Amazon Develops Robotic Systems with Advanced Tactile Sensing

Amazon Develops Robotic Systems with Advanced Tactile Sensing

The introduction of advanced tactile sensing technology will enable warehouse machinery to perform delicate manipulation tasks that previously required human precision. The development of these new robotic systems marks a major advancement in warehouse automation, as robots are increasingly used to handle complex and fragile items.  

Robotic systems equipped with sensing systems to measure pressure, texture, and resistance on an object will allow the robot to adapt its grasping ability as it interacts with the object. This added capability will help address one of the main challenges of robotic systems today: how to handle fragile and/or irregularly shaped items without damaging them while also scaling up. 

Bringing the Sense of Touch to Robotics  

Traditional warehouse robots have been very effective at repetitive tasks that involve numerous identical items, such as moving boxes or sorting standardized packages. At the same time, traditional warehouse robots have not been very effective at tasks that require fine motor skills, such as grasping soft, fragile, or uniquely shaped objects.  

Through tactile sensing, Amazon has enabled its robots to interact with objects in ways similar to those of humans. For example, sensors integrated into a robot’s gripper provide continuous feedback on the force applied to an object, allowing the software to make micro-adjustments during object handling.  

Amazon’s development has enabled robots to emulate one of the most sophisticated features of the human hand: the ability to manipulate and handle items without relying solely on visual feedback.  

Enhancing Precision in Warehouse Operations  

Introducing touch or feel sensation, in addition to the various levels of accuracy already achieved in warehouses, has resulted in not only robots being able to perform tasks that were previously only possible with human intervention but also textiles and electronic devices.  

With this new capability, not only do robots have new and expanded capabilities to automate additional functions, reducing the reliance on human labor for performing intricate functions that require careful handling, but also, the robots are able to consistently accomplish these same functions with the same level of quality, as there will be no fatigue incurred in performing these functions by the robots.  

Amazon continues to use these improvements to enhance efficiency while maintaining its high level of product safety during order fulfillment.  

AI Integration for Smarter Manipulation  

Systems that combine tactile sensing with artificial intelligence can learn from experience and adapt their behavior over time. Machine learning algorithms use sensor data processing to help robots identify and react to different objects and situations.  

A robot learns to change its grip by studying three factors: the weight, shape, and material of the things it handles. The system improves its operational capacity through ongoing learning while also gaining the ability to handle different types of products.  

AI integration enables tactile sensing technology to function as a predictive system, allowing robots to forecast object behavior during handling.  

Scaling Automation Across Fulfillment Centers  

Amazon has a significant logistics network; therefore, the scalability of intelligent robotics across this network is a major focus for the company. easing picking and packing accuracy.  

Automation of more complex tasks will enable Amazon to achieve greater operational efficiency while providing the flexibility needed to handle the increasing variation in product inventory. As e-commerce continues its rapid expansion, the variety of product catalogs will grow.  

The speed at which this computerization can be integrated into Amazon’s daily operations will depend on the number of units deployed.  

Reducing Damage and Returns  

Enhancing robotic manipulation delivers tangible benefits by reducing product damage during fulfillment. Damage to products reduces financial resources, negatively affects customer satisfaction, and increases return rates.  

By leveraging tactile sensors to apply the appropriate amount of force, robots can lift, handle, or hold items securely without damaging them. This increased accuracy will help minimize damage during product movement, resulting in a more dependable delivery experience.  

Amazon has put a concerted effort into reducing errors while pursuing an overarching goal of improving its entire logistics pipeline from the warehouse through to the end-use customer.  

Human-Robot Collaboration  

While technology has improved, people are still necessary for warehouse work. Robots with tactile capabilities will complement humans, not take over.  

In a collaborative workplace, the robot will handle the repeatable and/or demanding parts of the job, allowing humans to focus on more difficult decision-making and supervisory tasks. This can increase an organization’s efficiency and reduce workers’ physical stress.  

Amazon is investigating the continued integration of robots into the workflow to increase both productivity and worker safety.  

Challenges in Tactile Robotics Development  

Multiple challenges must be overcome in the development of reliable tactile sensing systems; they must perform well over long periods in industrial settings and detect very small pressure changes under repeated stress.  

Moving tactile information into an AI system requires sophisticated algorithms capable of processing large amounts of instantaneous data. This project focuses on developing performance standards that enable the sensors to operate correctly across multiple device types and work environments. 

