Google DeepMind has introduced Gemini Robotics, a new system that combines Gemini's large language model capabilities with robotic control. This allows robots to understand and execute complex instructions given in natural language, moving beyond pre-programmed behaviors. Gemini provides high-level understanding and planning, while a smaller, specialized model handles low-level control in real-time. The system is designed to be adaptable across various robot types and environments, learning new skills more efficiently and generalizing its knowledge. Initial testing shows improved performance in complex tasks, opening up possibilities for more sophisticated and helpful robots in diverse settings.
Pivot Robotics, a YC W24 startup building robots for warehouse unloading, is hiring Robotics Software Engineers. They're looking for experienced engineers proficient in C++ and ROS to develop and improve the perception, planning, and control systems for their robots. The role involves working on real-world robotic systems tackling challenging problems in a fast-paced startup environment.
HN commenters discuss the Pivot Robotics job posting, mostly focusing on the compensation offered. Several find the $160k-$200k salary range low for senior-level robotics software engineers, especially given the Bay Area location and YC backing. Some argue the equity range (0.1%-0.4%) is also below market rate for a startup at this stage. Others suggest the provided range might be for more junior roles, given the requirement for only 2+ years of experience, and point out that actual offers could be higher. A few express general interest in the company and its mission of automating grocery picking. The low compensation is seen as a potential red flag by many, while others attribute it to the current market conditions and suggest negotiating.
The US is significantly behind China in adopting and scaling robotics, particularly in industrial automation. While American companies focus on software and AI, China is rapidly deploying robots across various sectors, driving productivity and reshaping its economy. This difference stems from varying government support, investment strategies, and cultural attitudes toward automation. China's centralized planning and subsidies encourage robotic implementation, while the US lacks a cohesive national strategy and faces resistance from concerns about job displacement. This robotic disparity could lead to a substantial economic and geopolitical shift, leaving the US at a competitive disadvantage in the coming decades.
Hacker News users discuss the potential impact of robotics on the labor economy, sparked by the SemiAnalysis article. Several commenters express skepticism about the article's optimistic predictions regarding rapid robotic adoption, citing challenges like high upfront costs, complex integration processes, and the need for specialized skills to operate and maintain robots. Others point out the historical precedent of technological advancements creating new jobs rather than simply eliminating existing ones. Some users highlight the importance of focusing on retraining and education to prepare the workforce for the changing job market. A few discuss the potential societal benefits of automation, such as increased productivity and reduced workplace injuries, while acknowledging the need to address potential job displacement through policies like universal basic income. Overall, the comments present a balanced view of the potential benefits and challenges of widespread robotic adoption.
Firefly Aerospace's Blue Ghost lander successfully touched down on the lunar surface, making them the first commercial company to achieve a soft landing on the Moon. The mission, part of NASA's Commercial Lunar Payload Services (CLPS) initiative, deployed several payloads for scientific research and technology demonstrations before exceeding its planned mission duration on the surface. Although communication was eventually lost, the landing itself marks a significant milestone for commercial lunar exploration.
Hacker News users discussed Firefly's lunar landing, expressing both excitement and skepticism. Several questioned whether "landing" was the appropriate term, given the lander ultimately tipped over after engine shutdown. Commenters debated the significance of a soft vs. hard landing, with some arguing that any controlled descent to the surface constitutes a landing, while others emphasized the importance of a stable upright position for mission objectives. The discussion also touched upon the challenges of lunar landings, the role of commercial space companies, and comparisons to other lunar missions. Some users highlighted Firefly's quick recovery from a previous launch failure, praising their resilience and rapid iteration. Others pointed out the complexities of defining "commercial" in the context of space exploration, noting government involvement in Firefly's lunar mission. Overall, the sentiment was one of cautious optimism, acknowledging the technical achievement while awaiting further details and future missions.
