📝 Blogs & Articles — Awesome Robotics
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138 posts, articles, and resources from across the field.
Innovations in navigation techniques are enhancing robotic autonomy.
4 postsSoft robots are being developed for delicate tasks like fruit harvesting.
2 postsPodcasts explore various aspects of collaborative and advanced robotic systems.
7 postsResearch focuses on simulation and design methodologies for effective robotic systems.
4 postsWhy it matters — The MIGHTY system's ability to generate efficient path plans could significantly enhance the operational efficiency of robots in critical scenarios like disaster recovery and logistics.
Why it matters — The Cross-Link system's decentralized approach to collective robotic behavior mimics natural materials, providing insights into swarm robotics and material science applications.
Why it matters — This soft robot gripper's use of fiber-optic sensors for assessing fruit ripeness demonstrates a significant advancement in soft robotics and agricultural automation.
Why it matters — The development of microrobots that operate without external control could revolutionize applications in targeted drug delivery and minimally invasive surgeries.
Why it matters — Understanding the complex dynamics of insect flight can inform the design of more stable and efficient flapping-wing robots, enhancing aerial robotics capabilities.
Why it matters — The integration of sonar and AI for navigation in drones mimics biological echolocation, offering new strategies for autonomous navigation in complex environments.
Why it matters — This AI system's ability to manage traffic among warehouse robots can optimize logistics operations, improving efficiency and reducing wait times.
Why it matters — The concept of resource-sharing among robots enhances resilience, a crucial aspect for developing robust robotic systems that can adapt to failures.
Why it matters — The multi-armed robot's design for agricultural tasks highlights the potential for improving efficiency in farming practices through advanced robotic assistance.
Why it matters — Using simulation for hospital automation addresses the critical shortage of healthcare professionals, showcasing robotics' potential to alleviate systemic pressures in healthcare.
Why it matters — Jaiveer Singh's focus on infrastructure development for robots emphasizes the importance of foundational technologies in advancing robotic capabilities.
Why it matters — The discussion on collaborative haptic systems sheds light on how touch interaction can enhance human-robot collaboration, a key area in robotics research.
Why it matters — The exploration of Vision-Language-Action models highlights the integration of visual and linguistic understanding in robotics, crucial for advanced interaction capabilities.
Why it matters — The use of robotic blacksmiths for manufacturing complex metal parts could revolutionize production processes in high-tech industries like aerospace and defense.
Why it matters — The focus on robotic sensing and manipulation for surgical applications emphasizes the growing role of robotics in precision medicine and automated healthcare solutions.
Why it matters — The development of autonomous delivery robots highlights advancements in navigation and operational independence, crucial for last-mile delivery solutions.
Why it matters — Using AI to generate new designs for robotic manipulators could lead to innovative solutions in robotics, enhancing functionality and adaptability.
Why it matters — The application of rugged robots in dangerous missions demonstrates the potential for robotics to enhance safety and efficiency in high-risk environments.
Why it matters — The approach to teaching skills across different robots addresses a significant challenge in robotics, facilitating easier upgrades and interoperability.
Why it matters — The discussion on autonomous navigation for drones is vital for improving aerial robotics' capabilities, especially in dynamic environments.
Why it matters — Insights from insect navigation can inform the development of more efficient robotic navigation systems, bridging biology and robotics.
Why it matters — The GRASP planner's ability to optimize long-horizon planning in learned dynamics represents a breakthrough in making complex robotic tasks more feasible.
Why it matters — Exploring how a robot's physical form can influence its capabilities underscores the importance of design in robotics, impacting both functionality and efficiency.
Why it matters — The GRASP planner's innovations in gradient-based planning for world models could significantly enhance the performance of robotic systems in complex environments.
Why it matters — Rich Walker's work on dexterous robotic hands is crucial for advancing manipulation capabilities in robotics, which is essential for both research and practical applications.
Why it matters — Simona Aracri's focus on oceanographic robots highlights the role of robotics in environmental monitoring, which is increasingly important for understanding and protecting marine ecosystems.
Why it matters — RoboTwin's approach to robot training through demonstration by factory workers simplifies the skill acquisition process, potentially accelerating the integration of robots into industrial settings.