Amazon is dedicating research and development resources to developing these technologies while enhancing its ability to scale effectively.  

Conclusion: A New Level of Robotic Precision  

Amazon has made significant advancements in its automation technology by developing robotic systems that employ advanced tactile sensing. They have developed a solution to one of the largest problems that typical robotics face when handling fragile items with precision.  

When these systems are fully implemented in warehouse operations, they will revolutionize operations while also establishing new standards for operational efficiency, precision product delivery, and reliability at all points along the logistics supply chain.

Source: Amazon News 

Tesla Patent Improves Safety in Human-Robot Interaction

Tesla Patent Improves Safety in Human-Robot Interaction

Recently, Tesla submitted a technical document that will create a more secure environment for humans to work alongside robots by enabling robots to anticipate people’s potential actions and respond instantly. As part of their overall robotics effort, which includes developing robots with advanced predictive capabilities for use in industries beyond the automotive sector, such as manufacturing, Tesla aims to implement more advanced predictive technology to reduce accidents and enhance human-robot interaction in industrial and consumer environments.  

Predictive Robotics for Safer Interaction  

The purpose of this patent is to give robots the ability to detect, interpret, and anticipate human movements, enabling them to proactively respond to their surroundings rather than merely react to changes. Most current robotic systems use pre-programmed motions or sensor data to respond to environmental changes. Tesla’s approach will use artificial intelligence-based models to enable robots to learn from human movement. These predictive algorithms will enable robots to anticipate a person’s movement trajectory, speed, and direction and adjust their behaviour accordingly.  

Implementing real-time sensor data with AI behaviour behavior of humans. For example, if a person enters the robot’s path or gestures toward an object, the robot can adjust its movement patterns in real time to maintain a safe distance. This represents a substantial leap forward from traditional safety protocols, which rely heavily on emergency stop mechanisms or limited interaction areas due to past technological limitations, by providing a much more fluid, human-centric model for robot use.  

Implications for Industrial and Consumer Robotics  

Tesla’s innovations have the potential to significantly impact robotics across both industrial automation and consumer robotics. For example, in an industrial environment, predictive robots will be able to work alongside humans and perform most heavy lifting, assembly work, and precision work while minimising the risk of worker injury. As for consumers, the use of this technology will also improve the performance of robots that assist around the house and in elderly care, making them safer to operate near people of all ages and abilities.  

The patent makes it clear that Tesla is committed to developing robotic systems that are functional yet intuitive, safe, and able to predict human behaviour so they can interact with humans in as natural a way as possible and provide assistance without constant interference or monitoring. This could lead to quicker, easier adoption of robotic systems across a wider range of safety of human interaction with robots.  

AI-Driven Motion Prediction  

Artificial intelligence-enabled motion prediction is the basis of Tesla’s technology. Machine learning models are trained using large datasets of people interacting with the world to predict motion. There are several methods used in the machine learning analysis process to understand how people currently move (or have moved) in relation to a particular task and to apply software predictions to facilitate those motions.  

As the system learns from each individual, it can personalise its motion prediction for that person, thereby improving its predictive power. Through predictive motion analysis, robotic arms, automated vehicles, and/or any other autonomous technologies can be controlled more precisely than ever before. For example, if a collaborative assembly line manufacturer anticipates the motion of a human operator at the assembly line and the robotic arm is able to predict that operator’s motion within a millisecond, both parties can safely perform their tasks without endangering themselves or others, resulting in a dramatic increase in the overall efficiency of the process.  

Enhancing Collaborative Workspaces  

Historically, safety issues have impeded human-robot collaboration, thereby restricting the closeness and mobility of robots working within shared environments. Tesla’s patent addresses these issues by allowing robots to adjust their trajectories reactively to human movements.  

An example may be illustrated using a robot on an automotive assembly line. A robot would be able to sense that an employee is reaching for an item and alter its location so as not to impede the employee’s action while completing its own action in an efficient manner. Whereas previously safety protocols tended to be rigid, unchanging systems of operation, the predictive capabilities of robots enable adaptable, context-sensitive interactions. This type of application may lead to the establishment of new standards for the safe use of robots in the workplace.  

