Future Innovations in Robotics to Anticipate

Yesterday, I shared ten ideas on the crossing paths of augmented humans and humanized robots. If you missed it, here’s the post: https://lnkd.in/gSEx4MNw Over the next few days, I’ll go deeper into each concept, starting with a big one: Synthetic Theory of Mind: Teaching Robots to Get You What will it take for robots to go beyond following commands and actually understand us? The next leap in robotics isn’t more compute. It’s empathy. We need a new kind of intelligence: A Synthetic Theory of Mind Engine is a system that lets machines infer our beliefs, emotions, intentions, and mental states. This isn’t sci-fi anymore. China recently introduced Guanghua No. 1, the world’s first robot explicitly designed with emotional intelligence. It can express joy, anger, and sadness and adapt behavior based on human cues. The vision: emotionally aware care, especially for aging populations. ... and as Scientific American reports, researchers are now building AI models that simulate how people think and feel, essentially teaching machines to reason about our inner world. We’re witnessing the first generation of emotionally intelligent machines. So, what can a Synthetic Theory of Mind Engine do? Imagine a robot that can: ⭐ Detect confusion in your voice and rephrase ⭐ Notice emotional fatigue and pause ⭐ Adapt its language based on what you already know ⭐ Predict what you’re about to need before you say it To do this, it builds a persistent mental model of you. One that evolves with every interaction; making collaboration more intuitive and aligned. In healthcare, education, customer support, and even companionship, the future of robotics isn’t just about capability. It’s about alignment with our goals, our states, and our humanity. We're not just building smarter agents. We’re building partners who can make us feel seen, understood, and supported. 2–3 years: Expect early pilots in eldercare, education, and social robotics 5–7 years: Emotionally aware, intent-sensitive agents in homes, hospitals, and teams If you're working on cognitive robotics, LLM + ToM integration, or human-aligned AI, I’d love to connect and collaborate.

Hello 👋 from the Automate Show in downtown Detroit. I’m excited to share with you what I’m learning. Robotics is undergoing a fundamental transformation, and NVIDIA is at the center of it all. I've been watching how leading manufacturers are deploying NVIDIA's Isaac platform, and the results are staggering: Universal Robotics & Machines UR15 Cobot now generates motion faster with AI. Vention is democratizing machine motion for businesses. KUKA has integrated AI directly into their controllers. But what's truly revolutionary is the approach: 1. Start with a digital twin In simulation, companies can deploy thousands of virtual robots to run experiments safely and efficiently. The majority of robotics innovation is happening in simulation right now, allowing for both single and multi-robot training before real-world deployment. 2. Implement "outside-in" perception Just as humans perceive the world from the inside out, robots need their own sensors. But the game-changer is adding "outside-in" perception - like an air traffic control system for robots. This dual approach is solving industrial automation's biggest challenges. 3. Leverage generative AI Factory operators can now use LLMs to manage operations with simple prompts: "Show me if there was a spill" or "Is the operator following the correct assembly steps?" Pegatron is already implementing this with just a single camera. They're creating an ecosystem where partners can integrate cutting-edge AI into existing systems, helping traditional manufacturers scale up through unprecedented ease of use. The most powerful insight? Just as ChatGPT reached 100 million users in 9 days, robotics adoption is about to experience its own inflection point. The barriers to entry are falling. The technology is becoming accessible even for mid-sized and smaller companies. And the future is being built in simulation before transforming our physical world. Michigan Software Labs Forbes Technology Council Fast Company Executive Board

