@Nikobar

Ranjay Krishna argues that language may be an unnecessary intermediary between perception and action in robotics. Humans do not translate every physical interaction into words before reacting. Catching a ball, pulling a hand away from something hot or moving through a room happens through a direct connection between perception and movement. He believes robotics models should work the same way, moving directly from visual and sensor input to action rather than relying on an LLM in the middle. submitted by /u/Responsible-Grass452 [link] [Kommentare]

I'm considering which certificates are worth the time and money to support my own ML software firm and strengthen my credentials. There is a sea of suggestions online coming from a range of sources that say you don't need a university degree to do this and just need to do "these" courses. What courses do you guys suggest? My current list below is where I'm at presently: Cloud MLOps & Architecture Baseline Professional Machine Learning Engineer by Google: https://cloud.google.com/learn/certification/machine-learning-engineer Microsoft Certified Azure AI Engineer Associate: https://aiskillsnavigator.microsoft.com/credentials/cert-42345e89c4ff32c631414873b457485bf392224af38ac852604946f2655e5782 AWS Certified Machine Learning Specialty: https://aws.amazon.com/certification/certified-machine-learning-specialty/ Deep Technical Competency Certificates IBM AI Engineering Professional Certificate: https://www.credly.com/org/ibm/badge/ibm-ai-engineering-professional-certificate Deep Learning AI Machine Learning or Generative AI Specialisations by Andrew Ng: https://www.deeplearning.ai/specializations/machine-learning Trust, Risk, and Enterprise Governance IAPP Certified AI Governance Professional (AIGP): https://iapp.org/certify/aigp ISO/IEC 42001 Lead Auditor/Practitioner: https://www.bsigroup.com/en-AU/training-courses/iso-42001-lead-auditor-practitioner-qualification/ submitted by /u/cam-douglas [link] [Kommentare]

Leading with the number that matters most if you're deciding whether to bother: on refrigerator-sorting, success goes from 60.0% in-domain to 13.3% out of distribution (robot pose perturbed ±10cm, previously-unseen objects swapped in). The other soft spot is long horizon, where the policy makes real partial progress and then misses the final precise placement. I'm putting that first because the release reel is slick and I'd rather calibrate before the demos do it for me. With that framing, here's what Robbyant open-sourced: LingBot-VLA 2.0 (Robbyant is an embodied AI company under Ant Group). The design bet is a unified whole-body action space. Everything maps to one 55-dim canonical action vector (arm joints, end-effector, gripper, a 12-dim dexterous hand, waist, head, mobile base), and a single policy is jointly trained across 20 embodiments, from an 8-DoF single arm up to a 32-DoF humanoid, on about 60,000 hours (roughly 50,000 h robot trajectories and 10,000 h egocentric human video). The action head is a MoE expert (loss-free token-level routing, DeepSeek-V3 style) with dual-query distillation from a depth teacher (LingBot-Depth) and a causal video teacher (DINO-Video, released too, which edges out DINOv3 and V-JEPA 2 on 3 of 4 LARYBench metrics). The one practitioner takeaway from their ablations I'd actually act on: the biggest single lever was representation, not architecture. Absolute to relative joint actions moved average success from 33.7 to 55.0 (+21.3), a larger swing than any model change they tested. Videos, matter-of-factly: a multi-embodiment grid (multiple robots, different tasks, at once), an 8-minute continuous autonomous run, transparent glass-vase flower arranging with a live depth window, and a contact-rich zipper pouch. Dual-arm real hardware, watermarked 1x speed and autonomous. It's a release reel, so no claim of zero cherry-picking, weight the failure cases above accordingly. One catch worth surfacing so nobody has to dig for it: GM-100 ("The Great March 100") is their own bimanual benchmark, from Yong-Lu Li's RHOS lab at SJTU with Robbyant and co-authored by the project lead. Not independent. Generalist scores (progress / success): Agilex Cobot Magic 66.2 / 34.4, Galaxea R1 Pro 34.6 / 15.6, ahead of GR00T N1.7, pi-0.5, and their own 1.0. Note success sits far under progress (Galaxea overall 15.6%, some tasks 0%). If you run a bimanual setup, the weights and code are open under the Robbyant org on GitHub and HuggingFace, so pulling them and breaking them on your own robots is the real test here. Independent numbers would be worth more than the self-reported ones. submitted by /u/Good-Razzmatazz-6179 [link] [Kommentare]