Potential Applications Beyond Tesla  

Tesla’s short-term focus is on using advanced robotics technology to improve its manufacturing processes, but the long-term implications of this technology are larger. Motion-predictive AI can also be utilised for manual movements to ensure safe and effective performance.  

By developing this foundational technology for predictive interaction, Tesla is helping create a future in which AI-enabled systems can work alongside people in a safe, easy, and efficient manner. Additionally, this patent presents another opportunity for Tesla to establish itself as a thought leader in the design of human-robot interfaces, which could ultimately shape industry standards and best practices for collaborative robots.  

Challenges in Implementation  

The challenges of implementing predictive robots remain despite their high expectations. Getting good results from predictive robots requires the following: 

  • To make predictions that are accurate, all the necessary sensor data must be collected (from multiple locations) and analysed using a high-speed algorithm.  
  • Unexpected behaviours, variations in human behaviour, and environmental variability create situations that are unsafe to manage for predictive robotics.  
  • Determining the level of integration required for the consumer-orientated will require careful calibration, testing, and validation of all components, as well as the use of new and improved AI models across different environments.  

Tesla has established a set of standards that can help with the above. However, deploying predictive robots into the real world will require building AI models and ensuring they are robust across diverse environments.  

Looking Ahead: The Future of Human-Robot Interaction  

Tesla’s patent for predictive robotics represents a major advancement in safe, intelligent collaboration between humans and robots. Robots will be able to predict and proactively respond to human movements, resulting in more effective working relationships at home and in the workplace, with either party being the same or different than before.  

As Artificial Intelligence continues to evolve, this technology will change the way we interact with robots by allowing them to work in closer proximity, with greater efficiency, flexibility, and safety. Tesla has taken bold steps to underscore the growing need for predictive intelligence in robotic systems by establishing a new standard for innovation within its field of expertise.  

A New Era in Collaborative Robotics  

Combining artificial intelligence with real-time sensors and predictive motion allows Tesla to create robots that can intelligently interpret and react to human actions. The published patent will demonstrate a future in which individuals and robots can live together safely and productively, as industries shift from manufacturing to home automation.  

Tesla’s innovation represents an important milestone toward realising the potential of collaborative robotics while helping mitigate risk; it also creates a model for the future of intelligent systems that are effective and built around human needs.

Source: https://patents.google.com/ 

Amazon Scales Warehouse Robotics to Improve US Delivery Speed

Amazon Scales Warehouse Robotics to Improve US Delivery Speed

Amazon is spending much more to implement warehouse robots in the United States to gain a competitive advantage by achieving shorter delivery times and building a more resilient supply chain. The firm would increase its business activities by implementing over half a million mobile robotic units to be used alongside human employees to enhance sorting, picking, and packing processes. These advances show that the company is committed to automating its operations as it develops more logistics solutions. 

Robotic Integration Across Fulfillment Centers  

Amazon has grown to have numerous distribution centers in the United States and beyond, with warehouse robotics programmes that started as small pilot projects and are now full-scale and operational. The system uses autonomous mobile robots (AMRs) to transfer inventory in large facilities, minimising physical load on workers, reducing unnecessary movement, and improving operational performance.  

The robots use high-level AI-based routing technology to monitor order volumes, patterns in warehouse traffic, and changes in facility layout and to adjust paths in real time. Optimising the movement will also enable Amazon to achieve a 25% increase in processing speed across its capacity centres, which are already operating at full capacity. The robotics system not only accelerates order processing but also helps human workers focus on crucial tasks, such as quality control, complex packaging, and inventory management, thereby boosting the entire organisation’s productivity.  

Robotic systems also help human workers by enabling them to focus on important business activities, such as evaluating product quality, performing complex packaging operations, and providing customer support. 

Enhancing Delivery Speeds in the US  

Fast delivery services now serve as the most important competitive advantage for online shopping businesses. Amazon expands its robotic systems to meet customer demand for same-day and next-day delivery. The company will achieve faster order processing and delivery operations through its plan to increase warehouse automation.  

Amazon operates robotics technology in both its standard urban fulfilment centres and delivery system, a logistics process that transports packages from the final storage location to customers’ doors, enabling rapid movement from storage to delivery vehicles.  

AI-Driven Warehouse Management  

The central component of Amazon’s robotics operations uses an artificial intelligence-based system that manages its operations. The machine learning algorithms track inventory status, forecast future demand changes, and control robot movements throughout the day.   