Claire chatted to Vikas Enti from Reframe Systems about using robotics and automation to build climate-resilient, high-performance homes. Vikas Enti is the co-founder and CEO of Reframe Systems, a physical AI company rethinking how homes are built through automation and localized fabrication. He previously spent more than a decade at Amazon Robotics, where he helped […]
Claire chatted to Krystal Mattich from Brain Corp about trustworthy autonomous robots in public spaces. Krystal Mattich leads global data governance, system security, and privacy compliance for Brain Corp: the world’s leading autonomy platform for commercial robotics. As Senior Director of Security, Privacy, and Risk, she is the architect of the privacy-first infrastructure that powers […]
Why it matters — The introduction of graphene-based sensors for robotic touch enhances the tactile capabilities of robots, which is essential for delicate manipulation tasks in various applications.
Why it matters — The focus on edge-first LLMs for autonomous vehicles and robotics highlights the need for efficient processing in real-time applications, which is critical for performance.
Why it matters — The evolution of robotic assistance in surgery illustrates the transformative impact of robotics on healthcare, highlighting the integration of AI and computer vision.
Why it matters — Accelerating bird's-eye-view pooling on NVIDIA GPUs represents a significant leap in perception technology for autonomous vehicles and robotics, enhancing spatial awareness.
Why it matters — This article reiterates the importance of safety systems in physical AI, highlighting the need for comprehensive approaches to ensure safe robotic operations in everyday settings.
Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can...
Why it matters — This article emphasizes the need for understanding the real world in order to develop effective action models for robots, which is crucial for their successful deployment in various applications.
Why it matters — The table tennis robot's performance against top players highlights the potential for robotics to replicate complex human skills, which can inform advancements in robotic learning and dexterity.
Ace rotates its paddle as it prepares to return the ball back to its human opponent, Yamato Kawamata, during a match in December 2025. Credit: Sony AI. In an article published today in Nature, Sony AI introduce Ace, the first robot to beat elite human players in competitive physical sport. Although AI systems have shown […]
Why it matters — The enhancements in spatial reasoning and multi-view understanding in Gemini Robotics ER 1.6 are crucial for improving the capabilities of autonomous robots in real-world tasks.
Why it matters — Integrating Physical AI capabilities into existing applications with NVIDIA Omniverse Libraries can streamline the design and validation processes for robotic systems.
Why it matters — The use of generative AI to enhance a wireless vision system represents a novel approach to improving robotic perception, particularly in challenging environments with obstructions.
Why it matters — By enhancing contact-rich manipulation and locomotion capabilities in industrial robotics, this research paves the way for more versatile and effective robotic systems in manufacturing.
Why it matters — Scaling synthetic data and physical AI reasoning with NVIDIA Cosmos World Foundation Models is essential for training next-generation AI-driven robots in a realistic manner.
Claire chatted to Alan Winfield from the University of the West of England about developing new standards for ethics and transparency in robotics. Alan Winfield is Professor of Robot Ethics at the University of the West of England (UWE), Visiting Professor at the University of York, and Associate Fellow of the Cambridge Centre for the […]
Why it matters — ICRA 2026 serves as a platform for the latest advancements in robotics, fostering collaboration and innovation among leading researchers in the field.
Image credit: RoboCup Federation. RoboCup 2026 kicked off today in Incheon, South Korea, with the league competitions running until 5 July. It’s an exciting time for RoboCup, as there have been some updates to the leagues and competition format. Most prominently, the soccer leagues will have a primary focus on humanoid robots. In a series […]
Image credit: HIMS / Nature Synthesis. In a paper published in Nature Synthesis, researchers led by Professor Timothy Noël of the University of Amsterdam’s Van ’t Hoff Institute for Molecular Sciences present an advance in autonomous laboratory systems for synthesis optimisation. A versatile, modular design and the option for “human-in-the-loop” analytics, RoboChem Flex caters to […]
The recently launched Robotics Café is a weekly online seminar series to bring together researchers, students and industry practitioners working in the field of autonomous robotics. One of the key aims of the initiative is to provide a dedicated platform for students to present and disseminate their work, enabling broader visibility and impact across academia […]
Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning. Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare, and much more. Along the way, they have published influential research, built systems with real-world impact, mentored their peers, and shaped the BAIR community for the better. Now they are headed everywhere ideas travel: to faculty and postdoctoral positions, to industry research labs, and to startups of their own founding — and several are still exploring what comes next and would love to hear from you. Please join us in celebrating the achievements of these wonderful graduates. We are proud of everything they have accomplished at Berkeley, and we can’t wait to see what they do next! Thank you to our friends at the Stanford AI Lab for this idea! Baifeng Shi Email: baifeng_shi@berkeley.edu Website: https://bfshi.github.io/ Advisor(s): Trevor Darrell Research Blurb: I work on building generalist vision and robotic models. What's next: Member of Technical Staff at Physical Intelligence Charlie Snell Email: csnell22@berkeley.edu Website: https://sea-snell.github.io Advisor(s): Dan Klein Research Blurb: My work aims to understand when and how the different LLM scaling paradigms can be traded off and interchanged. In particular, test-time scaling treats each prompt independently, drawing long chains of inferences and then forgetting them entirely between prompts. This differs critically from pretraining, which instead learns a compressed representation from a large dataset. I believe bridging the gap between these methods of scaling computation, presents a key open challenge in the field: how can we develop methods which turn the inferences drawn at test-time back into learned representations that the model can hold onto across interactions. Devin Guillory Email: dguillory@berkeley.edu Website: https://devinguillory.com Advisor(s): Trevor Darrell Research Blurb: Accounting for data shifts in computer vision models What's next: Building collaborative AI systems, looking for conspirators. Eve Fleisig Email: efleisig@berkeley.edu Website: https://efleisig.com Advisor(s): Dan Klein Research Blurb: I design language models to work reliably and fairly for the broad range of real LLM users. First, my research leverages disagreement among user preferences as signal, in order to train and evaluate LLMs for entire populations of users. Second, I work on designing rigorous evaluations to extricate challenging LLM harms that diverse users face. Finally, I work on core technical failures of LLMs, like miscalibrated confidence, to reduce downstream risks when models are deployed to users with different needs. Combined, these interventions facilitate building LLMs that minimize societal harms, and maximize benefits to a wider range of real-world users. What's next: Postdoctoral fellow at Princeton CITP Grace Luo Email: graceluo@berkeley.edu Website: https://graceluo.net Advisor(s): Trevor Darrell Research Blurb: My research is on interpreting and controlling generative models. For example, I've worked on re-purposing image generators for computer vision tasks, and meta-modeling language activations for better LLM probing and steering. What's next: Research scientist in industry Hanlin Zhu Email: hanlinzhu@berkeley.edu Website: https://hanlinzhu.com/ Advisor(s): Stuart Russell, Jiantao Jiao Research Blurb: My research centers on understanding and improving the reasoning capabilities of large language models (LLMs). What's next: Member of Technical Staff at OpenAI Haozhi Qi Email: hqi@berkeley.edu Website: https://haozhi.io/ Advisor(s): Jitendra Malik, Yi Ma Research Blurb: Dexterous Manipulation and Robot Learning What's next: Research scientist at Amazon; Faculty at University of Chicago J.D. Zamfirescu-Pereira Email: zamfi@berkeley.edu Website: https://zamfi.net Advisor(s): Bjoern Hartmann Research Blurb: My research focuses on effective human-AI co-design. I study the boundaries of language interfaces as a medium for interacting with AI, creating systems that blend language-focused interactions with structured user interfaces that draw on different levels of abstraction. I focus on language-oriented technologies, like LLMs and text-to-image models, that are powerful mediators of design processes. These technologies enable humans to describe their desires at almost any level of abstraction, from high-level goals vaguely specified (“I’d like a game to help my kid learn to read”) to low-level corrections of undesired outputs (“Don’t say ‘I know because I’ve tasted it’ when about a recipe substitution's taste”). What's next: Assistant Professor, Computer Science, UCLA Jiachen Lian Email: jiachenlian@berkeley.edu Website: https://jlian2.github.io Advisor(s): Gopala Anumanchipalli Research Blurb: My research focuses on human-centered AI across speech, healthcare, and systems. Looking for: Look for AI talents to join our startup Josh Kang Email: minwoo_kang@berkeley.edu Website: https://joshuaminwookang.github.io/ Advisor(s): John Canny Research Blurb: I study language modeling and related topics in NLP; specific interests are human user simulation and building conversational, collaborative AI agents. What's next: AI Scientist at Mistral AI Junhao (Bear) Xiong Email: junhao_xiong@berkeley.edu Website: https://www.linkedin.com/in/junhao-bear-xiong Advisor(s): Jennifer Listgarten, Yun Song Research Blurb: Junhao (Bear) Xiong is a PhD candidate at UC Berkeley, advised by Jennifer Listgarten and Yun S. Song. His work focuses on machine learning methods for biology, with an emphasis on generative modeling for proteins. Previously, he studied Applied Math and Computer Science at Johns Hopkins. Looking for: Research scientist Kaylo Littlejohn Email: kaylo_littlejohn@berkeley.edu Website: https://kaylolittlejohn.com Advisor(s): Gopala Anumanchipalli Research Blurb: My research is focused on speech modeling and natural language processing. I co-led the development of multimodal AI tools to accurately translate brain activity into text, audible personalized speech, and a high-fidelity "digital talking avatar" (Nature 2023, Nature Neuroscience 2025). I am also tech lead for voice modeling at Roblox. Looking for: Research Scientist / Engineer Kent Chang Email: kentkchang@berkeley.edu Website: https://kentkc.org Advisor(s): David Bamman Research Blurb: I work on NLP and multimodal machine learning, with a focus on evaluating large language models and building multimodal systems for understanding dialogue, narrative, and social interaction. My research includes benchmarks for LLM memorization, multimodal datasets sourced from feature films and television, and studies of model behavior. I'm interested in bridging computational methods with questions from the humanities and social sciences about whose voices get represented in AI systems, and about AI's broader impact. My work has appeared at EMNLP and ACL, among others. Looking for: (teaching) faculty, Research Scientist, ML/AI SWE Kevin Black Email: kvablack@berkeley.edu Website: https://kevin.black Advisor(s): Sergey Levine Research Blurb: I work on large-scale robot learning: including imitation learning, reinforcement learning, generative modeling, real-time control, and whatever else it takes to make robots work in the real world! What's next: Research Scientist of Physical Intelligence Kunhe Yang Email: kunheyang@berkeley.edu Website: https://www.kunheyang.com/ Advisor(s): Nika Haghtalab Research Blurb: My research focuses on the theoretical foundations of designing and evaluating AI algorithms in environments shaped by human incentives and AI agency. My work spans human-centric policy learning, incentive-aware evaluation, and multi-agent collaboration and information transmission, drawing on tools from machine learning theory and computational economics. What's next: Postdoc Research at Stanford Lisa Dunlap Email: lisabdunlap@berkeley.edu Website: https://lisabdunlap.com Advisor(s): Joseph Gonzalez, Trevor Darrell Research Blurb: Auditing generative models. What's next: Research Engineer at Anthropic Long (Tony) Lian Email: longlian@berkeley.edu Website: https://tonylian.com/ Advisor(s): Trevor Darrell, Adam Yala Research Blurb: My research primarily focuses on developing real-time multi-modal multi-agent systems and parallel reasoning systems through end-to-end RL. What's next: Member of Technical Staff at Thinking Machines Lab Maulik Bhatt Email: maulikbhatt@berkeley.edu Website: https://maulikb.com Advisor(s): Negar Mehr Research Blurb: My research develops autonomous robots that can safely coordinate with humans and other robots in shared environments. I build scalable algorithms grounded in game theory and diffusion models that let agents reason about the intent and behavior of others around them. My work spans real-time multi-agent trajectory planning and imitation learning in the presence of multi-modality. I've validated these methods on hardware platforms ranging from quadrotors to manipulators, with the goal of making multi-agent coordination robust, interpretable, and deployable in the real world. What's next: Joining Toyota Woven's end-to-end autonomous driving team. Michael Psenka Email: psenka@berkeley.edu Website: https://www.michaelpsenka.io/ Advisor(s): Aditi Krishnapriyan Research Blurb: Work in various domains (reinforcement learning, world models, AI+bio/chem), generally working on longer-horizon and out-of-distribution problems in planning and interpolation (e.g. robot manipulation from start state to goal, molecular dynamics of proteins between ground states). My thesis took a variational approach (think calculus of variations) directly from deep generative models of the environment, framing path-finding as minimizing a functional induced by the learned model itself (its score, its critic, or its dynamics). Through my research I've gained insight on how to properly handle dynamics in deep learning systems, and I plan to continue developing systems that are dynamic and adaptive. What's next: Lead Research Scientist at Baseten Nathan Lichtlé Email: nathan.lichtle@gmail.com