Most safety alignment work treats "detect the attack" as a text classification problem — does the prompt contain language the model's safety guardrails should catch. That assumption breaks down for LLM agents with real tool access. Here's a concrete case: take a known, public security vulnerability (a CVE), work out the sequence of tool calls that would exploit it, then have an LLM rewrite that as an ordinary-sounding request. Nothing in the resulting text looks like an attack — because the "attack" isn't in the text, it's in the tool-call sequence the text leads to. A model whose guardrails only trigger on textual cues has nothing to catch. We tested this against LLM agents using Model Context Protocol (MCP) tool access (filesystem IO). No base model (1B–14B parameters) refused more than 35% of these attacks, and SOTA safety-tuning (DPO, SafeDPO) only pushed that to 48%. Training-free methods do better — one gets to roughly 3x the baseline refusal rate with no fine-tuning run at all. Full methodology, training/eval code (four methods), dataset, and papers in the first comment. submitted by /u/mlsandwich [link] [Kommentare]

I'm writing a short series of tutorials on FlashAttention: from theory to efficient CUDA kernels. Part 1 is the theoretical foundation. It walks through a modern algebraic formalism showing that FlashAttention is an associative operation, which lets you treat it as a regular reduction on the GPU and apply all the same scheduling optimizations. Some recent MLSys and CVPR papers lean on this framing, and I find it much more powerful than the original. Overview: Safe softmax, Welford's variance, and FlashAttention are the same secretly-associative operation The twisted monoid (transport of structure), why the max-rescale coupling doesn't break associativity The qk_scale = log2(e)/√D you already see in FA-2 and the Triton kernels, derived from scratch Numerical analysis: overflow bounds, error limits, and why tiling never amplifies error Bird's 3rd Homomorphism Theorem as a test for whether any loop is secretly associative submitted by /u/NoVibeCoding [link] [Kommentare]

Hello I published a paper. Most defenses against fine-tuning poisoning try to detect malicious data or reduce its impact. I explored a different question: What if the model simply could not learn certain malicious updates? The idea is to constrain fine-tuning to a subspace learned from trusted LoRA adapters. Useful adaptation remains possible, but some malicious directions become geometrically unreachable. A concrete example: a company fine-tunes a model on large datasets coming from users, external sources, or generated data. A small amount of poisoned data could introduce a hidden behavior triggered by a specific phrase or pattern. Another example is a local or on-device assistant that keeps adapting to its user. Instead of allowing it to learn any possible behavior from new data, its adaptation could be restricted to variations of behaviors already represented by a trusted pool of adapters. The goal here is not to detect every possible poison or backdoor, but to restrict the space of updates the model is allowed to learn. I tested the approach on 196 public LoRA adapters, including adaptive attacks specifically designed to bypass the defense. The results are strong: attack success drops sharply while useful adaptation is largely preserved on tasks covered by the adapter pool. The paper, code, and experiments are public. Paper: https://arxiv.org/abs/2607.05300 Code: https://github.com/infinition/z-manifold I would be very interested to see people try to break it. submitted by /u/Bright_Warning_8406 [link] [Kommentare]