During peak holiday periods, AI systems dynamically reallocate robots to zones experiencing higher demand. The system also tracks energy usage, robot movement patterns, and maintenance timetables, letting each facility establish its own autonomous management system.   

Amazon combines robotics with predictive analytics to achieve faster delivery times, which result in reduced operational costs, fewer mistakes, and safer work environments for its employees.  

Sustainability and Efficiency Gains  

Amazon uses its automated systems to achieve environmental sustainability goals that extend beyond its current operations. The company achieves cost savings through its robotic systems, which operate at peak efficiency by reducing the walking required of warehouse staff and lowering energy use. The company claims that its robot systems have already improved operational efficiency while decreasing energy consumption throughout its facilities. 

The new generation of robots uses lightweight yet durable materials and energy-efficient motors to reduce environmental impact while achieving high operational performance.  

Workforce Collaboration and Training  

Amazon states that robots serve as tools that enhance human workers’ abilities rather than replace them. The company trains its associates to operate robots using mobile devices and sensors to achieve robotic unit operation. 

The company has established training programmes to develop employees’ skills so they can handle every aspect of robotic system operation and maintenance. This partnership creates a workplace sector that promotes safety and efficiency and trains employees to manage more automated tasks.  

Implications for US Logistics and E-Commerce  

Amazon’s warehouse robotics expansion has multiple impacts on the entire US logistics industry. The competitors face pressure to implement identical systems because the technology delivers faster processing and more reliable performance, which drives e-commerce and third-party logistics to adopt automated systems.   

Automated fulfilment meets while also allowing them to develop their own robotic systems. Industry analysts predict that these investments will change how customers expect delivery times, service dependability, and service performance.  

Challenges and Considerations  

The robotics industry faces technical and operational hurdles that prevent its benefits from being fully realised. The safe operation of warehouse systems requires three components: precise facility mapping, ongoing system oversight, and flexible artificial intelligence systems.  

The organisation needs to maintain its numerous units through two essential requirements: strong infrastructure and highly skilled personnel. Amazon works on system improvements to increase production while maintaining operational dependability.  

Three main elements need assessment to create automation systems for facility operations: workplace safety requirements, labour relations, and regulatory compliance. The company establishes safety standards governing robot operations in areas requiring continuous operational oversight.    

Looking Ahead  

Amazon plans to integrate robotic solutions into its current and future distribution centres by 2026. In the future, there will be improved artificial intelligence systems, faster mobile robots, and more efficient systems that can tie delivery operations to drones and self-driving cars.   

The application of warehouse robots in Amazon’s operations enables the development of an innovative e-commerce logistics system, thereby reducing delivery times across the United States. The project demonstrates that AI-driven automation systems can be efficient in terms of operational efficiency, safety standards, and customer satisfaction.

Sources: Operations 

NVIDIA Cosmos Model Lets Robots Simulate Physics Before Taking Action

NVIDIA Cosmos Model Lets Robots Simulate Physics Before Taking Action

The next wave of AI-powered robots, such as humanoids and self-driving vehicles, needs high-quality physics-based training data. If their datasets lack diversity and realism, these systems may not train well and could struggle with unexpected situations. Gathering large real-world datasets is costly, time-consuming, and often constrained by pragmatic constraints.  

NVIDIA Cosmos addresses this problem by accelerating the development of world-class models (WCMs). Cosmos WFM enables faster synthetic data generation and provides a foundation for training specialized physical AI models. In this post, we’ll look at the newest Cosmos WFM’s, their main features to advance physical AI, and how you can use them.  

Cosmos World Foundation Model Updates 

NVIDIA Cosmos world-based models are improving rapidly, making it easier for users to access high-quality synthetic data and accelerated physical AI development. After just one year, recent updates ensure users benefit from faster, more flexible, and realistic data generation processes.  

  • Cosmos Transfer 2.5: Delivers Faster, More Scalable Data Augmentation. The process of creating varied data by altering existing data from simulations and 3D spatial inputs provides greater variety within environments, lighting, and scene setups.  
  • Cosmos predict 2.5: improves generation of rare scenarios for sequences up to 30 seconds, attaining up to 10 times higher accuracy when post-trained on custom or sector-specific data. It also supports multi-view outputs, custom camera setups, and various policy outputs, such as action and simulation.  
  • Cosmos Reason 2: offers advanced physical AI reasoning with better spatio-temporal understanding (the ability to interpret spatial and temporal relationships) and more precise timestamps. It adds: Object Detection, 2D and 3D point localization (Finding locations in flat and 3D spaces), Bounding box coordinates (Boxes that identify the positions of objects), Reasoning explanations, and labels. It now supports Long Context Improved inputs up to 256,000 tokens (a token is a unit of text, like a word or character).  