Website: https://nathanlichtle.com Advisor(s): Alexandre M. Bayen Research Blurb: RL for autonomous driving. What's next: Chief Scientist & Co-founder at Yumi Health Neerja Thakkar Email: nthakkar@berkeley.edu Website: https://neerja.me/ Advisor(s): Jitendra Malik Research Blurb: My research focuses on scaling predictive world models to handle the complexity of in-the-wild motion. Using autoregressive and diffusion frameworks, I develop better representations for real-world prediction and propose methods to efficiently adapt these models to new domains. Looking for: Research scientist Nikita Mehandru Email: nmehandru@berkeley.edu Website: https://n-mehandru.github.io/ Advisor(s): Ahmed Alaa and David Bamman Research Blurb: My research develops and applies machine learning methods for clinical reasoning and disease progression modeling using unstructured text and time series data from electronic health records. In collaboration with physicians at UCSF, I bridge method development and clinical validation with the intention to build reliable, interpretable AI systems in medicine. Looking for: Research Scientist Niklas Lauffer Email: nlauffer@berkeley.edu Website: https://niklaslauffer.github.io/ Advisor(s): Stuart Russell and Sanjit Seshia Research Blurb: Niklas's research is focused on AI safety and reinforcement learning, particularly in the area of multi-agent interaction and LM agents. He's worked on enabling adversarial learning in cooperative and mixed-motive settings, solving issues of covariate shift in training LM agents on long-horizon tasks, as well as evaluating safety risks posed by LM agents in multi-agent settings. What's next: Research Scientist at Google Deepmind Qiyang Li Email: qcli@berkeley.edu Website: https://colinqiyangli.github.io/ Advisor(s): Sergey Levine Research Blurb: Recent progress in robotic manipulation policy learning has been largely driven by (1) the increasing availability of large-scale prior datasets and (2) the success of action chunking, where the policy predicts a short sequence of future actions rather than a single one. However, most action chunking policies are trained via supervised imitation learning, because efficient online self-improvement with reinforcement learning (RL) remains challenging—limiting real-world applicability. My PhD research studied how we could leverage prior data to optimize action-chunking policies with RL, combining empirical results with theoretical insights. Looking for: Post-doc/research scientist for RL in robotics and LLMs! Sampada Deglurkar Email: sampada_deglurkar@berkeley.edu Website: https://sdeglurkar.github.io/ Advisor(s): Prof Claire Tomlin Research Blurb: My research is in providing safety assurances for AI-enabled autonomous systems, ranging from robots to autonomous vehicles to aviation systems. For this, I have worked with uncertainty quantification for machine learning models, decision-making under uncertainty algorithms, and tools for producing probabilistic guarantees on system operation. Looking for: Research scientist, Research engineer Vinamra Benara Email: vbenara@berkeley.edu Website: https://cs.berkeley.edu/~vbenara Advisor(s): Ion Stoica Research Blurb: My research focuses on LLM post-training, including data curation, RLHF, RLVR with VLMs, evaluations, reasoning, agentic workflows, and interpretability. I also have strong expertise in systems infrastructure for distributed computing. Looking for: Research scientist / Research Engineer Vongani Maluleke Email: vongani_maluleke@berkeley.edu Website: https://people.eecs.berkeley.edu/~vongani_maluleke/ Advisor(s): Jitendra Malik and Angjoo Kanazawa Research Blurb: Vongani Maluleke is a PhD candidate at UC Berkeley (BAIR, advised by Jitendra Malik and Angjoo Kanazawa), where she led the development of MAGNet, a unified multi-agent motion generation framework that supports a wide range of motion generation tasks without retraining or architectural changes, outperforming task-specialized state-of-the-art baselines. She is currently extending this work by deploying it on a Unitree G1 humanoid to make it embody social intelligence. Before her PhD, she was a Senior AI Consultant at Deloitte, awarded Exceptional Performer two consecutive years, leading AI system development across media, telecommunications, retail, and financial services. Looking for: Research scientist Wei-Jer Chang Email: weijer_chang@berkeley.edu Website: https://weijer-chang.github.io/ Advisor(s): Masayoshi Tomizuka Research Blurb: My research focuses on developing safe and intelligent autonomous systems for complex, human-centered environments. I work at the intersection of machine learning, generative models, and reinforcement learning, with applications in autonomy. My work addresses challenges in multi-agent interaction, interactive human behavior, and long-tail safety-critical scenarios at scale. Looking for: Research Scientist, Applied Scientist, Roboticist Xiuyu Li Email: xiuyu@berkeley.edu Website: https://xiuyuli.com/ Advisor(s): Kurt Keutzer Research Blurb: My research