I have done quite a bit of work, including most of my Master's thesis, with ROS Noetic and Ubuntu 20.04, which worked well. However, I recently got a Raspberry Pi 5 that I want to use for various personal robotics projects, and the Pi 5 doesn't support anything below Ubuntu 23.10. I'm currently debating between installing ROS 2 Jazzy Jalisco with Ubuntu 24.04 or ROS 2 Lyrical Luth with Ubuntu 26.04. Both have similar EoL (2029 vs 2031), so I'm curious if any of you in the robotics community have found major advantages or disadvantages between the two distros. Any reason not to choose Lyrical, since it's newer? Thanks for all your wisdom! submitted by /u/Dying_Of_Board-dom [link] [Kommentare]

Have been building this project for a while now, and would love to get eyes on it. Will appreciate it if you could try it out in actual workflows and give me feedback so I can decide the direction to move in. Currently it has been benchmarked against OPEN3D v0.19 on the same machine, using full-resolution frames from three real datasets: TUM RGB-D, KITTI, and nuScenes-mini. In the table below, higher is better — a ratio above 1 means ThreeCrate is faster than Open3D. Workload How ThreeCrate compares Reading files (raw float parsing) 1.8x–2.2x faster Voxel downsampling (CPU) 1.6x–1.8x faster Voxel downsampling (GPU, wgpu) 1.8x–2.9x faster (vs our own CPU path, not Open3D) Normal estimation 0.57x–1.09x (falls behind on big clouds) Single-scale ICP 0.71x–0.99x (falls behind on big clouds) Would appreciate any contributions and feedback for the repo. Link to the repo: https://github.com/rajgandhi1/threecrate submitted by /u/Practical-Dig-4052 [link] [Kommentare]

Agility Robotics CTO Pras Velagapudi says Digit’s early commercial work is focused on repetitive warehouse and manufacturing tasks like moving totes, unloading AMRs, placing items on shelves, and connecting parts of existing automation systems. He says these are useful “in-between” automation roles where companies do not want to heavily modify infrastructure. The article covers Agility’s partnership with NVIDIA as the first partner for Halos for Robots, NVIDIA’s autonomous safety platform for robots, as well as Agility’s plan to go public through a merger with Churchill Capital Corp. XI, giving the company a $2.5 billion pre-money valuation and $620 million in expected gross proceeds. submitted by /u/Responsible-Grass452 [link] [Kommentare]

This article contains real world patterns you can use to minimize your API/Agent costs (without major refactoring). Instead of just glazing over routing, it provides a real world example of routing with a pretrained classifier and an actual routing table which works! It also provides a recipe for training your own prompt classification model. Use this for cost reductions of up to 60%. Stop tokenmaxxing. Start tokenminning. submitted by /u/Nice-Dragonfly-4823 [link] [Kommentare]

Hello everyone. I wanted to ask if the below book is a good source of machine learning in 2026 or if it is now outdated and if there are better options. Hands-on Machine Learning with. Scikit-Learn, Keras, and TensorFlow : The Third Edition (1200 pages). submitted by /u/Elyartaker [link] [Kommentare]

I am working in a research group in Germany, not that well known but in general good output. I have one colleague who does nothing in his PhD. He does not want to work, or he is not able to do any good research, his level is super bad. Plus He doesn’t even care about that. To wrap it up, he is just here for the money. Since he doesn’t want to work or he can’t really do anything good, instead what he does is “paper fishing”, he searches for people in the group doing some good research, and asks that they put his name on the paper. In this case he has something to cover up for him when the professor asks him about his progress. As long as his name is on the paper, progress is checked and funding is renewed. But he actually does nothing. I know this is very unprofessional and unethical. But people tell me it’s normal in academia. Professors all the time put names of their friends and this is how it works in academia. What are your thoughts of this behaviour? submitted by /u/impressivestatus21 [link] [Kommentare]