Cosmos Transfer Creates Photorealistic Videos That Adhere To Real-World Physics 

Cosmos Transfer creates detailed word sense from structural inputs, ensuring accurate spatial alignment and composition.  

Cosmos Transfer uses the controlnet architecture to retain pre-trained knowledge, resulting in structured, consistent outputs. It uses spatial-temporal control maps to match artificial and real-world scenes, giving detailed control over:  

  • scene layout  
  • object placement and movement  
  • eye points  
  • lidar scans  
  • trajectories  
  • HD maps  
  • 3D bounding boxes  

Ground Truth Annotations: High Fidelity References for Exact Alignment  

Output: photorealistic video sequences with controlled layout, object placement, and motion.  

Key Capabilities 

  • Generate scalable, photorealistic, synthetic data that aligns with real-world physics, allowing users to train more reliable AI and robotics models.s.  
  • Control object interactions and scene composition with structured multi-modal input, giving users precise customization and more relevant training data for their specific use cases.s.  

Using Cosmos Transfer for Controllable Synthetic Data 

With Generative AI APIs and SDKs, NVIDIA Omniverse enables users to create accurate 3D simulations for real-world training and testing. These experiments provide ground-truth video inputs for Cosmos Transfer, improving photorealism and diversifying datasets to fit user-specific conditions, ensuring your AI agents are better prepared for real-world deployment.  

This process speeds up the generation of high-quality data, enabling users’ AI agents to learn more efficiently from simulation to real-world applications, reducing development cycles and boosting performance in practical tasks.  

As a result, Cosmos Transfer helps users train robots and AI for diverse environments and conditions by adding realistic lighting and textures. This improves model robustness and makes it easier for users to transition from simulation to real-world use, especially for robotics platforms like GR00T-N1.1.  

Cosmos Predict for Generating Future World States 

Cosmos Predict WFM enables users to generate predictive video sequences for future scenarios using varied inputs such as text, video, and image sequences. Its smooth, accurate video generation helps users test and refine how AI systems might respond in real-world situations.  

The following key capabilities were developed in our Cosmos Credit functions. It creates realistic video scenes directly from text prompts.  

  • Predicts subsequent events in a video by generating missing frames or continuing motion  
  • Generates multiple frames (intermediate images) between a starting and ending image to create a smooth, complete video sequence.  

Cosmos Predict WFM is a solid starting point for training world models, AI systems that simulate environments used in robotics and self-driving vehicles. After initial training, you can teach these models to generate actions rather than videos for policy modelling and AI decision-making, or adapt them for visual language tasks to build custom AI perception models (systems that understand visual information).  

Cosmos Reason: Designed to Perceive Reason and Respond Intelligently 

Cosmos Reason is a flexible AI model designed to understand motion, how objects interact, and relationships over time and space. It uses chain-of-thought reasoning to examine visual input, predict outcomes from prompts, and choose the best actions. Unlike text-only models, it bases its reasoning on actual physics and provides clear natural-language context for its answers.  

Video: observations along with a text question for instruction (prompt).  

Output: a text response created using long-term chain of thought reasoning (step-by-step analysis over time).  

  • Understands how objects move, interact, and change  
  • Predicts and selects optimal next actions based on observations.  
  • Continuously refines its decision-making ability over time.  
  • It is designed for further training to help build perception AI and embodied AI models.  

Let’s Get Started 

Explore our Cosmos Cookbook for user-focused step-by-step guidance, technical tips, and examples that help you streamline and accelerate your Cosmos WFM projects.s.  

Access open Cosmos models and datasets on Hugging Face and GitHub to quickly enhance your projects or evaluate models, making experimentation and implementation faster and easier for users.  

Join our Cosmos Discord community now—connect with peers, get real-time support, and share unique experiences. Become part of our vibrant network today!  