If an AI can control 1,000 robots to perform 1 million skills in 1 billion different simulations, then it may "just work" in our real world, which is simply another point in the vast space of possible realities. This is the fundamental principle behind why simulation works so effectively for robotics. Real-world teleoperation data scales linearly with human time (< 24 hrs/robot/day). Sim data scales exponentially with compute. There are 3 big trends for simulators in the near future: 1. Massive parallelization on large clusters. Physics equations are "just" matrix math at their core. I hear GPUs are good at matrix math 🔥. One can run 100K copies of simulation on a single GPU. To put this number in perspective: 1 hour of wallclock compute time gives a robot 10 years (!!) of training experience. That's how Neo was able to learn martial arts in a blink of an eye in the Matrix Dojo. 2. Generative graphics pipeline. Traditionally, simulators require a huge amount of manual effort from artists: 3D assets, textures, scene layouts, etc. But every component in the workflow can be automated: text-to-image, text-to-3D mesh, and LLMs that write Universal Scene Description (USD) files as a coding exercise. RoboCasa is one example of a prior work (https://robocasa.ai/). 3. End2end neural net that acts as simulator itself. This is still bluesky research and quite far from replacing a graphics pipeline, but we are seeing some exciting signs-of-life based on video gen models: Sora, Veo2, CogVideoX, Hunyuan (text-to-video); and action-driven world models: GameNGen, Oasis, Genie-2, etc. Genesis does great on (1) for certain tasks, shows good promises on (2), and could become a data generation tool for reaching (3). Its sim2real capabilities for locomotion are good, but there's still a long way to go for contact-rich, dexterous manipulation. It shows a bold vision and is on the right path to providing a virtual cradle for embodied AI. It is open-source and puts a streamlined user journey at the front and center. I had the privilege to know Zhou Xian and play a small part in his project since a year ago. Xian has been crunching code non-stop on Genesis with a very small group of core devs. He often replied to my messages at 3 am. Zhenjia Xu from our GEAR team helped with sim2real experiments in his spare time. Genesis is truly a grassroot effort with an intense focus on quality engineering. Nothing gives me more joy than seeing the simulation ecosystem bloom. Robotics should be a moonshot initiative owned by all of humanity. Congratulations! https://lnkd.in/gF7MSDXK

Sometimes you can feel the trends starting to emerge from the fringes, and I would put humanoid robots in that category right now. 🤖 On Episode 136 of The Artificial Intelligence Show we talked about the recent news and rumors of Apple and Meta now entering the humanoid robot space, joining an already crowded group of tech leaders who are building or investing in robots, including Alphabet, Amazon, NVIDIA, OpenAI and Tesla. Plus, you have emerging robotics companies such as Figure, which is in talks to raise $1.5 billion at a $39.5 billion valuation. During this week's podcast I referenced my AI Timeline notes from March 2024 (Episode 87) regarding the impending "Robotics Explosion (2026 - 2030)." I'm working a full 2nd edition of the AI Timeline for release soon, but here's what I wrote last year about humanoid robotics. These bullets largely seems to still hold true. * Lots of investment going into humanoid robots in 2024 (e.g. OpenAI, Tesla Optimus, Figure, Amazon, Google, NVIDIA, Boston Dynamics, etc.) that are leading to major advancements in the hardware. * Multimodal LLMs are the “brains” embodied in the robots. * In the 2026 - 2030 range we start to see widespread commercial applications (e.g. a humanoid robot stocking retail shelves, or providing limited nursing home care). * Commercial robotics will likely be narrow applications initially (i.e. trained to complete specific, high-value tasks), but more general robots that are capable of quickly developing a diverse range of skills through observation and reinforcement learning will emerge. * There is the potential for general purpose consumer robots in the next decade. These robots will likely be available for purchase or lease. They will start as a luxury for the elite, and then quickly move into the mass market as manufacturing costs rapidly fall due to technological advances and competition. * Tangible impact on blue collar jobs starts to become more clear. I'll drop the episode timestamps in the comment section. https://lnkd.in/dRYjDMjj