focuses on developing scalable and self-improving large language model agents, with emphasis on coding agents for complex, long-horizon tasks. This direction builds on my work in parallel reasoning, and on broader expertise in making generative models more efficient in training and inference across language and vision. What's next: Member of Technical Staff at xAI Yichen Xie Email: yichenxie0928@gmail.com Website: https://yichen928.github.io/ Advisor(s): Masayoshi Tomizuka Research Blurb: My research focuses on building multimodal foundation models and world models that understand and interact with complex physical environments. I aim to develop unified representations across modalities, enabling AI systems to reason over space, time, and dynamics toward general-purpose embodied intelligence. What's next: Research Scientist at Luma AI Yigit Efe Erginbas Email: erginbas@berkeley.edu Website: https://www.linkedin.com/in/erginbas/ Advisor(s): Kannan Ramchandran, Thomas A. Courtade Research Blurb: My PhD research spans two threads: online learning in large-scale markets, and interpretability of large machine learning models. In the first, I work on sequential decision-making with applications to recommendation, pricing, and assortment selection. My focus is on designing algorithms with provable guarantees for welfare maximization, revenue maximization, and stability. In the second, I develop scalable attribution methods that exploit the sparse, low-degree structure of real-world interactions, using tools from signal processing and information theory. More recently, I have been exploring principled ways to evaluate the faithfulness of model self-explanations. What's next: Researcher at Hudson River Trading's AI Labs (HAIL) Yiheng Li Email: yhli@berkeley.edu Website: https://Yihengli.com Advisor(s): Masayoshi Tomizuka Research Blurb: I am working on vision world modeling, with prior experience in diffusion model's efficiency as well as in autonomous driving. What's next: Research Scientist at Waymo Zhe Fu Email: zhefu@berkeley.edu Website: https://fu-zhe.com/ Advisor(s): Alexandre Bayen Research Blurb: My research focuses on physics-informed learning and control for mixed-autonomy systems, with applications in transportation. I design physics-informed neural networks to learn solutions of nonlinear partial differential equations, enabling accurate and data-efficient prediction of traffic dynamics. Building on these models, I develop both model-based and learning-based control strategies that coordinate automated vehicles to improve system-level performance. My work bridges machine learning, control, and real-world deployment, and has been validated in large-scale field experiments. More broadly, I aim to advance trustworthy, interpretable AI for decision-making in complex, real-world systems. What's next: I will be an Energy Fellow at Stanford after graduation. Also looking for Faculty, or research scientist positions in AI, control, and autonomy.
NVIDIA RTX technologies are deeply integrated into Unreal Engine 5 through the NVIDIA RTX Branch of Unreal Engine and the NVIDIA DLSS Unreal Engine plugin. This...
Deborah Lupton / Pop Chips / Licenced by CC-BY 4.0. Henrik I Christensen, Professor of Computer Science & Engineering at University of California San Diego, has recently released a global robotics technology roadmap. This position paper focuses on Asia, Europe, and America and outlines the current state-of-the-art in robotics, and highlights the main opportunities. The […]
New MIT work advances the growing field of ionotronics, in which data are transferred through ions, potentially providing a bridge between electronics and biological tissue.
We’re excited to launch our new series, where we’ll be speaking with leading researchers to explore the breakthroughs driving AI and the reality of the future promises – to give you an inside perspective on the headlines. Our first interviewee is Ross King, who created the first robot scientist back in 2009. He spoke to […]
Google DeepMind researches AI's harmful manipulation risks across areas like finance and health, leading to new safety measures.
RoboCup is an international competition that promotes and advances robotics and AI through the challenges presented by its various leagues. We got the chance to sit down with Professor Manuela Veloso, one of RoboCup’s founders, to find out more about how it all started, how the community has grown over the years, and the vision […]
Industrial and medical systems are rapidly increasing the use of high-performance AI to improve worker productivity, human-machine interaction, and downtime...
As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids...
Open source AI has shown how quickly developers can innovate when models, data and tools are shared. Robotics has the same opportunity, but advancements in physical AI development can still be gated by costly and fragmented resources, from large datasets and robot foundation models to simulation, compute and validation tools. NVIDIA and Hugging Face are […]