Firefly Aerospace's Blue Ghost lunar lander successfully touched down on the moon, marking a significant milestone for the company and the burgeoning commercial lunar exploration industry. The robotic spacecraft, carrying NASA and commercial payloads, landed in the Mare Crisium basin after a delayed descent. This successful mission makes Firefly the first American company to soft-land on the moon since the Apollo era and the fourth private company overall to achieve this feat. While details of the mission's success are still being confirmed, the landing signals a new era of lunar exploration and establishes Firefly as a key player in the field.
HN commenters discuss the Firefly "Blue Ghost" moon landing, expressing excitement tinged with caution. Some celebrate the achievement as a win for private spaceflight and a testament to perseverance after Firefly's previous launch failure. Several commenters question the "proprietary data" payload and speculate about its nature, with some suggesting it relates to lunar resource prospecting. Others highlight the significance of increased lunar activity by both government and private entities, anticipating a future of diverse lunar missions. A few express concern over the potential for increased space debris and advocate for responsible lunar exploration. The landing's role in Project Artemis is also mentioned, emphasizing the expanding landscape of lunar exploration partnerships.
NASA's video covers the planned lunar landing of Firefly Aerospace's Blue Ghost Mission 1 lander. This mission marks Firefly's inaugural lunar landing and will deliver several NASA payloads to the Moon's surface to gather crucial scientific data as part of the agency's Commercial Lunar Payload Services (CLPS) initiative. The broadcast details the mission's objectives, including deploying payloads that will study the lunar environment and test technologies for future missions. It also highlights Firefly's role in expanding commercial access to the Moon.
HN commenters express excitement about Firefly's upcoming moon landing, viewing it as a significant step for private space exploration and a positive development for the US space industry. Some discuss the technical challenges, like the complexities of lunar landing and the need for a successful landing to validate Firefly's technology. Others highlight the mission's scientific payloads and potential future implications, including resource utilization and lunar infrastructure development. A few commenters also mention the importance of competition in the space sector and the role of smaller companies like Firefly in driving innovation. There's some discussion of the mission's cost-effectiveness compared to larger government-led programs.
Figure AI has introduced Helix, a vision-language-action (VLA) model designed to control general-purpose humanoid robots. Helix learns from multi-modal data, including videos of humans performing tasks, and can be instructed using natural language. This allows users to give robots complex commands, like "make a heart shape out of ketchup," which Helix interprets and translates into the specific motor actions the robot needs to execute. Figure claims Helix demonstrates improved generalization and robustness compared to previous methods, enabling the robot to perform a wider variety of tasks in diverse environments with minimal fine-tuning. This development represents a significant step toward creating commercially viable, general-purpose humanoid robots capable of learning and adapting to new tasks in the real world.
HN commenters express skepticism about the practicality and generalizability of Helix, questioning the limited real-world testing environments and the reliance on simulated data. Some highlight the discrepancy between the impressive video demonstrations and the actual capabilities, pointing out potential editing and cherry-picking. Concerns about hardware limitations and the significant gap between simulated and real-world robotics are also raised. While acknowledging the research's potential, many doubt the feasibility of achieving truly general-purpose humanoid control in the near future, citing the complexity of real-world environments and the limitations of current AI and robotics technology. Several commenters also note the lack of open-sourcing, making independent verification and further development difficult.
Robocode is a programming game where you code robot tanks in Java or .NET to battle against each other in a real-time arena. Robots are programmed with artificial intelligence to strategize, move, target, and fire upon opponents. The platform provides a complete development environment with a custom robot editor, compiler, debugger, and battle simulator. Robocode is designed to be educational and entertaining, allowing programmers of all skill levels to improve their coding abilities while enjoying competitive robot combat. It's free and open-source, offering a simple API and a wealth of documentation to help get started.