ROS Intern – CNDE Lab, IIT Madras Position - Robotics Operating System (ROS) Intern Organization - Centre for Nondestructive Evaluation (CNDE), IIT Madras Location - IIT Madras, Chennai, India Duration 3–6 Months (extendable based on performance) About the Role CNDE Lab, IIT Madras is seeking a motivated and enthusiastic ROS Intern to support the development of robotic systems, autonomous navigation solutions, perception pipelines, and AI-driven applications. The intern will work closely with researchers and engineers on cutting-edge robotics projects involving ROS/ROS2, computer vision, sensor integration, and robotic automation. Key Responsibilities - Develop, test, and maintain ROS/ROS2 packages and nodes. - Integrate sensors such as LiDAR, RGB-D cameras, IMUs, and other robotic peripherals. - Implement robotic navigation, localization, and mapping algorithms. - Develop communication interfaces between hardware and software systems. - Assist in simulation and testing using Gazebo, RViz, Isaac Sim, or similar platforms. - Debug and optimize robotic software for real-world deployment. - Participate in system integration, testing, and documentation activities. - Collaborate with researchers on robotics, AI, and automation projects. Required Skills Programming - Strong knowledge of Python. - Good understanding of C++ programming. - Experience with Linux (Ubuntu) environment. Robotics - Familiarity with ROS/ROS2 concepts, including nodes, topics, services, actions, and launch files. - Understanding of robotic kinematics, localization, navigation, and sensor integration. - Knowledge of Git and version control systems. CAD & Design - Experience with Fusion 360 for basic mechanical design, robot assembly, and component modeling. Preferred Skills - Experience with Computer Vision using OpenCV. - Knowledge of SLAM, Navigation Stack, MoveIt, or robotic manipulation. - Experience with simulation environments such as Gazebo or Isaac Sim. - Familiarity with AI/ML applications in robotics. - Experience working with robotic arms, mobile robots, or autonomous systems. Eligibility - Undergraduate, postgraduate, or recent graduates in: - Robotics - Computer Science - Mechanical Engineering - Electronics Engineering - Artificial Intelligence - Related disciplines What You Will Gain - Hands-on experience with advanced robotics research. - Exposure to real-world robotic systems and deployments. - Mentorship from researchers and engineers at IIT Madras. - Opportunity to contribute to innovative robotics and AI projects. Application Process Interested candidates should send their updated resume along with a brief statement of interest to: Email: msalamdata@gmail.com submitted by /u/Independent_Loss_593 [link] [Kommentare]

Following up on the on-demand comparison from a couple weeks back - pulled spot/ interruptible pricing this time since that's where the real savings conversation actually lives for anyone running checkpointed training or batch jobs. Checked: June 2026. Spot/interruptible tier, single GPU. ~ H100 80GB -Spot/Interruptible RunPod :- $1.80-$2.40/hr , Community spot, can terminate without notice. Vast.ai :- $1.47-$2.00/hr (low end seen as low as $1.03/hr in thinner markets) wide range, host-dependent. AWS (P5, spot) :- technically available, $2.50-$3.10/hr extremely limited, frequently unavailable at any price. ~ A100 80GB - Spot/Interruptible RunPod :- community spot as low as $0.20-0.40/hr (high variance) , reliability drops fast at this end. Vast.ai:- $0.67/hr typical, lower with thinner-reliability hosts, marketplace bidding, varies by host score. AWS (P4d, spot) :- ~$1.00-1.50/hr more consistently available than P5 spot. What stood out: The spot discount vs on-demand is real - 40-60% off on H100, sometimes more on A100 - but the spread between providers on spot is much wider than on-demand. You're not comparing apples to apples, you're comparing apples to "whatever fell off the truck this hour." AWS spot for H100 (P5) is more of a theoretical price point than a practical one right now - availability is thin enough that "checked the price" and "could actually get one" are two different questions. Vast.ai's floor prices look incredible until you check host reliability scores. The $0.67/hr A100 and the $1.50/hr A100 are not the same product even though they're listed the same way. This tier only makes sense if your job checkpoints well - anything customer-facing or latency-sensitive, spot isn't worth the risk regardless of price. Not selling anything, just tracking this for my own training runs and figured others here are doing the same math. Anyone actually running production batch jobs on spot right now? Curious what interruption rates you're actually seeing vs what's advertised. submitted by /u/Shot-Calligrapher166 [link] [Kommentare]

