proficient Market Insights’ “Global Robot Operating System” report, ROS 1 robots comprised of 74% of the total ROS market in 2021, “Global Robot Operating System (ROS) Market 2022 Size Of $ (globenewswire.com) Designed to Maximize System Flexibility, Adaptability, Mobility, and Learning Industrial Robots (For illustrative purpose only; design is subject to change) Training/Teleoperation HMI Package1 Motion Tracking Hand Controllers Sarcos Control Interface Module CLA Software Framework Algorithms/Libraries 2 2 User Interface2 HMI Secure Cloud Portal2 Connection not required for autonomous robot operations Various Robotic Platforms3

Sarcos ai platform: Expected Advantages How our approach differs Full stack, closed-loop autonomy enables adaptability to dynamic changes in environment or defined task without human intervention or reprogramming Uses probabilistic machine learning (ML) techniques to learn the task, accounting for uncertainty and variability Dynamic model inference methods require much less training data; robots can learn to generalize with only a few demonstrations (1~ 5) Computational efficiencies gained through use of Sarcos’ domain-specific language models Hardware agnostic1 Addresses robotic-specific challenges beyond integration Solves for system stability and pose estimation/end effector orientation Solves long-horizon tasks in arbitrary human environments Fuses multi-sensor data inputs together to improve system flexibility & adaptability Flexible instructional input options for task model learning (i.e., LLMs, DSLs2, motion-capture-based teleoperation, AR/VR, video input, etc.) Can provide language-to-motion instructions ideal for edge computing/robotics applications; doesn’t require cost/latency associated with use of LLMs requiring connectivity to the Cloud Complex task learning capabilities are similar to humans; in some cases, we believe robots can be trained in orders- of-magnitude less time than it takes relying on current state-of-the-art approaches3 Enables on-device computing; lower total cost of ownership (TCO) with no need to incur recurring cloud services costs Improves system implementation and startup times Designed to work with most industrial robots being sold today. According to the Proficient Market Insights’ “Global Robot Operating System” report, ROS 1 robots comprised of 74% of the total ROS market in 2021, “Global Robot Operating System (ROS) Market 2022 Size Of $ (globenewswire.com) Domain specific languages. Robotics Transformer 1 & 2 deep learning-based approach, 2022 – 2023.

Hardware agnostic1 Expected to enable stationary and mobile robotic platforms to be agile and autonomous, reduce human intervention and increase ROI Industrial Robots and Cobots Unmanned Aerial Vehicles Unmanned Ground Vehicles and Humanoids 1. Designed to work with most industrial robots being sold today. According to the Proficient Market Insights’ “Global Robot Operating System” report, ROS 1 robots comprised of 74% of the total ROS market in 2021, “Global Robot Operating System (ROS) Market 2022 Size Of $ (globenewswire.com)

Benefits of computing on the edge Complex extremely large data set integration Enormous amounts of cloud compute required Predict outcomes, make recommendations driven by large data sets and models Humans utilize in decision making, process improvement and optimizations Environmental, situational awareness data from local workspace, more constrained (domain specific approach) On robot real-time human like reasoning applied to base models based on unexpected events “Closed loop” – adapting to those events real-time and update base models without retraining Structured and unstructured environments without retraining Traditional AI / ML Product Solution (Cloud Compute1,2) VS. Closed-Loop Autonomy for Robotics (Edge Compute) C3.ai: A Full-Stack IoT Platform for Everyone The Gordian Knot of Structured Programming (c3.ai), . Photo Source: c3.ai

hidden costs of Power-Hungry AI approaches How it’s done today “You’ll be astonished how much power it takes to generate a single AI image1” Photo Source: FreePik “The Gordian Knot of Structured Programming4 ” Photo Source: c3.ai “RT-1: Robotics Transformer for real-world control at scale2” Photo Source: Google Research “RT-2: Vision-Language-Action Models3” Stable Diffusion's open-source XL model used almost as much power per image as that required to fully charge a smartphone Creating 1,000 images using same model generated CO2 emission equivalent of 4.1 miles driven by a gas-powered car. AI Power usage by AI servers on a global scale is equivalent to what Argentina uses in 1 year. Google reported1 it used 5.6 billion gallons of water to cool their AI servers in 2022 (20% increase over 2021). Example: Model trained on real-world robotics dataset: 130k episodes 700+ tasks collected from 13 robots over 17 mos. “….the model size: 5B vs 55B for the RT-2 PaLI-X variant..” “The ‘build it yourself’ approach requires numerous integrations of underlying components that were not designed to work together, resulting in a degree of complexity that overwhelms even the best development teams.” CO2 AI 1. Futurism.com 2. Google Research Blog 3. robotics-transformer2.github.io 4. c3.ai

Potential use cases Examples based on discussions with potential customers

Tasks & CHALLENGES Changes in production line (products, fixes, updates) come at high cost – robot retraining and manufacturing downtime Sub parts Assembly manufacturing Structured manufacturing line, task variability Opportunity & expected benefits Low cost/quickly able to repurpose manipulators/ robots to perform new task. Minimal production downtime for new task training. Employee can train in AR, deploy models across robots quickly Quickly adapt to varying tasks on a multi-product assembly line set up Run assembly lines with mixed products to meet demand Robots automatically adapt tasks to be performed based on object detected) Provides flexibility & future proof task planning; extends usability & life of robot

Tasks Identify and torque bolts to spec on large steel structures; bridges, buildings, manufacturing facilities, etc. Inspection and repair of damaged bolts, moving/ aligning/securing steel beams (a.k.a. Cooning)  build & repairsteel structures construction Unstructured, ground-level/at-height, in-door/out-door, heavy tools Opportunity & expected benefits Precision detection of bolts and placement of tool. or traditional training models. Adapt to varying environmental conditions at height to complete job Operate safely as environment changes to ensure safety of personnel – reduce risks associated with at-height work in inclement weather. challenges Highly unstructured environment + at-height risks Level of precision and speed required not achievable with teleoperation

Tasks Unmanned aerial vehicles Persistent detection, tracking, and classification Defense / commercial Unstructured, in-flight Opportunity & expected benefits Persistent sensor-based detection, tracking & classification resolves representation uncertainty and enhances situational awareness Shared situation and / or navigation across UAVs enhances the collective knowledge and understanding of the entire fleet challenges Highly unstructured environment – in flight High levels of uncertainty

Sarcos financial position

Sarcos financial position As of December 31, 2023; includes all cash, cash equivalents and marketable securities. For continuing operations, monthly and quarterly cash usage will vary; represents average monthly change in cash, cash equivalents and marketable securities. As of December 31, 2023. $39.1 million $1.6 million/mo. Year-End Cash Balance1 Expected Net Monthly Cash Usage per Month for 20242 Shares Outstanding3 25,877,865 shares

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