HN users fondly recall Robocode as a fun and educational tool for learning Java, programming concepts, and even AI basics. Several commenters share nostalgic stories of playing it in school or using it for programming competitions. Some lament its age and lack of modern features, suggesting updates like better graphics or web integration could revitalize it. Others highlight the continuing relevance of its core mechanics and the existence of active communities still engaging with Robocode. The educational value is consistently praised, with many suggesting its potential for teaching children programming in an engaging way. There's also discussion of alternative robot combat simulators and the challenges of updating older Java codebases.
This GitHub repository showcases a method for visualizing the "thinking" process of a large language model (LLM) called R1. By animating the chain of thought prompting, the visualization reveals how R1 breaks down complex reasoning tasks into smaller, more manageable steps. This allows for a more intuitive understanding of the LLM's internal decision-making process, making it easier to identify potential errors or biases and offering insights into how these models arrive at their conclusions. The project aims to improve the transparency and interpretability of LLMs by providing a visual representation of their reasoning pathways.
Hacker News users discuss the potential of the "Frames of Mind" project to offer insights into how LLMs reason. Some express skepticism, questioning whether the visualizations truly represent the model's internal processes or are merely appealing animations. Others are more optimistic, viewing the project as a valuable tool for understanding and debugging LLM behavior, particularly highlighting the ability to see where the model might "get stuck" in its reasoning. Several commenters note the limitations, acknowledging that the visualizations are based on attention mechanisms, which may not fully capture the complex workings of LLMs. There's also interest in applying similar visualization techniques to other models and exploring alternative methods for interpreting LLM thought processes. The discussion touches on the potential for these visualizations to aid in aligning LLMs with human values and improving their reliability.
A hobbyist detailed the construction of a homemade polarimetric synthetic aperture radar (PolSAR) mounted on a drone. Using readily available components like a software-defined radio (SDR), GPS module, and custom-designed antennas, they built a system capable of capturing radar data and processing it into PolSAR imagery. The project demonstrates the increasing accessibility of complex radar technologies, highlighting the potential for low-cost environmental monitoring and other applications. The build involved significant challenges in antenna design, data synchronization, and motion compensation, which were addressed through iterative prototyping and custom software development. The resulting system provides a unique and affordable platform for experimenting with PolSAR technology.
Hacker News users generally expressed admiration for the project's complexity and the author's ingenuity in building a polarimetric synthetic aperture radar (PolSAR) system on a drone. Several commenters questioned the legality of operating such a system without proper licensing, particularly in the US. Some discussed the potential applications of the technology, including agriculture, archaeology, and disaster relief. There was also a technical discussion about the challenges of processing PolSAR data and the limitations of the system due to the drone's platform. A few commenters shared links to similar projects or resources related to SAR technology. One commenter, claiming experience in the field, emphasized the significant processing power required for true PolSAR imaging, suggesting the project may be closer to a basic SAR implementation.
Reinforcement learning (RL) is a machine learning paradigm where an agent learns to interact with an environment by taking actions and receiving rewards. The goal is to maximize cumulative reward over time. This overview paper categorizes RL algorithms based on key aspects like value-based vs. policy-based approaches, model-based vs. model-free learning, and on-policy vs. off-policy learning. It discusses fundamental concepts such as the Markov Decision Process (MDP) framework, exploration-exploitation dilemmas, and various solution methods including dynamic programming, Monte Carlo methods, and temporal difference learning. The paper also highlights advanced topics like deep reinforcement learning, multi-agent RL, and inverse reinforcement learning, along with their applications across diverse fields like robotics, game playing, and resource management. Finally, it identifies open challenges and future directions in RL research, including improving sample efficiency, robustness, and generalization.
HN users discuss various aspects of Reinforcement Learning (RL). Some express skepticism about its real-world applicability outside of games and simulations, citing issues with reward function design, sample efficiency, and sim-to-real transfer. Others counter with examples of successful RL deployments in robotics, recommendation systems, and resource management, while acknowledging the challenges. A recurring theme is the complexity of RL compared to supervised learning, and the need for careful consideration of the problem domain before applying RL. Several commenters highlight the importance of understanding the underlying theory and limitations of different RL algorithms. Finally, some discuss the potential of combining RL with other techniques, such as imitation learning and model-based approaches, to overcome some of its current limitations.