Been chewing on this for a while and I honestly can't tell if it's genuinely open or if I'm just not googling the right terms, so I'll throw it out here. There's a paper by Roynard that put words to something I'd been vaguely bothered by for a while. He calls it a category error, the idea being that pretty much every memory system applies the same persistence model to everything it stores, usually some kind of decay, no matter what the thing actually is. Which is fine for some throwaway message, but it gets weird when the same treatment gets applied to something you've actually validated and would want to stick around and get corrected if it turns out wrong. So you end up storing very different kinds of information under the same rules, even though how much you should trust them or how long they should live is completely different, and I don't think many people are treating that difference as a real property of the memory. That kind of lines up with the thing that keeps biting me once an agent's been running a while, which is that the system doesn't really have any idea how it came to know something in the first place. Something it actually observed, something it inferred a few steps back, and something it just made up all get written down more or less the same way and come back out later at the same confidence, and because none of it is marked as one kind or another, the agent doesn't really have a way to notice when it's wrong. So something stale or hallucinated can resurface later and it looks basically the same as something solid. Timestamps help a little, but they don't tell you what actually backed a belief, and they don't do much when whatever supported it turns out to be wrong and you'd want the agent to walk that back. What bugs me is that this feels like it should already map onto stuff people worked out decades ago. Belief revision (AGM and everything after it) is basically about updating a set of beliefs without it going inconsistent, but I've never really seen it bolted onto an agent's memory at any serious scale. Truth maintenance systems (JTMS/ATMS) are almost exactly this, since the whole point is tracking justifications and retracting conclusions when their premises disappear, and yet they seem basically absent from the agent stacks I've looked at. Calibration work is more about confidence on outputs than about the support behind things you've already stored as they age. And the smarter retrieval stuff, like HippoRAG or the temporal knowledge graph systems (Zep, Graphiti), makes retrieval better but still kind of treats memory as things to find rather than claims that have a support status attached. The closest thing I've found to what I'm describing is Hindsight, which actually splits memory into separate networks and has real conflict-resolution policies, but even there contradictions get surfaced when you query rather than reconciled up front, and corrections don't really propagate to the beliefs that depended on them. A recent paper gets at the human side of it pretty well, The Missing Knowledge Layer in AI. Their framing is that language sort of collapses uncertainty, so when a model states a guess, an inference, and an actual recollection all at the same surface confidence, the person on the other end can't really tell which one they're reacting to, which felt like the same problem I'm describing just viewed from the user's side instead of from inside the memory. What I actually want, at the end of all this, is a small set of primitives for it. Some reasonably clean way to assert something, say what supports it, note that two things contradict, supersede one with another, retract it when it falls apart, with the epistemic status being an actual property of the thing instead of something I bolt on after the fact. Belief revision and TMS feel like the right ancestors, but neither was really built for noisy, high-volume, LLM-generated memory, so a lot of my time just goes into trying to figure out what the right primitives even are. So for anyone who's actually shipped long-horizon agents, I'm curious whether anyone types their memory by epistemic status (observed, inferred, asserted, retracted) instead of just storing flat text plus embeddings, whether anyone's gotten classical belief revision or a TMS to hold up as the actual live memory layer rather than falling over on scale and noise, and whether handling contradictions at write time is even worth the latency or if most people just eat the inconsistency and sort it out at read time. And honestly, if this is already a solved thing and I'm just reinventing some old formalism with extra steps, I'd genuinely appreciate someone pointing me at it so I can go read. But if it's actually underexplored then I'm a little surprised, because it really looks like one of the things that quietly wrecks agents once they've been running longer than a week. submitted by /u/Bright-Fun-1638 [link] [Kommentare]