Stanford University researchers released a new AI report, partnering with the likes of Accenture, McKinsey & Company, OpenAI, and others, highlighting technical breakthroughs, trends, and market opportunities with large language models (LLMs).  Since the report is 500+ pages!!! (link in comments), sharing a handful of the insights below: 1. Rise of Multimodal AI: We're moving beyond text-only models. AI systems are becoming increasingly adept at handling diverse data types, including images, audio, and video, alongside text. This opens up possibilities for apps in areas like robotics, healthcare, and creative industries. Imagine AI systems that can understand and generate realistic 3D environments or diagnose diseases from medical scans. 2. AI for Scientific Discovery: AI is transforming scientific research. Models like GNoME are accelerating materials discovery, while others are tackling complex challenges in drug development. Expect AI to play a growing role in scientific breakthroughs, leading to new materials and more effective medicines. 3. AI and Robotics Synergy: The combination of AI and robotics is giving rise to a new generation of intelligent robots. Models like PaLM-E are enabling robots to understand and respond to complex commands, learn from their environment, and perform tasks with greater dexterity. Expect to see AI-powered robots playing a larger role in manufacturing, logistics, healthcare, and our homes. 4. AI for Personalized Experiences: AI is enabling hyper-personalization in areas like education, healthcare, and entertainment. Imagine educational platforms that adapt to your learning style, healthcare systems that provide personalized treatment plans, and entertainment experiences that cater to your unique preferences. 5. Democratization of AI: Open-source models (e.g., Llama 3 just released) and platforms like Hugging Face are empowering a wider range of developers and researchers to build and experiment with AI. This democratization of AI will foster greater innovation and lead to a more diverse range of applications.

When we think of robots, we often imagine metallic humanoid machines. But the robots of tomorrow might look very different - soft, flexible, and specialized for specific tasks. Here's why this shift is happening and what it means for the future of robotics: Researchers are increasingly exploring soft, flexible materials to build robots. Why? There are several key advantages: - Safety: Soft robots are inherently safer to interact with humans. - Adaptability: They can squeeze through tight spaces and conform to different environments. - Resilience: Soft robots are less likely to break when dropped or impacted. - Efficiency: In some cases, soft materials allow for simpler designs with fewer parts. Rather than trying to create humanoid robots that can do everything, many researchers are focusing on specialized robots optimized for specific tasks. For example: - "Vine robots" that can grow to explore tight spaces - Jumping robots that can leap over obstacles - Soft, octopus-inspired robots for grasping objects This specialization allows robots to go beyond human capabilities in niche applications. These new types of robots are already finding practical uses: - Search and rescue operations - Minimally invasive medical procedures - Space exploration - Inspecting hazardous environments As we continue to innovate in materials science and robot design, we're likely to see more soft, flexible, and highly specialized robots entering our lives - not as humanoid servants, but as tools optimized for specific tasks. The future of robotics may not look like science fiction predicted, but it promises to be just as transformative. :)

🤖 What if AI doesn’t take jobs, but creates better ones? This week on The Bridgecast, I sat down with Arshad Hisham, Founder and CEO of inGen Dynamics Inc., to talk about the real future of AI and robotics in business, and why IT leaders need to start building smart policies now. We covered a ton of ground, including: 🧠 AI Adoption Strategy: Organizations are rushing to deploy AI without proper oversight, creating risks and inconsistent results. Effective AI governance isn't just about compliance; it's about achieving a competitive advantage. Companies need clear ethics guidelines, data quality standards, cross-functional oversight committees, and comprehensive employee training. The winners will be those who establish governance frameworks first, enabling safe and scalable AI adoption as technology evolves.  🦾 Human-Centric Robotics: Workplace robotics will succeed or fail based on human acceptance, rather than technical specifications. Workers will embrace robotic colleagues when they understand limitations, feel valued, and see clear benefits. The key is designing for transparency, intuitive interaction, and clear role boundaries. Invest equally in change management and technology. 📉 Beyond the Hype: The AI market is flooded with solutions promising transformation but delivering incremental gains. True value assessment requires measurable outcomes: cost reduction, improved customer satisfaction, faster go-to-market and better decision-making. Establish baselines, track long-term performance, and prioritize specific problems over broad transformation. Focus on use cases with clear ROI, existing data infrastructure, and stakeholder buy-in.  👤 The Next Wave: Humanoid robots are moving from fiction to reality within 18 months. Initial deployments will target logistics, manufacturing, and customer service and handling repetitive tasks while maintaining flexibility. Unlike fixed automation, these robots work alongside humans, requiring new safety protocols and management approaches. Start identifying use cases, assessing workforce capabilities, and developing integration strategies now. Early preparation in stakeholder education and human-robot collaboration protocols will create significant competitive advantages. Arshad cuts through the noise with clarity and conviction. If you’re navigating AI, robotics, or digital transformation, this episode is a must-listen. 👉 Tune in and tell me: How human is your tech strategy? #AIAdoption #Robotics #DigitalTransformation #TheBridgecast