This project showcases WiFi-controlled RC cars built using ESP32 microcontrollers. The cars utilize readily available components like a generic RC car chassis, an ESP32 development board, and a motor driver. The provided code establishes a web server on the ESP32, allowing control through a simple web interface accessible from any device on the same network. The project aims for simplicity and ease of replication, offering a straightforward way to experiment with building your own connected RC car.
Several Hacker News commenters express enthusiasm for the project, praising its simplicity and the clear documentation. Some discuss potential improvements, like adding features such as obstacle avoidance or autonomous driving using a camera. Others share their own experiences with similar projects, mentioning alternative chassis options or different microcontrollers. A few users suggest using a more robust communication protocol than UDP, highlighting potential issues with range and reliability. The overall sentiment is positive, with many commenters appreciating the project's educational value and potential for fun.
Waymo, Alphabet's self-driving unit, plans to expand its autonomous vehicle testing to over ten new US cities. Focusing on trucking and delivery services, Waymo will leverage its existing experience in Phoenix and San Francisco to gather data and refine its technology in diverse environments. This expansion aims to bolster the development and eventual commercial deployment of their autonomous driving systems for both passenger and freight transport.
HN commenters are generally skeptical of Waymo's expansion plans. Several point out that Waymo's current operational areas are geographically limited and relatively simple to navigate compared to more complex urban environments. Some question the viability of truly driverless technology in the near future, citing the ongoing need for human intervention and the difficulty of handling unpredictable situations. Others express concern about the safety implications of widespread autonomous vehicle deployment, particularly in densely populated areas. There's also discussion of the regulatory hurdles and public acceptance challenges that Waymo and other autonomous vehicle companies face. Finally, some commenters suggest Waymo's announcement is primarily a PR move designed to attract investment and maintain public interest.
This paper introduces a novel method for 3D scene reconstruction from images captured in adverse weather conditions like fog, rain, and snow. The approach leverages Gaussian splatting, a recent technique for representing scenes as collections of small, oriented Gaussian ellipsoids. By adapting the Gaussian splatting framework to incorporate weather effects, specifically by modeling attenuation and scattering, the method is able to reconstruct accurate 3D scenes even from degraded input images. The authors demonstrate superior performance compared to existing methods on both synthetic and real-world datasets, showing robust reconstructions in challenging visibility conditions. This improved robustness is attributed to the inherent smoothness of the Gaussian splatting representation and its ability to effectively handle noisy and incomplete data.
Hacker News users discussed the robustness of the Gaussian Splatting method for 3D scene reconstruction presented in the linked paper, particularly its effectiveness in challenging weather like fog and snow. Some commenters questioned the practical applicability due to computational cost and the potential need for specialized hardware. Others highlighted the impressive visual results and the potential for applications in autonomous driving and robotics. The reliance on LiDAR data was also discussed, with some noting its limitations in certain adverse weather conditions, potentially hindering the proposed method's overall robustness. A few commenters pointed out the novelty of the approach and its potential to improve upon existing methods that struggle with poor visibility. There was also brief mention of the challenges of accurately modelling dynamic weather phenomena in these reconstructions.
Jannik Grothusen built a cleaning robot prototype in just four days using GPT-4 to generate code. He prompted GPT-4 with high-level instructions like "grab the sponge," and the model generated the necessary robotic arm control code. The robot, built with off-the-shelf components including a Raspberry Pi and a camera, successfully performed basic cleaning tasks like wiping a whiteboard. This project demonstrates the potential of large language models like GPT-4 to simplify and accelerate robotics development by abstracting away complex low-level programming.