Be inspired: Watch the GTC Keynote from NVIDIA founder and CEO Jensen Huang. Then explore Cosmos sessions and kick-start your own breakthrough projects with insights at https://www.nvidia.com/gtc/sessions/physical-AI-days/. Start your journey today! 

Source: Scale Synthetic Data and Physical AI Reasoning with NVIDIA Cosmos World Foundation Models 

Boston Dynamics Atlas Specs Reveal Robot With Expanded Multi-Axis Motion

Boston Dynamics Atlas Specs Reveal Robot With Expanded Multi-Axis Motion

Key Details 

  • Boston Dynamics launches immediate production of the humanoid Atlas robot.  
  • In 2026, the rover will be deployed at Hyundai and Google DeepMind, with additional customers anticipated the following year.  
  • Atlas will be trained with new AI-based models to handle many industrial tasks, starting with the automotive industry.  

Boston Dynamics, a leader in mobile robotics, introduced the product version of its new Atlas robot at the Consumer Electronics Show in Las Vegas. The fully electric humanoid was shown during Hyundai’s CES Media Day, which also included a live demo. It is the latest Atlas prototype and a lively dance performance by the well-known Spot Robots.  

Production of the new Atlas robots will begin immediately at the company’s Boston headquarters. All units for 2026 are already spoken for, with fleets set to ship to Hyundai’s Robotics Metaplant Applications Center (RMAC) and Google DeepMind soon. More customers will be added in early 2027.  

For more than 30 years, Boston Dynamics has been building some of the world’s most advanced robots, said Robert Playter, the company’s CEO. This is the best tool we have ever built. Atlas is going to change the way the industry works and make its mark. It will be the initial step toward a long-term goal we have dreamed about since we were children. Useful robots can walk into our homes and help make our lives safer, more productive, and more fulfilling.  

Atlas is an enterprise-grade humanoid robot capable of handling many tasks. From moving materials to filling orders, it learns from new tasks quickly, adapts to evolving environments, lifts heavy loads, and works independently with little supervision. It keeps working at a steady, reliable pace and does not need to stop when its battery runs low. Instead, it will find a charging station, swap its own batteries, and return to work.  

The robot connects easily to manufacturing systems such as MES (Manufacturing Execution System) and WMS (Warehouse Management System), as well as other industrial software, via Boston Dynamics’ Orbit software. Once one Atlas robot learns a new task, that skill can be shared instantly with the whole fleet.  

Atlas operates autonomously via remote or with a tablet. It has 56 degrees of freedom. A 2.3-meter reach lifts up to 50 kg, is water-resistant, and works from -22°C to 40°C.   

Safety features include human detection and fenceless guarding (protecting people without physical barriers), with workflow integration via barcode or RFID (radio-frequency identification).  

“Our new Atlas is the most production-friendly robot we’ve got,” said Zach Jackowski, GM of Atlas at Boston Dynamics. This generation of Atlas uses fewer unique parts, and every component is made to fit with automotive supply chains. With support from Hyundai Motor Group, we will reach the highest reliability and economies of scale in the industry.  

Along with launching Atlas at CES, Boston Dynamics announced a new partnership with Google DeepMind. They plan to use Google DeepMind’s advanced base models to improve Atlas’s cognitive abilities. The company also shared that Hyundai Mobis will supply Atlas actuators. Both organizations will work together to build an efficient supply chain and speed up actuator development and production.  

Hyundai Motor Group holds a majority stake in Boston Dynamics. The company is preparing to deploy tens of thousands of Boston Dynamics robots in its manufacturing facilities. Hyundai also announced a $26 billion investment in its U.S. operations. This includes plans for a new robotics facility with an annual capacity of 30,000 robots.  

To learn more, visit www.bostondynamics.com.  

About Boston Dynamics. 

Boston Dynamics leads the world in developing and deploying highly mobile robots that handle tough industrial and safety challenges. Our robots have advanced mobility, dexterity, and intelligence, enabling automation in hard-to-reach or unsafe environments such as factories, power plants, construction sites, warehouses, and distribution centers.  
 
Our portfolio includes three robots:  

  • Spot, a four-legged robot for industrial inspections and public safety  
  • Stretch, a robot that moves boxes for logistics and retail  
  • Atlas, our electric humanoid platform, is now in development.

Source: Boston Dynamics Unveils New Atlas Robot to Revolutionize Industry