Hacker News users discussed the practicality and potential of a GPT-4 powered cleaning robot. Several commenters were skeptical of the robot's actual capabilities, questioning the feasibility of complex task planning and execution based on the limited information provided. Some highlighted the difficulty of reliable object recognition and manipulation, particularly in unstructured environments like a home. Others pointed out the potential safety concerns of an autonomous robot interacting with a variety of household objects and chemicals. A few commenters expressed excitement about the possibilities, but overall the sentiment was one of cautious interest tempered by a dose of realism. The discussion also touched on the hype surrounding AI and the tendency to overestimate current capabilities.
The video demonstrates a functioning bicycle built with omni-directional ball wheels instead of traditional wheels. The creator showcases the build process, highlighting the custom-made frame and the challenges of incorporating the spherical wheels. The bike's unique mechanics allow for sideways and diagonal movement, though it requires considerable effort and balance to maneuver, resulting in a slow and somewhat wobbly ride. Despite the unconventional design, the creator successfully demonstrates the bike's ability to move in various directions, proving the concept's feasibility.
Commenters on Hacker News largely praised the engineering and ingenuity of the omni-directional bike. Several expressed fascination with the complex mechanics and control systems required to make it work. Some discussed the potential applications of such a drive system, suggesting uses in robotics or other vehicles. A few questioned the practicality of the design for everyday use, citing potential issues with efficiency, terrain handling, and the learning curve required to ride it. There was also some discussion about the similarities and differences between this design and other omni-directional vehicle concepts. One commenter even offered a mathematical analysis of the kinematics involved.
Artist David Bowen's "Tele-present Wind" installation physically translates real-time wind data from a remote location to the movements of a robotic arm holding a flag. The arm's joints are mapped to the wind speed and direction captured by an anemometer, recreating the flag's flutter as if it were directly experiencing the distant wind. This creates a tangible, kinetic representation of a remote weather phenomenon, bridging the gap between distant locations through technology and art.
Hacker News users discussed the artistic merit and technical implementation of David Bowen's "Tele-Present Wind" project. Some praised the elegant simplicity of the concept and its effective conveyance of remote environmental conditions. Others questioned the artistic value, finding it more of an interesting technical demo than a compelling piece of art. Several commenters delved into the technical specifics, discussing the choice of motors, potential improvements to the system's responsiveness, and the challenges of accurately representing wind force and direction. The use of real-time data and the potential for experiencing distant environments resonated with many, while some debated the meaning and implications of digitally mediated natural experiences. A few users also mentioned similar projects they had seen or worked on, highlighting the growing interest in combining technology and nature in artistic endeavors.
Intrinsic, a Y Combinator-backed (W23) robotics software company making industrial robots easier to use, is hiring. They're looking for software engineers with experience in areas like robotics, simulation, and web development to join their team and contribute to building a platform that simplifies robot programming and deployment. Specifically, they aim to make industrial robots more accessible to a wider range of users and businesses. Interested candidates are encouraged to apply through their website.
The Hacker News comments on the Intrinsic (YC W23) hiring announcement are few and primarily focused on speculation about the company's direction. Several commenters express interest in Intrinsic's work with robotics and AI, but question the practicality and current state of the technology. One commenter questions the focus on industrial robotics given the existing competition, suggesting more potential in consumer robotics. Another speculates about potential applications like robot chefs or home assistants, while acknowledging the significant technical hurdles. Overall, the comments express cautious optimism mixed with skepticism, reflecting uncertainty about Intrinsic's specific goals and chances of success.
This paper explores the feasibility of using celestial navigation as a backup or primary navigation system for drones. Researchers developed an algorithm that identifies stars in daytime images captured by a drone-mounted camera, using a star catalog and sun position information. By matching observed star positions with known celestial coordinates, the algorithm estimates the drone's attitude. Experimental results using real-world flight data demonstrated the system's ability to determine attitude with reasonable accuracy, suggesting potential for celestial navigation as a reliable, independent navigation solution for drones, particularly in GPS-denied environments.
HN users discussed the practicality and novelty of the drone celestial navigation system described in the linked paper. Some questioned its robustness against cloud cover and the computational requirements for image processing on a drone. Others highlighted the potential for backup navigation in GPS-denied environments, particularly for military applications. Several commenters debated the actual novelty, pointing to existing star trackers and sextants used in maritime navigation, suggesting the drone implementation is more of an adaptation than a groundbreaking invention. The feasibility of achieving the claimed accuracy with the relatively small aperture of a drone-mounted camera was also a point of contention. Finally, there was discussion about alternative solutions like inertial navigation systems and the limitations of celestial navigation in certain environments, such as urban canyons.
O1 isn't aiming to be another chatbot. Instead of focusing on general conversation, it's designed as a skill-based agent optimized for executing specific tasks. It leverages a unique architecture that chains together small, specialized modules, allowing for complex actions by combining simpler operations. This modular approach, while potentially limiting in free-flowing conversation, enables O1 to be highly effective within its defined skill set, offering a more practical and potentially scalable alternative to large language models for targeted applications. Its value lies in reliable execution, not witty banter.
Hacker News users discussed the implications of O1's unique approach, which focuses on tools and APIs rather than chat. Several commenters appreciated this focus, arguing it allows for more complex and specialized tasks than traditional chatbots, while also mitigating the risks of hallucinations and biases. Some expressed skepticism about the long-term viability of this approach, wondering if the complexity would limit adoption. Others questioned whether the lack of a chat interface would hinder its usability for less technical users. The conversation also touched on the potential for O1 to be used as a building block for more conversational AI systems in the future. A few commenters drew comparisons to Wolfram Alpha and other tool-based interfaces. The overall sentiment seemed to be cautious optimism, with many interested in seeing how O1 evolves.
Summary of Comments ( 207 )
https://news.ycombinator.com/item?id=43344082
HN commenters express cautious optimism about Gemini's robotics advancements. Several highlight the impressive nature of the multimodal training, enabling robots to learn from diverse data sources like YouTube videos. Some question the real-world applicability, pointing to the highly controlled lab environments and the gap between demonstrated tasks and complex, unstructured real-world scenarios. Others raise concerns about safety and the potential for misuse of such technology. A recurring theme is the difficulty of bridging the "sim-to-real" gap, with skepticism about whether these advancements will translate to robust and reliable performance in practical applications. A few commenters mention the limited information provided and the lack of open-sourcing, hindering a thorough evaluation of Gemini's capabilities.
The Hacker News post titled "Gemini Robotics brings AI into the physical world" has generated a moderate discussion with a handful of comments focusing on various aspects of the announcement. No single comment stands out as overwhelmingly compelling, but several offer interesting perspectives.
Several comments express skepticism or caution regarding the claims made in the original blog post. One user points out the discrepancy between the impressive video demonstrations and the often less impressive reality of deployed robotic systems, suggesting that the real-world performance of these robots might not match the curated presentations. This sentiment is echoed by another commenter who highlights the "reality gap" often encountered in robotics, where simulated environments don't fully capture the complexity and unpredictability of the physical world. They suggest a wait-and-see approach to evaluate how these robots perform in real-world scenarios.
Another line of discussion revolves around the practical applications and implications of this technology. One comment questions the economic viability of such robots, wondering if the cost of development and deployment would outweigh the potential benefits in specific use cases. This comment also touches upon the potential for job displacement, a common concern with advancements in automation.
There's also a brief exchange about the nature of the AI being used. One user asks for clarification on whether the robots are truly using Gemini or a simpler model, reflecting the general interest in understanding the underlying technology powering these demonstrations.
Finally, some comments simply express general interest in the technology, acknowledging the potential of AI-powered robotics while remaining cautiously optimistic about its future impact. Overall, the comments reflect a mix of excitement and skepticism, with a focus on the practical challenges and real-world implications of bringing these advancements out of the lab and into everyday life.